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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([[[[ 2.0641, 1.8414, 2.1144, -2.1858, -1.4553, 2.1089, -3.9414],
[ 2.5480, 0.4189, 0.8150, -1.8488, -1.5655, 1.6026, -0.3057],
[ 3.8920, -1.8116, 0.7533, -2.1390, 0.9199, -0.8702, 2.0602]],
[[ 0.4179, -0.7373, 0.9346, 1.8390, -3.5611, 3.4647, 0.2530],
[-2.0087, 1.1262, -1.9001, 3.3054, -2.2922, 2.4464, 0.9105],
[ 1.6731, 0.8197, -0.3959, 4.5894, -1.9321, -2.5554, -0.1521]],
[[-0.4913, -1.6713, -0.8794, 2.4646, -1.6725, -3.4529, -2.7974],
[ 4.2529, -4.1975, 0.4804, -1.7241, -2.2581, -1.1349, -1.2331],
[ 3.0714, -0.8710, -4.8588, 3.2943, -2.8378, -0.1089, -1.8729]],
[[ 1.0005, 3.5533, -2.5484, -1.0387, -1.3163, 1.9498, -1.7814],
[-1.0856, -2.3275, 3.0730, -0.1047, -3.7895, 1.5157, 0.0685],
[ 0.7635, 1.6033, 1.8651, -0.7784, -1.7362, -1.0176, 0.9097]]],
[[[ 0.4167, -3.1517, 0.4268, -0.6477, -2.1251, -2.2218, -4.0205],
[-0.3417, -0.5707, -1.0588, -0.4271, -0.6683, -1.1274, -3.2400],
[ 6.3509, -0.7531, 1.9027, 1.8726, -4.0646, 0.4263, 2.2371]],
[[ 0.2658, 3.4582, -4.4293, 1.3780, -2.8750, -1.7799, 0.5450],
[ 0.7751, -1.5647, 0.0314, 3.6410, -2.6702, 0.5702, 2.2426],
[-0.6385, -0.9095, 1.5091, -1.2678, -3.0067, -0.2964, 1.6353]],
[[ 1.9896, 2.1162, 2.8666, 0.4280, -2.3102, -1.7958, 0.4322],
[ 3.9568, 0.4840, -2.0115, 0.9279, -2.8587, 0.3561, -0.0295],
[ 0.6160, 0.1291, 0.5133, -2.9567, -2.2645, 0.7163, 3.2457]],
[[ 0.8378, 0.2365, 0.0756, -0.3337, -2.9120, -1.5043, -0.7300],
[-0.2882, 1.1744, -3.2432, -3.2175, -0.3126, -0.3074, -1.8022],
[ 0.7445, -0.6921, -1.7870, 2.7280, -6.2481, 4.1910, 0.4509]]]])
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 0.197 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)