Note
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Export Phi-3.5-mini-instruct piece by piece¶
torch.export.export() often breaks on big models because there
are control flows or instructions breaking the propagation of
dynamic shapes (see …). The function usually gives an indication where
the model implementation can be fixed but in case, that is not possible,
we can try to export the model piece by piece: every module
is converted separately from its submodule. A model can be exported even
if one of its submodules cannot.
Model¶
import pprint
from typing import Any, Dict
import torch
import torch._export.tools
import transformers
from onnx_diagnostic.helpers.cache_helper import make_dynamic_cache
from experimental_experiment.helpers import string_type
from experimental_experiment.torch_interpreter.piece_by_piece import (
trace_execution_piece_by_piece,
)
def get_phi35_untrained(batch_size: int = 2, **kwargs) -> Dict[str, Any]:
"""
Gets a non initialized model with two sets of inputs and different shapes.
:param batch_size: batch size
:param kwargs: to overwrite the configuration, example ``num_hidden_layers=1``
:return: dictionary
See `Phi-3.5-mini-instruct/config.json
<https://huggingface.co/microsoft/Phi-3.5-mini-instruct/blob/main/config.json>`_.
"""
config = {
"_name_or_path": "Phi-3.5-mini-instruct",
"architectures": ["Phi3ForCausalLM"],
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_phi3.Phi3Config",
"AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM",
},
"bos_token_id": 1,
"embd_pdrop": 0.0,
"eos_token_id": 32000,
"hidden_act": "silu",
"hidden_size": 3072,
"initializer_range": 0.02,
"intermediate_size": 8192,
"max_position_embeddings": 131072,
"model_type": "phi3",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 32,
"original_max_position_embeddings": 4096,
"pad_token_id": 32000,
"resid_pdrop": 0.0,
"rms_norm_eps": 1e-05,
"rope_scaling": {
"long_factor": [
1.0800000429153442,
1.1100000143051147,
1.1399999856948853,
1.340000033378601,
1.5899999141693115,
1.600000023841858,
1.6200000047683716,
2.620000123977661,
3.2300000190734863,
3.2300000190734863,
4.789999961853027,
7.400000095367432,
7.700000286102295,
9.09000015258789,
12.199999809265137,
17.670000076293945,
24.46000099182129,
28.57000160217285,
30.420001983642578,
30.840002059936523,
32.590003967285156,
32.93000411987305,
42.320003509521484,
44.96000289916992,
50.340003967285156,
50.45000457763672,
57.55000305175781,
57.93000411987305,
58.21000289916992,
60.1400032043457,
62.61000442504883,
62.62000274658203,
62.71000289916992,
63.1400032043457,
63.1400032043457,
63.77000427246094,
63.93000411987305,
63.96000289916992,
63.970001220703125,
64.02999877929688,
64.06999969482422,
64.08000183105469,
64.12000274658203,
64.41000366210938,
64.4800033569336,
64.51000213623047,
64.52999877929688,
64.83999633789062,
],
"short_factor": [
1.0,
1.0199999809265137,
1.0299999713897705,
1.0299999713897705,
1.0499999523162842,
1.0499999523162842,
1.0499999523162842,
1.0499999523162842,
1.0499999523162842,
1.0699999332427979,
1.0999999046325684,
1.1099998950958252,
1.1599998474121094,
1.1599998474121094,
1.1699998378753662,
1.2899998426437378,
1.339999794960022,
1.679999828338623,
1.7899998426437378,
1.8199998140335083,
1.8499997854232788,
1.8799997568130493,
1.9099997282028198,
1.9399996995925903,
1.9899996519088745,
2.0199997425079346,
2.0199997425079346,
2.0199997425079346,
2.0199997425079346,
2.0199997425079346,
2.0199997425079346,
2.0299997329711914,
2.0299997329711914,
2.0299997329711914,
2.0299997329711914,
2.0299997329711914,
2.0299997329711914,
2.0299997329711914,
2.0299997329711914,
2.0299997329711914,
2.0799996852874756,
2.0899996757507324,
2.189999580383301,
2.2199995517730713,
2.5899994373321533,
2.729999542236328,
2.749999523162842,
2.8399994373321533,
],
"type": "longrope",
},
"rope_theta": 10000.0,
"sliding_window": 262144,
"tie_word_embeddings": False,
"torch_dtype": "bfloat16",
"use_cache": True,
"attention_bias": False,
"vocab_size": 32064,
}
config.update(**kwargs)
conf = transformers.Phi3Config(**config)
model = transformers.Phi3ForCausalLM(conf)
model.eval()
cache = make_dynamic_cache(
[
(torch.randn(batch_size, 32, 30, 96), torch.randn(batch_size, 32, 30, 96))
for i in range(config["num_hidden_layers"])
]
)
cache2 = make_dynamic_cache(
[
(torch.randn(batch_size + 1, 32, 31, 96), torch.randn(batch_size + 1, 32, 31, 96))
for i in range(config["num_hidden_layers"])
]
)
inputs = dict(
input_ids=torch.randint(0, 32064, (batch_size, 3)).to(torch.int64),
attention_mask=torch.ones((batch_size, 33)).to(torch.int64),
past_key_values=cache,
)
inputs2 = dict(
input_ids=torch.randint(0, 32064, (batch_size + 1, 4)).to(torch.int64),
attention_mask=torch.ones((batch_size + 1, 35)).to(torch.int64),
past_key_values=cache2,
)
return dict(inputs=inputs, model=model, inputs2=inputs2)
data = get_phi35_untrained(num_hidden_layers=2)
model, inputs, inputs2 = data["model"], data["inputs"], data["inputs2"]
print(string_type(inputs, with_shape=True))
dict(input_ids:T7s2x3,attention_mask:T7s2x33,past_key_values:DynamicCache(key_cache=#2[T1s2x32x30x96,T1s2x32x30x96], value_cache=#2[T1s2x32x30x96,T1s2x32x30x96]))
Dynamic Shapes¶
We want to infer the dynamic shapes from the two sets of inputs we gave. For that, we use a function to trace the execution of the model including its submodules. It is going to execute the model twice with the two sets of inputs and stores every intermediate input and output.
