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Patch for Phi3RotaryEmbedding (dynamic_rope_update) #128

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Merged
sdpython merged 1 commit into from
Jun 6, 2025
Merged

Patch for Phi3RotaryEmbedding (dynamic_rope_update) #128

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38 changes: 38 additions & 0 deletions _unittests/ut_torch_models/test_validate_models.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,38 @@
import unittest
import packaging.version as pv
import torch
from onnx_diagnostic.ext_test_case import (
ExtTestCase,
hide_stdout,
requires_torch,
requires_experimental,
requires_transformers,
)
from onnx_diagnostic.torch_models.test_helper import validate_model


class TestValidateModel(ExtTestCase):
@requires_transformers("4.52")
@requires_torch("2.7.99")
@requires_experimental()
@hide_stdout()
def test_validate_microsoft_phi4_reasoning(self):
# python -m onnx_diagnostic validate -m microsoft/Phi-4-mini-reasoning
# --run -v 1 --export custom -o dump_test --no-quiet --device cuda --patch
summary, data = validate_model(
"microsoft/Phi-4-mini-reasoning",
do_run=True,
verbose=2,
exporter="custom",
do_same=True,
patch=True,
rewrite=True,
stop_if_static=2 if pv.Version(torch.__version__) > pv.Version("2.6.1") else 0,
dump_folder="dump_test_validate_model_custom",
)
self.assertLess(summary["disc_onnx_ort_run_abs"], 1e-5)
self.assertIn("onnx_filename", data)


if __name__ == "__main__":
unittest.main(verbosity=2)
2 changes: 1 addition & 1 deletion onnx_diagnostic/_command_lines_parser.py
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Original file line number Diff line number Diff line change
Expand Up @@ -356,7 +356,7 @@ def get_parser_validate() -> ArgumentParser:
"--runtime",
choices=["onnxruntime", "torch", "ref"],
default="onnxruntime",
help="onnx runtime to use, ",
help="onnx runtime to use, onnxruntime by default",
)
parser.add_argument(
"-o",
Expand Down
88 changes: 88 additions & 0 deletions onnx_diagnostic/torch_export_patches/patches/patch_transformers.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
import inspect
from dataclasses import dataclass
from functools import wraps
from typing import Any, Callable, Dict, List, Optional, Tuple
import torch
import transformers
Expand Down Expand Up @@ -531,3 +532,90 @@ def prepare_inputs_for_generation(
# 8. Remove unexpected `generate` inputs (TODO @joao: fix trainer and examples)
model_inputs.pop("labels", None)
return model_inputs


def patched_dynamic_rope_update(rope_forward):
"""
patch:transformers.modeling_rope_utils.dynamic_rope_update
"""

def longrope_frequency_update(self, position_ids, device):
seq_len = torch.max(position_ids) + 1
if hasattr(self.config, "original_max_position_embeddings"):
original_max_position_embeddings = self.config.original_max_position_embeddings
else:
original_max_position_embeddings = self.config.max_position_embeddings
# At export time, seq_len is unknown.
long_inv_freq, _ = self.rope_init_fn(
self.config, device, seq_len=original_max_position_embeddings + 1
)
original_inv_freq = self.original_inv_freq.to(device)

cond = (seq_len > original_max_position_embeddings).item()
inv_freq = torch.cond(
cond,
(lambda x, y: x.clone()),
(lambda x, y: y.clone()),
[long_inv_freq, original_inv_freq],
)
self.inv_freq = inv_freq
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Collaborator

@titaiwangms titaiwangms Jun 6, 2025

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We don't need to register the buffer?

