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Setup prompt-processsing and token-generation testing in text generation tasks #229

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65f1ca0
draft improve llm random inputs
titaiwangms Sep 19, 2025
94a9b10
Merge branch 'main' into titaiwang/fix_modelbuilder_discrepancy
titaiwangms Sep 19, 2025
5c55755
resolve conflicts
titaiwangms Sep 22, 2025
aa8b0f8
revert unintentional changes
titaiwangms Sep 22, 2025
cd1a19f
add comments
titaiwangms Sep 22, 2025
f413ea7
draft-patched_sdpa
titaiwangms Sep 23, 2025
8bd2fa1
set is_causal
titaiwangms Sep 23, 2025
d65493b
support prompt processing and token generation
titaiwangms Sep 23, 2025
df3ad9b
fix mypy
titaiwangms Sep 23, 2025
d817f19
fix draft
titaiwangms Sep 24, 2025
f15360e
fix static cahce
titaiwangms Sep 24, 2025
6fea147
fix torch.export 0/1 specializing
titaiwangms Sep 25, 2025
9568e18
add a test
titaiwangms Sep 26, 2025
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30 changes: 0 additions & 30 deletions onnx_diagnostic/helpers/helper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1061,36 +1061,6 @@ def max_diff(
print(f"[max_diff] to_tuple2: {string_type(expected)} ? {string_type(got)}")
return max_diff(expected, got.to_tuple(), debug_info=_debug("to_tuple2"), **_dkws)

if isinstance(got, (list, tuple)):
if len(got) != 1:
if verbose >= 6:
print(
f"[max_diff] list,tuple,2: {string_type(expected)} "
f"? {string_type(got)}"
)
if verbose > 2:
import torch

print(
f"[max_diff] (a) inf because len(expected)={len(expected)}!=1, "
f"len(got)={len(got)}, level={level}, _index={_index}"
)
for i, (a, b) in enumerate(zip(expected, got)):
if isinstance(a, torch.Tensor) and isinstance(b, torch.Tensor):
print(
f" i={i} expected {a.dtype}:{a.shape}, "
f"has {b.dtype}:{b.shape}, _index={_index}"
)
else:
print(
f" i={i} a is {type(a)}, "
f"b is {type(b)}, _index={_index}"
)
return dict(abs=np.inf, rel=np.inf, sum=np.inf, n=np.inf, dnan=np.inf)
if verbose >= 6:
print(f"[max_diff] list,tuple,1: {string_type(expected)} ? {string_type(got)}")
return max_diff(expected, got[0], debug_info=_debug("lt1"), **_dkws)

if isinstance(expected, (tuple, list)):
if verbose >= 6:
print(f"[max_diff] list,tuple,0: {string_type(expected)} ? {string_type(got)}")
Expand Down
126 changes: 79 additions & 47 deletions onnx_diagnostic/tasks/text_generation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -59,8 +59,8 @@ def get_inputs(
dummy_max_token_id: int,
num_hidden_layers: int,
batch_size: int = 2,
sequence_length: int = 30,
sequence_length2: int = 3,
past_sequence_length: int = 30,
sequence_length: int = 3,
dynamic_rope: bool = False,
num_key_value_heads: Optional[int] = None,
head_dim: Optional[int] = None,
Expand All @@ -76,17 +76,18 @@ def get_inputs(
:param head_dim: last dimension of the cache
:param dummy_max_token_id: dummy max token id
:param batch_size: batch size
:param sequence_length: sequence length
:param sequence_length2: new sequence length
:param past_sequence_length: past sequence length
:param sequence_length: new sequence length
:param dynamic_rope: use dynamic rope (see :class:`transformers.LlamaConfig`)
:param cls_cache: cache class, by default it is
:class:`transformers.cache_utils.DynamicCache`
:return: dictionary
"""
batch = "batch"
seq_length = "seq_length" # torch.export.Dim("seq_length", min=1, max=4096)
cache_length = "cache_length" # torch.export.Dim("cache_length", min=1, max=4096)
seq_length = "seq_length"
past_seq_length = "past_seq_length"

# TODO(team): Is this code block still necessary?
if config is not None and config.__class__.__name__ == "FalconMambaConfig":
try:
from transformers.models.mamba.modeling_mamba import MambaCache
Expand All @@ -98,23 +99,23 @@ def get_inputs(
MambaCache,
), f"Unexpected value for cls_cache={cls_cache} and config={config}"
seq_length_multiple = 8
sequence_length = (
(sequence_length + seq_length_multiple)
past_sequence_length = (
(past_sequence_length + seq_length_multiple)
// seq_length_multiple
* seq_length_multiple
)
# sequence_inc = seq_length_multiple
sequence_length2 = seq_length_multiple
sequence_length = seq_length_multiple