diag = trace_execution_piece_by_piece(model, [inputs, inputs2], verbose=2)
[_trace_forward_execution] -trace- M:__main__-Phi3ForCausalLM.forward
[_trace_forward_execution] -trace- .. M:model-Phi3Model.forward
[_trace_forward_execution] -trace- .... M:embed_tokens-Embedding.forward
[_trace_forward_execution] -trace- .... M:layers[0]-Phi3DecoderLayer.forward
[_trace_forward_execution] -trace- ...... M:self_attn-Phi3Attention.forward
[_trace_forward_execution] -trace- ........ M:o_proj-Linear.forward
[_trace_forward_execution] -trace- ........ M:qkv_proj-Linear.forward
[_trace_forward_execution] -trace- ...... M:mlp-Phi3MLP.forward
[_trace_forward_execution] -trace- ........ M:gate_up_proj-Linear.forward
[_trace_forward_execution] -trace- ........ M:down_proj-Linear.forward
[_trace_forward_execution] -trace- ........ M:activation_fn-SiLU.forward
[_trace_forward_execution] -trace- ...... M:input_layernorm-Phi3RMSNorm.forward
[_trace_forward_execution] -trace- ...... M:post_attention_layernorm-Phi3RMSNorm.forward
[_trace_forward_execution] -trace- ...... M:resid_attn_dropout-Dropout.forward
[_trace_forward_execution] -trace- ...... M:resid_mlp_dropout-Dropout.forward
[_trace_forward_execution] -trace- .... M:layers[1]-Phi3DecoderLayer.forward
[_trace_forward_execution] -trace- ...... M:self_attn-Phi3Attention.forward
[_trace_forward_execution] -trace- ........ M:o_proj-Linear.forward
[_trace_forward_execution] -trace- ........ M:qkv_proj-Linear.forward
[_trace_forward_execution] -trace- ...... M:mlp-Phi3MLP.forward
[_trace_forward_execution] -trace- ........ M:gate_up_proj-Linear.forward
[_trace_forward_execution] -trace- ........ M:down_proj-Linear.forward
[_trace_forward_execution] -trace- ........ M:activation_fn-SiLU.forward
[_trace_forward_execution] -trace- ...... M:input_layernorm-Phi3RMSNorm.forward
[_trace_forward_execution] -trace- ...... M:post_attention_layernorm-Phi3RMSNorm.forward
[_trace_forward_execution] -trace- ...... M:resid_attn_dropout-Dropout.forward
[_trace_forward_execution] -trace- ...... M:resid_mlp_dropout-Dropout.forward
[_trace_forward_execution] -trace- .... M:norm-Phi3RMSNorm.forward
[_trace_forward_execution] -trace- .... M:rotary_emb-Phi3RotaryEmbedding.forward
[_trace_forward_execution] -trace- .. M:lm_head-Linear.forward
[trace_execution_piece_by_piece] run with dict(args:(),kwargs:dict(input_ids:T7s2x3,attention_mask:T7s2x33,past_key_values:DynamicCache(key_cache=#2[T1s2x32x30x96,T1s2x32x30x96], value_cache=#2[T1s2x32x30x96,T1s2x32x30x96])))
[__main__:Phi3ForCausalLM] > **dict(input_ids:T7r2,attention_mask:T7r2,past_key_values:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]))
[model:Phi3Model] > **dict(input_ids:T7r2,attention_mask:T7r2,position_ids:None,past_key_values:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]),inputs_embeds:None,use_cache:None,output_attentions:bool,output_hidden_states:bool,cache_position:None)
[embed_tokens:Embedding] > T7r2
[embed_tokens:Embedding] < T1r3
[rotary_emb:Phi3RotaryEmbedding] > *(T1r3,T7r2)
[rotary_emb:Phi3RotaryEmbedding] < *(T1r3,T1r3)
[layers[0]:Phi3DecoderLayer] > *(T1r3,), **dict(attention_mask:T1r4,position_ids:T7r2,past_key_value:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]),output_attentions:bool,use_cache:bool,cache_position:T7r1,position_embeddings:(T1r3,T1r3))
[input_layernorm:Phi3RMSNorm] > T1r3
[input_layernorm:Phi3RMSNorm] < T1r3
[self_attn:Phi3Attention] > **dict(hidden_states:T1r3,attention_mask:T1r4,position_ids:T7r2,past_key_value:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]),output_attentions:bool,use_cache:bool,cache_position:T7r1,position_embeddings:(T1r3,T1r3))
[qkv_proj:Linear] > T1r3
[qkv_proj:Linear] < T1r3
[o_proj:Linear] > T1r3
[o_proj:Linear] < T1r3
[self_attn:Phi3Attention] < *(T1r3,None)
[resid_attn_dropout:Dropout] > T1r3
[resid_attn_dropout:Dropout] < T1r3
[post_attention_layernorm:Phi3RMSNorm] > T1r3
[post_attention_layernorm:Phi3RMSNorm] < T1r3
[mlp:Phi3MLP] > T1r3
[gate_up_proj:Linear] > T1r3
[gate_up_proj:Linear] < T1r3
[activation_fn:SiLU] > T1r3
[activation_fn:SiLU] < T1r3
[down_proj:Linear] > T1r3
[down_proj:Linear] < T1r3
[mlp:Phi3MLP] < T1r3
[resid_mlp_dropout:Dropout] > T1r3
[resid_mlp_dropout:Dropout] < T1r3
[layers[0]:Phi3DecoderLayer] < *(T1r3,)
[layers[1]:Phi3DecoderLayer] > *(T1r3,), **dict(attention_mask:T1r4,position_ids:T7r2,past_key_value:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]),output_attentions:bool,use_cache:bool,cache_position:T7r1,position_embeddings:(T1r3,T1r3))
[input_layernorm:Phi3RMSNorm] > T1r3
[input_layernorm:Phi3RMSNorm] < T1r3
