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Export with DynamicCache and dynamic shapes¶
Every LLMs implemented in transformers use cache.
One of the most used is transformers.cache_utils.DynamicCache.
The cache size is dynamic to cope with the growing context.
The example shows a tool which determines the dynamic shapes
for torch.export.export() based on a set of valid inputs.
DynamicCache¶
torch.export.export() serializes caches and any custom class
if these serialization functions are provided with is the case for
transformers.cache_utils.DynamicCache and transformers>=4.50.
The dynamic shapes must be provided following the serialized form.
import pprint
import torch
from onnx_diagnostic import doc
from onnx_diagnostic.ext_test_case import has_transformers
from onnx_diagnostic.helpers import string_type
from onnx_diagnostic.helpers.cache_helper import (
flatten_unflatten_for_dynamic_shapes,
make_dynamic_cache,
)
from onnx_diagnostic.export import ModelInputs
from onnx_diagnostic.torch_export_patches import torch_export_patches
class Model(torch.nn.Module):
def forward(self, cache, z):
return (
z
+ cache.key_cache[0]
+ cache.key_cache[1]
+ cache.value_cache[0]
+ cache.value_cache[1]
)
model = Model()
n_layers = 2
bsize, nheads, slen, dim = 2, 4, 3, 7
cache = make_dynamic_cache(
[
(torch.randn(bsize, nheads, slen, dim), torch.randn(bsize, nheads, slen, dim))
for i in range(n_layers)
]
)
z = torch.randn((1, 1, 1, 7))
model(cache, z) # to check it works.
tensor([[[[-1.8605, -5.4504, -2.3892, 0.4026, 3.2462, 0.3760, -1.4181],
[-2.8890, -2.2520, -0.2076, -2.6661, 1.3611, 2.2601, -1.8810],
[-2.1088, -2.1647, 0.1484, -3.5379, -1.5968, 4.3554, 5.6420]],
[[ 1.7319, 0.4264, 0.3296, 2.2837, 1.8617, -0.2268, 1.7536],
[-1.1231, 0.1229, -2.4479, 1.7075, 4.6380, -0.5404, -0.0735],
[-1.2506, -4.2822, -0.5056, 2.6407, 2.1057, 0.2186, 0.9482]],
[[-3.1648, -0.1417, 3.6555, -2.5482, 1.3253, 0.4597, 0.5646],
[-2.7349, -0.0772, -4.9611, -4.3342, 2.2564, 1.8305, 2.5631],
[ 0.1012, -1.3093, -1.9298, -2.2986, -0.7864, 4.8788, 2.9099]],
[[ 1.0965, -2.0789, -2.1207, 0.3917, 4.0795, 3.1778, -2.2771],
[-4.8578, -6.9271, -6.5074, 2.1369, -0.8894, -0.1846, -0.8819],
[-0.0309, -3.7392, 2.5612, -2.6582, -2.2330, 1.7601, -1.4208]]],
[[[-0.3899, -2.6561, -5.8506, -3.9568, -0.4239, 1.0534, 6.2202],
[-2.4387, -3.9891, -1.8072, -0.6490, -1.4709, -1.2247, 0.6174],
[ 0.1999, -0.0763, -0.3712, -2.6219, -1.8042, 4.5284, 2.4134]],
[[-1.7219, -3.4888, -1.7979, -5.6367, 1.3094, 0.8254, 0.6792],
[ 0.4737, 4.8481, -0.5698, -2.6678, -0.3776, 0.8704, 2.4824],
[-2.5786, -1.0099, 2.0233, 3.8570, 4.4071, 0.5649, 2.2431]],
[[ 2.2775, -2.1189, -1.5625, 2.7245, 1.0061, 0.2306, -0.2830],
[-1.0669, -1.2480, -3.3949, -0.2680, -1.2898, 0.5371, 1.0551],
[ 2.7892, -3.7749, 0.1293, -1.5835, -1.5676, -1.1500, -0.6408]],
[[ 2.3852, 1.0136, 1.2422, -0.0792, 2.0969, 0.6596, -2.9321],
[-1.7794, 1.1378, -0.9360, -3.3211, -0.4293, 1.4050, 2.0528],
[-2.3679, -0.9945, 1.0518, -4.2388, -1.1410, 2.0224, -2.4178]]]])
The cache looks like this:
print(string_type(cache, with_shape=True))
DynamicCache(key_cache=#2[T1s2x4x3x7,T1s2x4x3x7], value_cache=#2[T1s2x4x3x7,T1s2x4x3x7])
cache2 = make_dynamic_cache(
[
(
torch.randn(bsize + 1, nheads, slen + 1, dim + 1),
torch.randn(bsize + 1, nheads, slen + 1, dim + 1),
)
for i in range(n_layers)
]
)
inputs = [
(cache, z),
(cache2, torch.randn((1, 1, 1, 8))),
]
And the second set of inputs looks like:
print(string_type(inputs[1], with_shape=True))
(DynamicCache(key_cache=#2[T1s3x4x4x8,T1s3x4x4x8], value_cache=#2[T1s3x4x4x8,T1s3x4x4x8]),T1s1x1x1x8)
Guess the dynamic shapes¶
The following tool can be used to guess the dynamic shapes
the way torch.export.export() expects them.