shapes = {
"input_ids": {0: batch, 1: "sequence_length"},
"attention_mask": {
0: batch,
1: "cache+seq", # cache_length + seq_length
1: "cache+seq", # past_seq_length + seq_length
},
"cache_position": {
0: batch,
1: "cache+seq", # cache_length + seq_length
1: "cache+seq", # past_seq_length + seq_length
},
"cache_params": [
[{0: batch} for _ in range(num_hidden_layers)],
Expand All @@ -123,9 +124,9 @@ def get_inputs(
}
inputs = dict(
input_ids=torch.randint(
0, dummy_max_token_id, (batch_size, sequence_length + sequence_length2)
0, dummy_max_token_id, (batch_size, past_sequence_length + sequence_length)
).to(torch.int64),
attention_mask=torch.ones((batch_size, sequence_length + sequence_length2)).to(
attention_mask=torch.ones((batch_size, past_sequence_length + sequence_length)).to(
torch.int64
),
cache_position=torch.arange(0, kwargs["conv_kernel"]).to(torch.int64),
Expand Down Expand Up @@ -167,46 +168,54 @@ def get_inputs(
make_cache = make_dynamic_cache if cache_name is None else make_caches[cache_name]
is_static = cache_name == "StaticCache"

# TODO(team): Is this code block still necessary?
if is_static:
# static
shapes = {
"input_ids": {0: batch, 1: seq_length},
"attention_mask": {0: batch, 2: "seq"},
"cache_position": {0: "seq"},
"attention_mask": {0: batch, 2: "sequence_length+past_sequence_length"},
"cache_position": {0: "sequence_length+past_sequence_length"},
"past_key_values": [
# [{0: batch, 2: cache_length} for _ in range(num_hidden_layers)],
# [{0: batch, 2: cache_length} for _ in range(num_hidden_layers)],
# [{0: batch, 2: past_seq_length} for _ in range(num_hidden_layers)],
# [{0: batch, 2: past_seq_length} for _ in range(num_hidden_layers)],
[{0: batch} for _ in range(num_hidden_layers)],
[{0: batch} for _ in range(num_hidden_layers)],
],
}
inputs = dict(
input_ids=torch.randint(
0, dummy_max_token_id, (batch_size, sequence_length2)
0, dummy_max_token_id, (batch_size, sequence_length)
).to(torch.int64),
attention_mask=torch.ones(
(batch_size, num_key_value_heads, sequence_length2, head_dim)
(
batch_size,
num_key_value_heads,
past_sequence_length + sequence_length,
head_dim,
)
).to(torch.bool),
cache_position=torch.arange(sequence_length2).to(torch.int64),
cache_position=torch.arange(past_sequence_length + sequence_length).to(
torch.int64
),
past_key_values=make_static_cache(
[
(
torch.randn(
batch_size,
num_key_value_heads,
sequence_length + sequence_length2,
past_sequence_length + sequence_length,
head_dim,
),
torch.randn(
batch_size,
num_key_value_heads,
sequence_length + sequence_length2,
sequence_length + past_sequence_length,
head_dim,
),
)
for i in range(num_hidden_layers)
],
max_cache_len=max(sequence_length + sequence_length2, head_dim),
max_cache_len=max(sequence_length + past_sequence_length, head_dim),
),
)
else:
Expand All @@ -215,53 +224,57 @@ def get_inputs(
"input_ids": {0: batch, 1: seq_length},
"attention_mask": {
0: batch,
1: "cache+seq", # cache_length + seq_length
1: "cache+seq", # past_seq_length + seq_length
},
"position_ids": {
0: batch,
1: "cache+seq", # cache_length + seq_length
1: seq_length,
},
"past_key_values": [
[{0: batch, 2: cache_length} for _ in range(num_hidden_layers)],
[{0: batch, 2: cache_length} for _ in range(num_hidden_layers)],
],
}

inputs = dict(
input_ids=torch.randint(
0, dummy_max_token_id, (batch_size, sequence_length2)
0, dummy_max_token_id, (batch_size, sequence_length)
).to(torch.int64),
attention_mask=torch.ones((batch_size, sequence_length + sequence_length2)).to(
torch.int64
),
position_ids=torch.arange(sequence_length, sequence_length + sequence_length2)
attention_mask=torch.ones(
(batch_size, sequence_length + past_sequence_length)
).to(torch.int64),
position_ids=torch.arange(
past_sequence_length, sequence_length + past_sequence_length
)
.to(torch.int64)
.expand((batch_size, -1)),
past_key_values=make_cache( # type: ignore[operator]
)
# Caches are involved
if past_sequence_length > 0:
inputs["past_key_values"] = make_cache(
[
(
torch.randn(
batch_size, num_key_value_heads, sequence_length, head_dim
batch_size, num_key_value_heads, past_sequence_length, head_dim
),
torch.randn(
batch_size, num_key_value_heads, sequence_length, head_dim
batch_size, num_key_value_heads, past_sequence_length, head_dim
),
)
for i in range(num_hidden_layers)
]
),
)
)
shapes["past_key_values"] = [
[{0: batch, 2: past_seq_length} for _ in range(num_hidden_layers)],
[{0: batch, 2: past_seq_length} for _ in range(num_hidden_layers)],
]
res = dict(inputs=inputs, dynamic_shapes=shapes)
if add_second_input:
# prompt processing (prefill) testing
res["inputs2"] = get_inputs(
model=model,
config=config,
dummy_max_token_id=dummy_max_token_id,
num_hidden_layers=num_hidden_layers,
batch_size=(batch_size + 1) if add_second_input > 0 else 1,
sequence_length=sequence_length + 1,
sequence_length2=sequence_length2
+ (add_second_input if add_second_input > 0 else -add_second_input),
batch_size=batch_size,
past_sequence_length=0,
sequence_length=32,
dynamic_rope=dynamic_rope,
num_key_value_heads=num_key_value_heads,
head_dim=head_dim,
Expand All @@ -276,6 +289,23 @@ def random_input_kwargs(config: Any) -> Tuple[Dict[str, Any], Callable]:
"""
Inputs kwargs.