# if seq_len > original_max_position_embeddings:
# self.inv_freq = self.long_inv_freq
# else:
# self.inv_freq = self.original_inv_freq

def dynamic_frequency_update(self, position_ids, device):
seq_len = torch.max(position_ids) + 1
if seq_len > self.max_seq_len_cached: # growth
inv_freq, self.attention_scaling = self.rope_init_fn(
self.config, device, seq_len=seq_len
)
self.register_buffer("inv_freq", inv_freq, persistent=False)
self.max_seq_len_cached = seq_len

if (
seq_len < self.original_max_seq_len
and self.max_seq_len_cached > self.original_max_seq_len
):
self.original_inv_freq = self.original_inv_freq.to(device)
self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
self.max_seq_len_cached = self.original_max_seq_len

@wraps(rope_forward)
def wrapper(self, x, position_ids):
if "dynamic" in self.rope_type:
dynamic_frequency_update(self, position_ids, device=x.device)
elif self.rope_type == "longrope":
longrope_frequency_update(self, position_ids, device=x.device)
return rope_forward(self, x, position_ids)

return wrapper


class patched_Phi3RotaryEmbedding(torch.nn.Module):
_PATCHES_ = ["forward"]
_PATCHED_CLASS_ = transformers.models.phi3.modeling_phi3.Phi3RotaryEmbedding

@torch.no_grad()
@patched_dynamic_rope_update
def forward(self, x, position_ids):
inv_freq_expanded = (
self.inv_freq[None, :, None]
.float()
.expand(position_ids.shape[0], -1, 1)
.to(x.device)
)
position_ids_expanded = position_ids[:, None, :].float()

device_type = (
x.device.type
if isinstance(x.device.type, str) and x.device.type != "mps"
else "cpu"
)
with torch.autocast(device_type=device_type, enabled=False): # Force float32
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos() * self.attention_scaling
sin = emb.sin() * self.attention_scaling

return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
142 changes: 142 additions & 0 deletions onnx_diagnostic/torch_models/hghub/hub_data_cached_configs.py
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Expand Up @@ -3951,3 +3951,145 @@ def _ccached_facebook_bart_large_cnn():
"vocab_size": 50264,
}
)


def _ccached_microsoft_phi4_reasoning():
"microsoft/Phi-4-mini-reasoning"
return transformers.Phi3Config(
**{
"architectures": ["Phi3ForCausalLM"],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 199999,
"embd_pdrop": 0.0,
"eos_token_id": 199999,
"full_attn_mod": 1,
"hidden_act": "silu",
"hidden_size": 3072,
"initializer_range": 0.02,
"intermediate_size": 8192,
"interpolate_factor": 1,
"lm_head_bias": false,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "phi3",
"num_attention_heads": 24,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"original_max_position_embeddings": 4096,
"pad_token_id": 199999,
"partial_rotary_factor": 0.75,
"resid_pdrop": 0.0,
"rms_norm_eps": 1e-05,
"rope_scaling": {
"long_factor": [
1,
1.118320672,
1.250641126,
1.398617824,
1.564103225,
1.74916897,
1.956131817,
2.187582649,
2.446418898,
2.735880826,
3.059592084,
3.421605075,
3.826451687,
4.279200023,
4.785517845,
5.351743533,
5.984965424,
6.693110555,
7.485043894,
8.370679318,
9.36110372,
10.4687158,
11.70738129,
13.09260651,
14.64173252,
16.37415215,
18.31155283,
20.47818807,
22.90118105,
25.61086418,
28.64115884,
32.03,
32.1,
32.13,
32.23,
32.6,
32.61,
32.64,
32.66,
32.7,
32.71,
32.93,
32.97,
33.28,
33.49,
33.5,
44.16,
47.77,
],
"short_factor": [
1,
1.118320672,
1.250641126,
1.398617824,
1.564103225,
1.74916897,
1.956131817,
2.187582649,
2.446418898,
2.735880826,
3.059592084,
3.421605075,
3.826451687,
4.279200023,
4.785517845,
5.351743533,
5.984965424,
6.693110555,
7.485043894,
8.370679318,
9.36110372,
10.4687158,
11.70738129,
13.09260651,
14.64173252,
16.37415215,
18.31155283,
20.47818807,
22.90118105,
25.61086418,
28.64115884,
32.03,
32.1,
32.13,
32.23,
32.6,
32.61,
32.64,
32.66,
32.7,
32.71,
32.93,
32.97,
33.28,
33.49,
33.5,
44.16,
47.77,
],
"type": "longrope",
},
"rope_theta": 10000.0,
"sliding_window": 262144,
"tie_word_embeddings": true,
"torch_dtype": "bfloat16",
"transformers_version": "4.50.0",
"use_cache": true,
"vocab_size": 200064,
}
)
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