mi = ModelInputs(Model(), inputs)
ds = mi.guess_dynamic_shapes()
pprint.pprint(ds)
(([[{0: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True),
2: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True),
3: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True)},
{0: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True),
2: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True),
3: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True)}],
[{0: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True),
2: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True),
3: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True)},
{0: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True),
2: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True),
3: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True)}]],
{3: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True)}),
{})
And finally the export.
The export is simple if transformers>=4.50, otherwise,
transformers needs to be patched.
onnx_diagnostic.torch_export_patches.torch_export_patches()
registers functions to serialize DynamicCache. This one is modified to make
the shape inference implemented in torch happy.
if has_transformers("4.50"):
ep = torch.export.export(model, inputs[0], dynamic_shapes=ds[0], strict=False)
else:
with torch_export_patches(patch_transformers=True) as modificator:
ep = torch.export.export(
model, modificator(inputs[0]), dynamic_shapes=ds[0], strict=False
)
print(ep)
# Do we need to guess?
# ++++++++++++++++++++
#
# Function :func:`onnx_diagnostic.helpers.string_type` is using
# the serialization functions to print out the DynamicCache the was
# :func:`torch.export.export` expects them.
print(string_type(cache, with_shape=True))
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, cache_key_cache_0: "f32[s26, 4, s28, s1]", cache_key_cache_1: "f32[s26, 4, s28, s1]", cache_value_cache_0: "f32[s26, 4, s28, s1]", cache_value_cache_1: "f32[s26, 4, s28, s1]", z: "f32[1, 1, 1, s1]"):
# File: /home/xadupre/github/onnx-diagnostic/_doc/examples/plot_export_with_dynamic_cache.py:39 in forward, code: z
add: "f32[s26, 4, s28, s1]" = torch.ops.aten.add.Tensor(z, cache_key_cache_0); z = cache_key_cache_0 = None
add_1: "f32[s26, 4, s28, s1]" = torch.ops.aten.add.Tensor(add, cache_key_cache_1); add = cache_key_cache_1 = None
add_2: "f32[s26, 4, s28, s1]" = torch.ops.aten.add.Tensor(add_1, cache_value_cache_0); add_1 = cache_value_cache_0 = None
add_3: "f32[s26, 4, s28, s1]" = torch.ops.aten.add.Tensor(add_2, cache_value_cache_1); add_2 = cache_value_cache_1 = None
return (add_3,)
Graph signature:
# inputs
cache_key_cache_0: USER_INPUT
cache_key_cache_1: USER_INPUT
cache_value_cache_0: USER_INPUT
cache_value_cache_1: USER_INPUT
z: USER_INPUT
# outputs
add_3: USER_OUTPUT
Range constraints: {s26: VR[2, int_oo], s28: VR[2, int_oo], s1: VR[2, int_oo]}
DynamicCache(key_cache=#2[T1s2x4x3x7,T1s2x4x3x7], value_cache=#2[T1s2x4x3x7,T1s2x4x3x7])
You can also use function
onnx_diagnostic.helpers.cache_helper.flatten_unflatten_for_dynamic_shapes()
to show a DynamicCache restructured the way torch.export.export() expects
it to be without the custom class.
print(string_type(flatten_unflatten_for_dynamic_shapes(cache), with_shape=True))
#2[#2[T1s2x4x3x7,T1s2x4x3x7],#2[T1s2x4x3x7,T1s2x4x3x7]]
This code works for any custom class if it was registered
with torch.utils._pytree.register_pytree_node().
doc.plot_legend("dynamic shapes\nfor DynamicCache", "torch.export.export", "tomato")
Total running time of the script: (0 minutes 0.310 seconds)
Related examples
Steel method forward to guess the dynamic shapes (with Tiny-LLM)
Steel method forward to guess the dynamic shapes (with Tiny-LLM)