[self_attn:Phi3Attention] > **dict(hidden_states:T1r3,attention_mask:T1r4,position_ids:T7r2,past_key_value:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]),output_attentions:bool,use_cache:bool,cache_position:T7r1,position_embeddings:(T1r3,T1r3))
[qkv_proj:Linear] > T1r3
[qkv_proj:Linear] < T1r3
[o_proj:Linear] > T1r3
[o_proj:Linear] < T1r3
[self_attn:Phi3Attention] < *(T1r3,None)
[resid_attn_dropout:Dropout] > T1r3
[resid_attn_dropout:Dropout] < T1r3
[post_attention_layernorm:Phi3RMSNorm] > T1r3
[post_attention_layernorm:Phi3RMSNorm] < T1r3
[mlp:Phi3MLP] > T1r3
[gate_up_proj:Linear] > T1r3
[gate_up_proj:Linear] < T1r3
[activation_fn:SiLU] > T1r3
[activation_fn:SiLU] < T1r3
[down_proj:Linear] > T1r3
[down_proj:Linear] < T1r3
[mlp:Phi3MLP] < T1r3
[resid_mlp_dropout:Dropout] > T1r3
[resid_mlp_dropout:Dropout] < T1r3
[layers[1]:Phi3DecoderLayer] < *(T1r3,)
[norm:Phi3RMSNorm] > T1r3
[norm:Phi3RMSNorm] < T1r3
[model:Phi3Model] < *BaseModelOutputWithPast(last_hidden_state:T1r3,past_key_values:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]))
[lm_head:Linear] > T1r3
[lm_head:Linear] < T1r3
[__main__:Phi3ForCausalLM] < *CausalLMOutputWithPast(logits:T1r3,past_key_values:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]))
[trace_execution_piece_by_piece] run with dict(args:(),kwargs:dict(input_ids:T7s3x4,attention_mask:T7s3x35,past_key_values:DynamicCache(key_cache=#2[T1s3x32x31x96,T1s3x32x31x96], value_cache=#2[T1s3x32x31x96,T1s3x32x31x96])))
[__main__:Phi3ForCausalLM] > **dict(input_ids:T7r2,attention_mask:T7r2,past_key_values:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]))
[model:Phi3Model] > **dict(input_ids:T7r2,attention_mask:T7r2,position_ids:None,past_key_values:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]),inputs_embeds:None,use_cache:None,output_attentions:bool,output_hidden_states:bool,cache_position:None)
[embed_tokens:Embedding] > T7r2
[embed_tokens:Embedding] < T1r3
[rotary_emb:Phi3RotaryEmbedding] > *(T1r3,T7r2)
[rotary_emb:Phi3RotaryEmbedding] < *(T1r3,T1r3)
[layers[0]:Phi3DecoderLayer] > *(T1r3,), **dict(attention_mask:T1r4,position_ids:T7r2,past_key_value:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]),output_attentions:bool,use_cache:bool,cache_position:T7r1,position_embeddings:(T1r3,T1r3))
[input_layernorm:Phi3RMSNorm] > T1r3
[input_layernorm:Phi3RMSNorm] < T1r3
[self_attn:Phi3Attention] > **dict(hidden_states:T1r3,attention_mask:T1r4,position_ids:T7r2,past_key_value:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]),output_attentions:bool,use_cache:bool,cache_position:T7r1,position_embeddings:(T1r3,T1r3))
[qkv_proj:Linear] > T1r3
[qkv_proj:Linear] < T1r3
[o_proj:Linear] > T1r3
[o_proj:Linear] < T1r3
[self_attn:Phi3Attention] < *(T1r3,None)
[resid_attn_dropout:Dropout] > T1r3
[resid_attn_dropout:Dropout] < T1r3
[post_attention_layernorm:Phi3RMSNorm] > T1r3
[post_attention_layernorm:Phi3RMSNorm] < T1r3
[mlp:Phi3MLP] > T1r3
[gate_up_proj:Linear] > T1r3
[gate_up_proj:Linear] < T1r3
[activation_fn:SiLU] > T1r3
[activation_fn:SiLU] < T1r3
[down_proj:Linear] > T1r3
[down_proj:Linear] < T1r3
[mlp:Phi3MLP] < T1r3
[resid_mlp_dropout:Dropout] > T1r3
[resid_mlp_dropout:Dropout] < T1r3
[layers[0]:Phi3DecoderLayer] < *(T1r3,)
[layers[1]:Phi3DecoderLayer] > *(T1r3,), **dict(attention_mask:T1r4,position_ids:T7r2,past_key_value:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]),output_attentions:bool,use_cache:bool,cache_position:T7r1,position_embeddings:(T1r3,T1r3))
[input_layernorm:Phi3RMSNorm] > T1r3
[input_layernorm:Phi3RMSNorm] < T1r3
[self_attn:Phi3Attention] > **dict(hidden_states:T1r3,attention_mask:T1r4,position_ids:T7r2,past_key_value:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]),output_attentions:bool,use_cache:bool,cache_position:T7r1,position_embeddings:(T1r3,T1r3))
[qkv_proj:Linear] > T1r3
[qkv_proj:Linear] < T1r3
[o_proj:Linear] > T1r3
[o_proj:Linear] < T1r3
[self_attn:Phi3Attention] < *(T1r3,None)
[resid_attn_dropout:Dropout] > T1r3
[resid_attn_dropout:Dropout] < T1r3
[post_attention_layernorm:Phi3RMSNorm] > T1r3
[post_attention_layernorm:Phi3RMSNorm] < T1r3
[mlp:Phi3MLP] > T1r3
[gate_up_proj:Linear] > T1r3
[gate_up_proj:Linear] < T1r3
[activation_fn:SiLU] > T1r3
[activation_fn:SiLU] < T1r3
[down_proj:Linear] > T1r3
[down_proj:Linear] < T1r3
[mlp:Phi3MLP] < T1r3
[resid_mlp_dropout:Dropout] > T1r3
[resid_mlp_dropout:Dropout] < T1r3
[layers[1]:Phi3DecoderLayer] < *(T1r3,)
[norm:Phi3RMSNorm] > T1r3
[norm:Phi3RMSNorm] < T1r3
[model:Phi3Model] < *BaseModelOutputWithPast(last_hidden_state:T1r3,past_key_values:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]))
