Note
Go to the end to download the full example code.
Export with dynamic dimensions in {0,1} into ONNX (custom)¶
This duplicates the example Export with dynamic dimensions in {0,1} but for
experimental_experiment.torch_interpreter.to_onnx().
It checks what inputs can be used to export and with which inputs it can work.
Available input sets¶
import itertools
from tqdm import tqdm
import numpy as np
import pandas
import onnxruntime
from onnx_diagnostic import doc
from onnx_diagnostic.helpers import max_diff, string_type, flatten_object
from onnx_diagnostic.helpers.torch_helper import torch_deepcopy
from onnx_diagnostic.helpers.rt_helper import make_feeds
from onnx_diagnostic.torch_models.hghub.model_inputs import get_untrained_model_with_inputs
from onnx_diagnostic.torch_export_patches import (
torch_export_patches,
register_additional_serialization_functions,
)
from experimental_experiment.torch_interpreter import to_onnx, ExportOptions
data = get_untrained_model_with_inputs("arnir0/Tiny-LLM", add_second_input=True)
model, dynamic_shapes = data["model"], data["dynamic_shapes"]
The trained model can be obtained with:
MODEL_NAME = "arnir0/Tiny-LLM"
tokenizer = transformers.AutoTokenizer.from_pretrained(MODEL_NAME)
model = transformers.AutoModelForCausalLM.from_pretrained(MODEL_NAME)
input_sets = {k: v for k, v in data.items() if k.startswith("inputs")}
for k, v in input_sets.items():
print(f"{k:20}: {string_type(v, with_shape=True)}")
inputs : dict(input_ids:T7s2x3,attention_mask:T7s2x33,position_ids:T7s2x3,past_key_values:DynamicCache(key_cache=#1[T1s2x1x30x96], value_cache=#1[T1s2x1x30x96]))
inputs_prompt : dict(input_ids:T7s1x11)
inputs2 : dict(input_ids:T7s3x4,attention_mask:T7s3x35,position_ids:T7s3x4,past_key_values:DynamicCache(key_cache=#1[T1s3x1x31x96], value_cache=#1[T1s3x1x31x96]))
inputs_empty_cache : dict(input_ids:T7s2x3,attention_mask:T7s2x3,position_ids:T7s2x3,past_key_values:DynamicCache(key_cache=#1[T1s2x1x0x96], value_cache=#1[T1s2x1x0x96]))
inputs_batch1 : dict(input_ids:T7s1x3,attention_mask:T7s1x33,position_ids:T7s1x3,past_key_values:DynamicCache(key_cache=#1[T1s1x1x30x96], value_cache=#1[T1s1x1x30x96]))
The dynamic shapes are:
print(f"dynamic_shapes: {string_type(dynamic_shapes)}")
dynamic_shapes: dict(input_ids:{0:DYN(batch),1:DYN(seq_length)},attention_mask:{0:DYN(batch),1:DYN(cache+seq)},position_ids:{0:DYN(batch),1:DYN(seq_length)},past_key_values:#2[{0:DYN(batch),2:DYN(cache_length)},{0:DYN(batch),2:DYN(cache_length)}])
Let’s check they all work and compute the expected values. We use deepcopy because caches are usually modified inplace.
expected = {}
for k, v in input_sets.items():
expected[k] = model(**torch_deepcopy(v))
print(f"{k:20}: {string_type(expected[k], with_shape=True)}")
inputs : CausalLMOutputWithPast(logits:T1s2x3x32000,past_key_values:DynamicCache(key_cache=#1[T1s2x1x33x96], value_cache=#1[T1s2x1x33x96]))
inputs_prompt : CausalLMOutputWithPast(logits:T1s1x11x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x11x96], value_cache=#1[T1s1x1x11x96]))
inputs2 : CausalLMOutputWithPast(logits:T1s3x4x32000,past_key_values:DynamicCache(key_cache=#1[T1s3x1x35x96], value_cache=#1[T1s3x1x35x96]))
inputs_empty_cache : CausalLMOutputWithPast(logits:T1s2x3x32000,past_key_values:DynamicCache(key_cache=#1[T1s2x1x3x96], value_cache=#1[T1s2x1x3x96]))
inputs_batch1 : CausalLMOutputWithPast(logits:T1s1x3x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x33x96], value_cache=#1[T1s1x1x33x96]))
Export with options¶
We try to export with the following options:
cache registration: register cache serialization with
onnx_diagnostic.torch_export_patches.register_additional_serialization_functions()oblivious: an option to remove some the exception raises by the exporter
rt: see
prefer_deferred_runtime_asserts_over_guardsintorch.export.export()cache_patch: patches the model before exporting with
onnx_diagnostic.torch_export_patches.torch_export_patches()
Some function first.
