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Export with DynamicCache and guessed 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([[[[ 1.6211e+00, -4.4963e-01, 3.7138e-01, -1.1672e+00, 3.0473e+00,
-3.8904e+00, -3.7442e+00],
[ 5.1387e-01, 5.5463e-01, 3.2164e+00, -2.0300e+00, -4.9825e-01,
-2.8044e+00, -3.4624e+00],
[-3.1017e+00, -3.0667e+00, -8.5357e-01, -2.0056e+00, 1.5722e+00,
1.9887e+00, -2.8111e+00]],
[[-1.5656e+00, 2.6021e+00, -6.9981e-03, -6.1359e-01, -1.8002e+00,
-1.8340e-02, 4.5578e-01],
[ 3.2180e-01, 3.9261e-02, 2.3844e-01, -6.3152e-01, -2.4216e+00,
-2.3339e+00, 1.7688e+00],
[-5.2917e-01, -2.4031e+00, 7.8195e-01, -1.4755e+00, 2.1120e+00,
3.2735e-01, -1.2401e+00]],
[[-6.8606e-01, -1.3246e+00, -2.0586e+00, 4.8146e-02, 1.4194e+00,
1.2322e+00, -7.9979e-01],
[ 1.6524e+00, -9.8557e-01, -7.8124e-01, -6.3959e-01, 7.0277e-02,
6.7879e-02, -1.3848e+00],
[-2.8186e+00, -2.7293e+00, -3.2263e-01, -2.3429e-01, -1.7123e+00,
-5.7148e-01, -9.9731e-02]],
[[-4.7298e-01, 1.0095e+00, 6.8650e-01, 1.1478e-01, -2.0815e+00,
-2.7628e+00, -1.5509e+00],
[ 2.3727e+00, -4.8947e+00, 5.2629e-01, -1.2931e+00, 1.0776e+00,
-2.6690e+00, -1.0616e+00],
[-3.7991e-01, -2.4463e-01, -2.0551e+00, 2.7257e-01, 1.5132e+00,
-1.2175e+00, -3.2151e-01]]],
[[[ 3.7307e+00, -2.8901e-01, -1.8701e+00, 1.7091e+00, 2.9891e-01,
-3.6944e+00, -8.1635e-01],
[-2.5388e+00, 1.0909e+00, -6.7015e-01, -3.6232e+00, -2.1953e-01,
-3.1885e+00, 2.2721e+00],
[ 1.8095e-02, -2.6668e+00, -2.6200e+00, 6.7534e-01, 2.6317e+00,
-1.5612e+00, -9.8545e-01]],
[[ 1.6619e+00, -7.3568e-02, 1.8104e+00, -2.6276e+00, -1.5602e+00,
1.8792e+00, -2.8923e+00],
[ 3.1849e+00, 1.1824e+00, -1.1947e+00, 3.7644e-01, -2.5369e+00,
4.8330e-03, 9.9245e-01],
[ 1.0146e+00, 3.8534e-01, 4.0157e-01, 7.2531e-01, 1.9562e-01,
7.5785e-01, 2.7832e-01]],
[[-2.3417e+00, -2.9269e+00, 2.3163e+00, -8.0094e-02, 3.8486e+00,
-4.0846e-01, -2.5925e+00],
[ 9.1589e-01, -8.1134e-01, 3.5342e+00, -3.5822e+00, 2.8905e+00,
1.7780e+00, -1.5687e+00],
[ 3.0770e+00, -9.1214e-01, 4.6789e-01, -6.9587e-01, -5.3018e-01,
-1.2687e-01, -1.2247e+00]],
[[ 5.3347e+00, 1.7573e+00, 7.6813e-01, -1.4423e+00, 3.8983e+00,
7.3556e-02, 1.2060e+00],
[-1.6905e+00, 4.6349e-01, -2.4926e+00, 8.4244e-01, -2.1735e+00,
-3.1046e-01, -1.7711e+00],
[ 1.6472e+00, 1.2324e+00, -8.4641e-01, -7.6808e-01, -3.5611e+00,
-2.4918e+00, 3.9598e-01]]]])
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)
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]}
Use string instead of DYNAMIC¶
ONNX exporter considers strings instead of DYNAMIC or AUTO to give names to every dimension.
dss = mi.guess_dynamic_shapes(auto="dim")
pprint.pprint(dss)
(([[{0: 'dim_0I_0o_0l0', 2: 'dim_0I_0o_0l2', 3: 'dim_0I_0o_0l3'},
{0: 'dim_0I_0o_1l0', 2: 'dim_0I_0o_1l2', 3: 'dim_0I_0o_1l3'}],
[{0: 'dim_0I_1o_0l0', 2: 'dim_0I_1o_0l2', 3: 'dim_0I_1o_0l3'},
{0: 'dim_0I_1o_1l0', 2: 'dim_0I_1o_1l2', 3: 'dim_0I_1o_1l3'}]],
{3: 'dim_1I3'}),
{})
Do we need to guess?¶
Function onnx_diagnostic.helpers.string_type() is using
the serialization functions to print out the DynamicCache the was
torch.export.export() expects them.
print(string_type(cache, with_shape=True))
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 11.039 seconds)
Related examples
Steel method forward to guess inputs and dynamic shapes (with Tiny-LLM)