[lm_head:Linear] > T1r3
[lm_head:Linear] < T1r3
[__main__:Phi3ForCausalLM] < *CausalLMOutputWithPast(logits:T1r3,past_key_values:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]))
[trace_forward_execution] traced execution of model Phi3ForCausalLM
>>> __main__: Phi3ForCausalLM
> ((),dict(input_ids:CT7s2x3[3876,15594:A9096.5],attention_mask:CT7s2x33[1,1:A1.0],past_key_values:DynamicCache(key_cache=#2[CT1s2x32x30x96[-4.400763034820557,4.259133338928223:A-0.004826152082167923],CT1s2x32x30x96[-4.0469818115234375,4.471982479095459:A0.002217433422628052]], value_cache=#2[CT1s2x32x30x96[-4.405541896820068,4.668274402618408:A0.002596911304140301],CT1s2x32x30x96[-4.310667037963867,4.2944207191467285:A0.00028137129325799627]])))
> ((),dict(input_ids:CT7s3x4[2392,29616:A20512.083333333332],attention_mask:CT7s3x35[1,1:A1.0],past_key_values:DynamicCache(key_cache=#2[CT1s3x32x31x96[-4.828048229217529,4.243164539337158:A0.00031101864580231346],CT1s3x32x31x96[-4.626999855041504,4.47794246673584:A-0.0025560081888748925]], value_cache=#2[CT1s3x32x31x96[-4.349869728088379,4.253993034362793:A0.0011117361406822917],CT1s3x32x31x96[-4.810003280639648,4.869333267211914:A-0.00027058590800271105]])))
>>> model: Phi3Model
> ((),dict(input_ids:CT7s2x3[3876,15594:A9096.5],attention_mask:CT7s2x33[1,1:A1.0],position_ids:None,past_key_values:DynamicCache(key_cache=#2[CT1s2x32x30x96[-4.400763034820557,4.259133338928223:A-0.004826152082167923],CT1s2x32x30x96[-4.0469818115234375,4.471982479095459:A0.002217433422628052]], value_cache=#2[CT1s2x32x30x96[-4.405541896820068,4.668274402618408:A0.002596911304140301],CT1s2x32x30x96[-4.310667037963867,4.2944207191467285:A0.00028137129325799627]]),inputs_embeds:None,use_cache:None,output_attentions:bool=False,output_hidden_states:bool=False,cache_position:None))
> ((),dict(input_ids:CT7s3x4[2392,29616:A20512.083333333332],attention_mask:CT7s3x35[1,1:A1.0],position_ids:None,past_key_values:DynamicCache(key_cache=#2[CT1s3x32x31x96[-4.828048229217529,4.243164539337158:A0.00031101864580231346],CT1s3x32x31x96[-4.626999855041504,4.47794246673584:A-0.0025560081888748925]], value_cache=#2[CT1s3x32x31x96[-4.349869728088379,4.253993034362793:A0.0011117361406822917],CT1s3x32x31x96[-4.810003280639648,4.869333267211914:A-0.00027058590800271105]]),inputs_embeds:None,use_cache:None,output_attentions:bool=False,output_hidden_states:bool=False,cache_position:None))
>>> embed_tokens: Embedding
> ((CT7s2x3[3876,15594:A9096.5],),{})
> ((CT7s3x4[2392,29616:A20512.083333333332],),{})
< (CT1s2x3x3072[-0.08262448012828827,0.07648692280054092:A0.00013174691782688906],)
< (CT1s3x4x3072[-0.08454269170761108,0.07825997471809387:A-2.2594149214932857e-05],)
<<<
>>> layers[0]: Phi3DecoderLayer
> ((CT1s2x3x3072[-0.08262448012828827,0.07648692280054092:A0.00013174691782688906],),dict(attention_mask:CT1s2x1x3x33[-3.4028234663852886e+38,-0.0:A-1.0311586261773601e+37],position_ids:CT7s1x3[30,32:A31.0],past_key_value:DynamicCache(key_cache=#2[CT1s2x32x30x96[-4.400763034820557,4.259133338928223:A-0.004826152082167923],CT1s2x32x30x96[-4.0469818115234375,4.471982479095459:A0.002217433422628052]], value_cache=#2[CT1s2x32x30x96[-4.405541896820068,4.668274402618408:A0.002596911304140301],CT1s2x32x30x96[-4.310667037963867,4.2944207191467285:A0.00028137129325799627]]),output_attentions:bool=False,use_cache:bool=True,cache_position:CT7s3[30,32:A31.0],position_embeddings:(CT1s1x3x96[-1.1855769157409668,1.1902371644973755:A0.746652018013669],CT1s1x3x96[-1.1887905597686768,1.190193772315979:A0.1589894221542636])))
> ((CT1s3x4x3072[-0.08454269170761108,0.07825997471809387:A-2.2594149214932857e-05],),dict(attention_mask:CT1s3x1x4x35[-3.4028234663852886e+38,-0.0:A-1.4583529141651237e+37],position_ids:CT7s1x4[31,34:A32.5],past_key_value:DynamicCache(key_cache=#2[CT1s3x32x31x96[-4.828048229217529,4.243164539337158:A0.00031101864580231346],CT1s3x32x31x96[-4.626999855041504,4.47794246673584:A-0.0025560081888748925]], value_cache=#2[CT1s3x32x31x96[-4.349869728088379,4.253993034362793:A0.0011117361406822917],CT1s3x32x31x96[-4.810003280639648,4.869333267211914:A-0.00027058590800271105]]),output_attentions:bool=False,use_cache:bool=True,cache_position:CT7s4[31,34:A32.5],position_embeddings:(CT1s1x4x96[-1.1855769157409668,1.190237045288086:A0.7129333875218435],CT1s1x4x96[-1.1719439029693604,1.1902378797531128:A0.18296290554159592])))
>>> self_attn: Phi3Attention
