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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 bypass_export_some_errors
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([[[[-0.6074, 0.4564, -2.5022, -3.0228, -0.7389, 0.8869, -0.2922],
[ 2.8649, 1.5606, -1.6871, -2.7368, 0.3228, -3.1701, 1.2549],
[ 2.5762, 0.9670, -4.0031, -3.1900, -0.0721, 2.7567, -0.9342]],
[[ 1.2530, -1.2665, -4.8364, 0.4504, -4.6639, 2.3003, -3.8798],
[ 1.3169, 0.8476, -1.9826, 0.7616, 1.8153, 0.1259, 1.2098],
[ 0.8704, -1.3857, -3.4236, -1.5669, 0.2834, 0.8064, 0.7629]],
[[ 0.5600, -1.2178, -2.3031, -1.4461, 0.4515, -4.2972, 2.2226],
[ 1.7402, -0.1522, -2.2510, 0.0965, -0.0474, -0.6318, -0.0480],
[-0.9039, 1.2713, -1.1893, -0.4839, -0.1363, 3.8304, 2.6745]],
[[-0.0085, 3.1588, -2.5299, -2.3592, -0.1064, 2.1106, 1.6984],
[ 2.3459, -3.0749, -0.2180, -0.9902, -1.8782, 0.0546, 3.0867],
[ 2.4693, -0.3514, -2.8640, -1.7124, 1.3470, 0.0556, 4.4353]]],
[[[ 0.7560, 1.5433, -1.9611, -2.8960, 0.3127, 0.8502, 3.7807],
[ 0.4000, 1.4497, -3.1303, -5.8418, 0.3858, 2.6408, -1.0751],
[ 3.0079, -0.3757, -0.3050, -2.1079, 0.8492, -0.6222, 1.2014]],
[[ 2.1729, 3.5238, -2.8492, -4.3999, 0.2071, 3.3403, 2.5480],
[ 0.8098, 0.0974, -1.1923, -0.4168, -0.8248, -0.6386, 3.3686],
[ 0.4880, -0.7822, 3.6328, -0.9024, -2.1802, -0.7438, 3.2056]],
[[ 3.9566, -2.9754, 1.6396, -1.5234, -2.6085, 3.2171, 0.6216],
[ 5.3821, 1.7626, -0.1185, -3.9268, 1.5964, -1.6121, -1.1457],
[ 0.4399, -2.2553, 2.6655, -6.1374, -0.6601, -3.7089, 1.9773]],
[[-1.5957, -5.7879, 2.7818, -0.3937, -1.1999, -1.6934, 2.2525],
[ 0.8051, 1.0250, 1.5346, -1.4982, -2.2119, 0.2248, -1.8336],
[-0.1183, -2.4124, 0.3726, 0.3715, 0.4399, 1.1047, 5.3878]]]])
The cache looks like this:
print(string_type(cache, with_shape=True))
DynamicCache[serialized](#2[#2[T1s2x4x3x7,T1s2x4x3x7],#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[serialized](#2[#2[T1s3x4x4x8,T1s3x4x4x8],#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.bypass_export_some_errors()
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 bypass_export_some_errors(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[serialized](#2[#2[T1s2x4x3x7,T1s2x4x3x7],#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 0.322 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)