def export_model(
model,
dynamic_shapes,
inputs,
cache=False,
oblivious=False,
rt=False,
cache_patch=False,
strict=False,
):
if cache and not cache_patch:
with register_additional_serialization_functions(patch_transformers=True):
return export_model(
model, dynamic_shapes, inputs, oblivious=oblivious, rt=rt, strict=strict
)
if cache_patch:
with torch_export_patches(
patch_torch=cache_patch in ("all", "torch", True, 1),
patch_transformers=cache_patch in ("all", "transformers", True, 1),
):
return export_model(
model, dynamic_shapes, inputs, oblivious=oblivious, rt=rt, strict=strict
)
return to_onnx(
model,
(),
kwargs=inputs,
dynamic_shapes=dynamic_shapes,
export_options=ExportOptions(
prefer_deferred_runtime_asserts_over_guards=rt,
backed_size_oblivious=oblivious,
strict=strict,
),
)
def try_export_model(
model,
dynamic_shapes,
inputs,
cache=False,
oblivious=False,
rt=False,
cache_patch=False,
strict=False,
):
try:
return export_model(
model,
dynamic_shapes,
inputs,
cache=cache,
oblivious=oblivious,
rt=rt,
cache_patch=cache_patch,
strict=strict,
)
except Exception as e:
return e
def validation(onx, input_sets, expected, catch_exception=True):
sess = onnxruntime.InferenceSession(
onx.SerializeToString(), providers=["CPUExecutionProvider"]
)
for k, v in input_sets.items():
try:
feeds = make_feeds(sess, torch_deepcopy(v), use_numpy=True)
except Exception as e:
if not catch_exception:
raise
yield k, e
continue
try:
got = sess.run(None, feeds)
except Exception as e:
if not catch_exception:
raise
yield k, e
continue
yield k, max_diff(flatten_object(expected[k], drop_keys=True), got)
Verification an example known to be working is.
ep = export_model(
model,
dynamic_shapes,
torch_deepcopy(input_sets["inputs"]),
cache_patch=True,
)
res = list(validation(ep, dict(inputs=input_sets["inputs"]), expected, catch_exception=False))
assert res[0][1]["abs"] < 1e-5, f"Unexpected issue with res={res}"
The main loop¶
results = []
possibilities = [
[0, 1],
[0, "all", "torch", "transformers"],
[0, 1],
[0, 1, "auto", "half"],
[0, 1],
list(input_sets),
]
with tqdm(list(itertools.product(*possibilities))) as pbar:
for cache, cache_patch, strict, oblivious, rt, inputs in pbar:
if cache_patch and not cache:
# patches include caches.
continue
kwargs = dict(
cache=cache, cache_patch=cache_patch, oblivious=oblivious, rt=rt, strict=strict
)
legend = "-".join(
(k if isinstance(v, int) else f"{k}:{v}") for k, v in kwargs.items() if v
)
legend = f"{legend}/{inputs}"
pbar.set_description(f"{legend} EXPORT")
# export
ep = try_export_model(
model, dynamic_shapes, torch_deepcopy(input_sets[inputs]), **kwargs
)
if isinstance(ep, Exception):
obs = {
**kwargs,
"export_with": inputs,
"EXPORT": 0,
"ERR-EXPORT": str(ep).split("\n")[0],
}
results.append(obs)
continue
pbar.set_description(f"{legend} VALIDATE")
common = {**kwargs, "export_with": inputs, "EXPORT": 1}
for inp, res in validation(ep, input_sets, expected):
if isinstance(res, Exception):
obs = {
**common,
"run_with": inp,
"ERR-RUN": str(res).split("\n")[0],
"WORKS": 0,
}
else:
obs = {
**common,
"run_with": inp,
"WORKS": int(~np.isnan(res["abs"]) and res["abs"] < 1e-3),
}
results.append(obs)
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_compiled_create_block_mask = torch.compile(
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return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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for subspec in spec.children_specs:
~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/variables/user_defined.py:1788: FutureWarning: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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return ctor(*args, **kwargs)
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Let’s save the results.
df = pandas.DataFrame(results)
df.to_excel("plot_export_tiny_llm_dim01_onnx.xlsx")
df
no_export = df[df.EXPORT == 0]
no_export.to_excel("plot_export_tiny_llm_dim01_onnx_custom.no_export.xlsx")
no_export
The validation failures.
If you have any error, then look at example Export Tiny-LLM with patches.
doc.plot_legend("Tiny-LLM\nexport with\ndimension in {0,1}", "to_onnx", "tomato")
Total running time of the script: (7 minutes 42.717 seconds)
Related examples