> ((),dict(hidden_states:CT1s2x3x3072[-4.117458343505859,3.675187587738037:A0.006496929148884729],attention_mask:CT1s2x1x3x33[-3.4028234663852886e+38,-0.0:A-1.0311586261773601e+37],position_ids:CT7s1x3[30,32:A31.0],past_key_value:DynamicCache(key_cache=#2[CT1s2x32x30x96[-4.400763034820557,4.259133338928223:A-0.004826152082167923],CT1s2x32x30x96[-4.0469818115234375,4.471982479095459:A0.002217433422628052]], value_cache=#2[CT1s2x32x30x96[-4.405541896820068,4.668274402618408:A0.002596911304140301],CT1s2x32x30x96[-4.310667037963867,4.2944207191467285:A0.00028137129325799627]]),output_attentions:bool=False,use_cache:bool=True,cache_position:CT7s3[30,32:A31.0],position_embeddings:(CT1s1x3x96[-1.1855769157409668,1.1902371644973755:A0.746652018013669],CT1s1x3x96[-1.1887905597686768,1.190193772315979:A0.1589894221542636])))
> ((),dict(hidden_states:CT1s3x4x3072[-4.234438896179199,3.8390731811523438:A-0.0011698477338398423],attention_mask:CT1s3x1x4x35[-3.4028234663852886e+38,-0.0:A-1.4583529141651237e+37],position_ids:CT7s1x4[31,34:A32.5],past_key_value:DynamicCache(key_cache=#2[CT1s3x32x31x96[-4.828048229217529,4.243164539337158:A0.00031101864580231346],CT1s3x32x31x96[-4.626999855041504,4.47794246673584:A-0.0025560081888748925]], value_cache=#2[CT1s3x32x31x96[-4.349869728088379,4.253993034362793:A0.0011117361406822917],CT1s3x32x31x96[-4.810003280639648,4.869333267211914:A-0.00027058590800271105]]),output_attentions:bool=False,use_cache:bool=True,cache_position:CT7s4[31,34:A32.5],position_embeddings:(CT1s1x4x96[-1.1855769157409668,1.190237045288086:A0.7129333875218435],CT1s1x4x96[-1.1719439029693604,1.1902378797531128:A0.18296290554159592])))
>>> o_proj: Linear
> ((CT1s2x3x3072[-1.7804882526397705,1.7273778915405273:A0.003596998118933412],),{})
> ((CT1s3x4x3072[-3.1012768745422363,2.9759175777435303:A0.0021097844879451383],),{})
< (CT1s2x3x3072[-1.6213274002075195,1.6112512350082397:A0.0015135138111317145],)
< (CT1s3x4x3072[-1.6543391942977905,1.7866795063018799:A-0.0027640321040425736],)
<<<
>>> qkv_proj: Linear
> ((CT1s2x3x3072[-4.117458343505859,3.675187587738037:A0.006496929148884729],),{})
> ((CT1s3x4x3072[-4.234438896179199,3.8390731811523438:A-0.0011698477338398423],),{})
< (CT1s2x3x9216[-4.603874683380127,4.854994297027588:A-0.007235225691783011],)
< (CT1s3x4x9216[-5.057411193847656,4.701048374176025:A-0.0005786661612424487],)
<<<
< (CT1s2x3x3072[-1.6213274002075195,1.6112512350082397:A0.0015135138111317145],None)
< (CT1s3x4x3072[-1.6543391942977905,1.7866795063018799:A-0.0027640321040425736],None)
<<<
>>> mlp: Phi3MLP
> ((CT1s2x3x3072[-4.074462413787842,4.080641269683838:A0.004271472019650317],),{})
> ((CT1s3x4x3072[-4.160494804382324,4.450273036956787:A-0.007759615481418791],),{})
>>> gate_up_proj: Linear
> ((CT1s2x3x3072[-4.074462413787842,4.080641269683838:A0.004271472019650317],),{})
> ((CT1s3x4x3072[-4.160494804382324,4.450273036956787:A-0.007759615481418791],),{})
< (CT1s2x3x16384[-4.665277481079102,4.764751434326172:A-0.003516147276544738],)
< (CT1s3x4x16384[-4.765247821807861,4.7090325355529785:A-0.0005196295543422972],)
<<<
>>> down_proj: Linear
> ((CT1s2x3x8192[-12.33570671081543,11.671303749084473:A-0.0007140373650187307],),{})
> ((CT1s3x4x8192[-9.95230770111084,10.653127670288086:A-0.0001526525493489491],),{})
< (CT1s2x3x3072[-5.095609188079834,5.600772857666016:A0.017001853358547377],)
< (CT1s3x4x3072[-5.286900043487549,6.241175174713135:A0.00745626809162382],)
<<<
>>> activation_fn: SiLU
> ((CT1s2x3x8192[-4.665277481079102,4.764751434326172:A-0.0084779705295972],),{})
> ((CT1s3x4x8192[-4.765247821807861,4.606565952301025:A0.002571067230685268],),{})
< (CT1s2x3x8192[-0.27846455574035645,4.724475383758545:A0.2441930089948916],)
< (CT1s3x4x8192[-0.27846455574035645,4.561019420623779:A0.24662724596589647],)
<<<
< (CT1s2x3x3072[-5.095609188079834,5.600772857666016:A0.017001853358547377],)
< (CT1s3x4x3072[-5.286900043487549,6.241175174713135:A0.00745626809162382],)
<<<
>>> input_layernorm: Phi3RMSNorm
> ((CT1s2x3x3072[-0.08262448012828827,0.07648692280054092:A0.00013174691782688906],),{})
> ((CT1s3x4x3072[-0.08454269170761108,0.07825997471809387:A-2.2594149214932857e-05],),{})
< (CT1s2x3x3072[-4.117458343505859,3.675187587738037:A0.006496929148884729],)
< (CT1s3x4x3072[-4.234438896179199,3.8390731811523438:A-0.0011698477338398423],)
<<<
>>> post_attention_layernorm: Phi3RMSNorm
> ((CT1s2x3x3072[-1.59036123752594,1.5879346132278442:A0.0016452607151254345],),{})
> ((CT1s3x4x3072[-1.6508663892745972,1.791300892829895:A-0.002786626275726197],),{})
< (CT1s2x3x3072[-4.074462413787842,4.080641269683838:A0.004271472019650317],)
< (CT1s3x4x3072[-4.160494804382324,4.450273036956787:A-0.007759615481418791],)
<<<
>>> resid_attn_dropout: Dropout
> ((CT1s2x3x3072[-1.6213274002075195,1.6112512350082397:A0.0015135138111317145],),{})