NOTE: We test two scenarios:
1. prompt processing (aka prefill):
input_ids=(batch_size, prompt_length)
attn_mask=(batch_size, 0+prompt_length) = (batch_size, prompt_length)
pos_ids=(batch_size, prompt_length)
past_key_values=(batch_size, num_key_value_heads, 0, head_dim)
present_key_values=(batch_size, num_key_value_heads, 0+prompt_length, head_dim)
2. token generation (aka decode).
input_ids=(batch_size, 1)
attn_mask=(batch_size, past_sequence_length+1)
pos_ids=(batch_size, 1)
past_key_values=(batch_size, num_key_value_heads, past_sequence_length,
head_dim)
present_key_values=(batch_size, num_key_value_heads,
past_sequence_length+1, head_dim)


If the configuration is None, the function selects typical dimensions.
"""
if config is not None:
Expand All @@ -290,8 +320,8 @@ def random_input_kwargs(config: Any) -> Tuple[Dict[str, Any], Callable]:
check_hasattr(config, "conv_kernel", "state_size", "intermediate_size") # 4 and 8
kwargs = dict(
batch_size=2,
sequence_length=30,
sequence_length2=3,
past_sequence_length=30,
sequence_length=3,
dummy_max_token_id=31999 if config is None else (config.vocab_size - 1),
num_hidden_layers=4 if config is None else config.num_hidden_layers,
intermediate_size=256 if config is None else config.intermediate_size,
Expand All @@ -300,10 +330,12 @@ def random_input_kwargs(config: Any) -> Tuple[Dict[str, Any], Callable]:
conv_kernel=8 if config is None else getattr(config, "conv_kernel", None),
)
else:
# Token generation (decode) testing
# NOTE: We have to export model in decode mode to preserve the cache
kwargs = dict(
batch_size=2,
sequence_length=30,
sequence_length2=3,
past_sequence_length=32,
sequence_length=1,
head_dim=(
16
if config is None
Expand Down
35 changes: 35 additions & 0 deletions onnx_diagnostic/torch_export_patches/onnx_export_errors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -415,6 +415,11 @@ def torch_export_patches(
except ImportError:
masking_utils = None

try:
import transformers.modeling_utils as modeling_utils
except ImportError:
modeling_utils = None

if verbose:
import transformers

Expand Down Expand Up @@ -509,6 +514,23 @@ def torch_export_patches(
patch_transformers_list.patched_sdpa_mask_recent_torch
)

if (
modeling_utils
and patch_transformers_list.patch_modeling_utils
and "sdpa" in modeling_utils.ALL_ATTENTION_FUNCTIONS
):
if verbose:
print(
"[torch_export_patches] patches "
"transformers.modeling_utils.sdpa_attention_forward"
)
f_transformers_sdpa_attention_forward = modeling_utils.ALL_ATTENTION_FUNCTIONS[
"sdpa"
]
modeling_utils.ALL_ATTENTION_FUNCTIONS["sdpa"] = (
patch_transformers_list.patched_sdpa_attention_forward
)

if custom_patches:
if verbose:
print("[torch_export_patches] applies custom patches")
Expand Down Expand Up @@ -688,6 +710,19 @@ def torch_export_patches(
"transformers.masking_utils.sdpa_mask "
"in ALL_MASK_ATTENTION_FUNCTIONS"
)
if (
modeling_utils
and patch_transformers_list.patch_modeling_utils
and "sdpa" in modeling_utils.ALL_ATTENTION_FUNCTIONS
):
modeling_utils.ALL_ATTENTION_FUNCTIONS["sdpa"] = (
f_transformers_sdpa_attention_forward
)
if verbose:
print(
"[torch_export_patches] restored "
"transformers.modeling_utils.sdpa_attention_forward"
)

########
# caches
Expand Down
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