> ((CT1s3x4x3072[-1.6543391942977905,1.7866795063018799:A-0.0027640321040425736],),{})
< (CT1s2x3x3072[-1.6213274002075195,1.6112512350082397:A0.0015135138111317145],)
< (CT1s3x4x3072[-1.6543391942977905,1.7866795063018799:A-0.0027640321040425736],)
<<<
>>> resid_mlp_dropout: Dropout
> ((CT1s2x3x3072[-5.095609188079834,5.600772857666016:A0.017001853358547377],),{})
> ((CT1s3x4x3072[-5.286900043487549,6.241175174713135:A0.00745626809162382],),{})
< (CT1s2x3x3072[-5.095609188079834,5.600772857666016:A0.017001853358547377],)
< (CT1s3x4x3072[-5.286900043487549,6.241175174713135:A0.00745626809162382],)
<<<
< (CT1s2x3x3072[-5.606287479400635,5.762608051300049:A0.018647113798023283],)
< (CT1s3x4x3072[-5.794071197509766,5.967385292053223:A0.004669641863668201],)
<<<
>>> layers[1]: Phi3DecoderLayer
> ((CT1s2x3x3072[-5.606287479400635,5.762608051300049:A0.018647113798023283],),dict(attention_mask:CT1s2x1x3x33[-3.4028234663852886e+38,-0.0:A-1.0311586261773601e+37],position_ids:CT7s1x3[30,32:A31.0],past_key_value:DynamicCache(key_cache=#2[CT1s2x32x33x96[-5.061519622802734,4.763699054718018:A-0.005803250564235515],CT1s2x32x30x96[-4.0469818115234375,4.471982479095459:A0.002217433422628052]], value_cache=#2[CT1s2x32x33x96[-4.603874683380127,4.668274402618408:A0.0014253464397422568],CT1s2x32x30x96[-4.310667037963867,4.2944207191467285:A0.00028137129325799627]]),output_attentions:bool=False,use_cache:bool=True,cache_position:CT7s3[30,32:A31.0],position_embeddings:(CT1s1x3x96[-1.1855769157409668,1.1902371644973755:A0.746652018013669],CT1s1x3x96[-1.1887905597686768,1.190193772315979:A0.1589894221542636])))
> ((CT1s3x4x3072[-5.794071197509766,5.967385292053223:A0.004669641863668201],),dict(attention_mask:CT1s3x1x4x35[-3.4028234663852886e+38,-0.0:A-1.4583529141651237e+37],position_ids:CT7s1x4[31,34:A32.5],past_key_value:DynamicCache(key_cache=#2[CT1s3x32x35x96[-5.101267337799072,5.592326641082764:A0.0004619398607301088],CT1s3x32x31x96[-4.626999855041504,4.47794246673584:A-0.0025560081888748925]], value_cache=#2[CT1s3x32x35x96[-5.057411193847656,4.3394317626953125:A0.00042293995440766975],CT1s3x32x31x96[-4.810003280639648,4.869333267211914:A-0.00027058590800271105]]),output_attentions:bool=False,use_cache:bool=True,cache_position:CT7s4[31,34:A32.5],position_embeddings:(CT1s1x4x96[-1.1855769157409668,1.190237045288086:A0.7129333875218435],CT1s1x4x96[-1.1719439029693604,1.1902378797531128:A0.18296290554159592])))
>>> self_attn: Phi3Attention
> ((),dict(hidden_states:CT1s2x3x3072[-4.0454277992248535,4.159030914306641:A0.013164340832402624],attention_mask:CT1s2x1x3x33[-3.4028234663852886e+38,-0.0:A-1.0311586261773601e+37],position_ids:CT7s1x3[30,32:A31.0],past_key_value:DynamicCache(key_cache=#2[CT1s2x32x33x96[-5.061519622802734,4.763699054718018:A-0.005803250564235515],CT1s2x32x30x96[-4.0469818115234375,4.471982479095459:A0.002217433422628052]], value_cache=#2[CT1s2x32x33x96[-4.603874683380127,4.668274402618408:A0.0014253464397422568],CT1s2x32x30x96[-4.310667037963867,4.2944207191467285:A0.00028137129325799627]]),output_attentions:bool=False,use_cache:bool=True,cache_position:CT7s3[30,32:A31.0],position_embeddings:(CT1s1x3x96[-1.1855769157409668,1.1902371644973755:A0.746652018013669],CT1s1x3x96[-1.1887905597686768,1.190193772315979:A0.1589894221542636])))
> ((),dict(hidden_states:CT1s3x4x3072[-3.9066245555877686,4.0234808921813965:A0.003327302300785028],attention_mask:CT1s3x1x4x35[-3.4028234663852886e+38,-0.0:A-1.4583529141651237e+37],position_ids:CT7s1x4[31,34:A32.5],past_key_value:DynamicCache(key_cache=#2[CT1s3x32x35x96[-5.101267337799072,5.592326641082764:A0.0004619398607301088],CT1s3x32x31x96[-4.626999855041504,4.47794246673584:A-0.0025560081888748925]], value_cache=#2[CT1s3x32x35x96[-5.057411193847656,4.3394317626953125:A0.00042293995440766975],CT1s3x32x31x96[-4.810003280639648,4.869333267211914:A-0.00027058590800271105]]),output_attentions:bool=False,use_cache:bool=True,cache_position:CT7s4[31,34:A32.5],position_embeddings:(CT1s1x4x96[-1.1855769157409668,1.190237045288086:A0.7129333875218435],CT1s1x4x96[-1.1719439029693604,1.1902378797531128:A0.18296290554159592])))
>>> o_proj: Linear
> ((CT1s2x3x3072[-2.426612615585327,2.5618062019348145:A0.0013105594592492447],),{})
> ((CT1s3x4x3072[-2.194685935974121,1.6999558210372925:A0.0006558623772832772],),{})
< (CT1s2x3x3072[-1.6817601919174194,1.8244985342025757:A0.007823421057183724],)
< (CT1s3x4x3072[-1.51430082321167,1.9903841018676758:A-0.0005759481610236131],)
<<<
>>> qkv_proj: Linear
> ((CT1s2x3x3072[-4.0454277992248535,4.159030914306641:A0.013164340832402624],),{})
> ((CT1s3x4x3072[-3.9066245555877686,4.0234808921813965:A0.003327302300785028],),{})
< (CT1s2x3x9216[-4.424978733062744,4.548888206481934:A0.007687200885247539],)
< (CT1s3x4x9216[-5.294676780700684,4.829901695251465:A0.0011063482611651427],)
<<<
< (CT1s2x3x3072[-1.6817601919174194,1.8244985342025757:A0.007823421057183724],None)
< (CT1s3x4x3072[-1.51430082321167,1.9903841018676758:A-0.0005759481610236131],None)
<<<
>>> mlp: Phi3MLP
> ((CT1s2x3x3072[-3.9352612495422363,3.729083776473999:A0.018089336692754848],),{})
> ((CT1s3x4x3072[-3.7487435340881348,4.227113723754883:A0.002809795542497976],),{})
>>> gate_up_proj: Linear
> ((CT1s2x3x3072[-3.9352612495422363,3.729083776473999:A0.018089336692754848],),{})
> ((CT1s3x4x3072[-3.7487435340881348,4.227113723754883:A0.002809795542497976],),{})
< (CT1s2x3x16384[-4.6556878089904785,4.95295524597168:A0.005004160443962273],)
< (CT1s3x4x16384[-4.945455551147461,5.058211326599121:A-0.0019263709165381708],)
<<<
>>> down_proj: Linear
> ((CT1s2x3x8192[-8.74767017364502,9.395020484924316:A0.0006071647130518922],),{})
> ((CT1s3x4x8192[-13.272783279418945,10.04904842376709:A-0.0015269200680454332],),{})
< (CT1s2x3x3072[-5.51132869720459,5.5960211753845215:A-0.0008730196218114846],)
< (CT1s3x4x3072[-5.300881862640381,5.086551189422607:A-0.002262299616445615],)
<<<
>>> activation_fn: SiLU
> ((CT1s2x3x8192[-4.588644027709961,4.672793388366699:A0.0015853615327661903],),{})
> ((CT1s3x4x8192[-4.798925399780273,5.058211326599121:A-0.0037998580466028407],),{})
< (CT1s2x3x8192[-0.27846455574035645,4.629525184631348:A0.24538704169494321],)
< (CT1s3x4x8192[-0.27846455574035645,5.026259422302246:A0.24493035986874703],)
<<<
< (CT1s2x3x3072[-5.51132869720459,5.5960211753845215:A-0.0008730196218114846],)
< (CT1s3x4x3072[-5.300881862640381,5.086551189422607:A-0.002262299616445615],)
<<<
>>> input_layernorm: Phi3RMSNorm
> ((CT1s2x3x3072[-5.606287479400635,5.762608051300049:A0.018647113798023283],),{})
> ((CT1s3x4x3072[-5.794071197509766,5.967385292053223:A0.004669641863668201],),{})
< (CT1s2x3x3072[-4.0454277992248535,4.159030914306641:A0.013164340832402624],)
< (CT1s3x4x3072[-3.9066245555877686,4.0234808921813965:A0.003327302300785028],)
<<<
>>> post_attention_layernorm: Phi3RMSNorm
> ((CT1s2x3x3072[-5.665860176086426,5.33676815032959:A0.02647053424324339],),{})
> ((CT1s3x4x3072[-5.680884838104248,6.474119186401367:A0.004093693750544642],),{})
< (CT1s2x3x3072[-3.9352612495422363,3.729083776473999:A0.018089336692754848],)
< (CT1s3x4x3072[-3.7487435340881348,4.227113723754883:A0.002809795542497976],)
<<<
>>> resid_attn_dropout: Dropout
> ((CT1s2x3x3072[-1.6817601919174194,1.8244985342025757:A0.007823421057183724],),{})
> ((CT1s3x4x3072[-1.51430082321167,1.9903841018676758:A-0.0005759481610236131],),{})
< (CT1s2x3x3072[-1.6817601919174194,1.8244985342025757:A0.007823421057183724],)
< (CT1s3x4x3072[-1.51430082321167,1.9903841018676758:A-0.0005759481610236131],)
<<<
>>> resid_mlp_dropout: Dropout
> ((CT1s2x3x3072[-5.51132869720459,5.5960211753845215:A-0.0008730196218114846],),{})
> ((CT1s3x4x3072[-5.300881862640381,5.086551189422607:A-0.002262299616445615],),{})
< (CT1s2x3x3072[-5.51132869720459,5.5960211753845215:A-0.0008730196218114846],)
< (CT1s3x4x3072[-5.300881862640381,5.086551189422607:A-0.002262299616445615],)
<<<
< (CT1s2x3x3072[-7.9020490646362305,8.29597282409668:A0.025597514293015895],)
< (CT1s3x4x3072[-8.080930709838867,8.061180114746094:A0.0018313946399454533],)
<<<
>>> norm: Phi3RMSNorm
> ((CT1s2x3x3072[-7.9020490646362305,8.29597282409668:A0.025597514293015895],),{})
> ((CT1s3x4x3072[-8.080930709838867,8.061180114746094:A0.0018313946399454533],),{})
< (CT1s2x3x3072[-4.05719518661499,4.155795574188232:A0.012949258255610453],)
< (CT1s3x4x3072[-3.9973807334899902,3.9562745094299316:A0.0008511715070787332],)
<<<
>>> rotary_emb: Phi3RotaryEmbedding
> ((CT1s2x3x3072[-0.08262448012828827,0.07648692280054092:A0.00013174691782688906],CT7s1x3[30,32:A31.0]),{})
> ((CT1s3x4x3072[-0.08454269170761108,0.07825997471809387:A-2.2594149214932857e-05],CT7s1x4[31,34:A32.5]),{})
< (CT1s1x3x96[-1.1855769157409668,1.1902371644973755:A0.746652018013669],CT1s1x3x96[-1.1887905597686768,1.190193772315979:A0.1589894221542636])
< (CT1s1x4x96[-1.1855769157409668,1.190237045288086:A0.7129333875218435],CT1s1x4x96[-1.1719439029693604,1.1902378797531128:A0.18296290554159592])
<<<
< (dict(last_hidden_state:CT1s2x3x3072[-4.05719518661499,4.155795574188232:A0.012949258255610453],past_key_values:DynamicCache(key_cache=#2[CT1s2x32x33x96[-5.061519622802734,4.763699054718018:A-0.005803250564235515],CT1s2x32x33x96[-5.262935161590576,5.203064441680908:A0.001793874462405861]], value_cache=#2[CT1s2x32x33x96[-4.603874683380127,4.668274402618408:A0.0014253464397422568],CT1s2x32x33x96[-4.3247175216674805,4.2944207191467285:A0.0008252018333532062]])),)
< (dict(last_hidden_state:CT1s3x4x3072[-3.9973807334899902,3.9562745094299316:A0.0008511715070787332],past_key_values:DynamicCache(key_cache=#2[CT1s3x32x35x96[-5.101267337799072,5.592326641082764:A0.0004619398607301088],CT1s3x32x35x96[-6.038402080535889,5.146603584289551:A-0.0029978743856185967]], value_cache=#2[CT1s3x32x35x96[-5.057411193847656,4.3394317626953125:A0.00042293995440766975],CT1s3x32x35x96[-5.294676780700684,4.869333267211914:A3.3132155645670344e-05]])),)
<<<
>>> lm_head: Linear
> ((CT1s2x3x3072[-4.05719518661499,4.155795574188232:A0.012949258255610453],),{})
> ((CT1s3x4x3072[-3.9973807334899902,3.9562745094299316:A0.0008511715070787332],),{})
< (CT1s2x3x32064[-4.982978820800781,4.97531270980835:A5.55359560734437e-05],)
< (CT1s3x4x32064[-5.064220428466797,5.067513465881348:A-0.0019615298296192804],)
<<<
< (dict(logits:CT1s2x3x32064[-4.982978820800781,4.97531270980835:A5.55359560734437e-05],past_key_values:DynamicCache(key_cache=#2[CT1s2x32x33x96[-5.061519622802734,4.763699054718018:A-0.005803250564235515],CT1s2x32x33x96[-5.262935161590576,5.203064441680908:A0.001793874462405861]], value_cache=#2[CT1s2x32x33x96[-4.603874683380127,4.668274402618408:A0.0014253464397422568],CT1s2x32x33x96[-4.3247175216674805,4.2944207191467285:A0.0008252018333532062]])),)
< (dict(logits:CT1s3x4x32064[-5.064220428466797,5.067513465881348:A-0.0019615298296192804],past_key_values:DynamicCache(key_cache=#2[CT1s3x32x35x96[-5.101267337799072,5.592326641082764:A0.0004619398607301088],CT1s3x32x35x96[-6.038402080535889,5.146603584289551:A-0.0029978743856185967]], value_cache=#2[CT1s3x32x35x96[-5.057411193847656,4.3394317626953125:A0.00042293995440766975],CT1s3x32x35x96[-5.294676780700684,4.869333267211914:A3.3132155645670344e-05]])),)
<<<
[_untrace_forward_execution] M:__main__-Phi3ForCausalLM
[_untrace_forward_execution] .. M:model-Phi3Model
[_untrace_forward_execution] .... M:embed_tokens-Embedding
[_untrace_forward_execution] .... M:layers[0]-Phi3DecoderLayer
[_untrace_forward_execution] ...... M:self_attn-Phi3Attention
[_untrace_forward_execution] ........ M:o_proj-Linear
[_untrace_forward_execution] ........ M:qkv_proj-Linear
[_untrace_forward_execution] ...... M:mlp-Phi3MLP
[_untrace_forward_execution] ........ M:gate_up_proj-Linear
[_untrace_forward_execution] ........ M:down_proj-Linear
[_untrace_forward_execution] ........ M:activation_fn-SiLU
[_untrace_forward_execution] ...... M:input_layernorm-Phi3RMSNorm
[_untrace_forward_execution] ...... M:post_attention_layernorm-Phi3RMSNorm
[_untrace_forward_execution] ...... M:resid_attn_dropout-Dropout
[_untrace_forward_execution] ...... M:resid_mlp_dropout-Dropout
[_untrace_forward_execution] .... M:layers[1]-Phi3DecoderLayer
[_untrace_forward_execution] ...... M:self_attn-Phi3Attention
[_untrace_forward_execution] ........ M:o_proj-Linear
[_untrace_forward_execution] ........ M:qkv_proj-Linear
[_untrace_forward_execution] ...... M:mlp-Phi3MLP
[_untrace_forward_execution] ........ M:gate_up_proj-Linear
[_untrace_forward_execution] ........ M:down_proj-Linear
[_untrace_forward_execution] ........ M:activation_fn-SiLU
[_untrace_forward_execution] ...... M:input_layernorm-Phi3RMSNorm
[_untrace_forward_execution] ...... M:post_attention_layernorm-Phi3RMSNorm
[_untrace_forward_execution] ...... M:resid_attn_dropout-Dropout
[_untrace_forward_execution] ...... M:resid_mlp_dropout-Dropout
[_untrace_forward_execution] .... M:norm-Phi3RMSNorm
[_untrace_forward_execution] .... M:rotary_emb-Phi3RotaryEmbedding
[_untrace_forward_execution] .. M:lm_head-Linear
Now we keep in memory every input/output for the submodules, we can guess the dynamic shapes for every of them. The final ones:
dynamic_shapes = diag.guess_dynamic_shapes()
print("The dynamic shapes are:")
pprint.pprint(dynamic_shapes)
The dynamic shapes are:
((),
{'attention_mask': {0: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True),
1: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True)},
'input_ids': {0: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True),
1: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True)},
'past_key_values': [[{0: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True),
2: _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)}],
[{0: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True),
2: _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)}]]})
And all the dynamic shapes all along the traced submodules.
print(
diag.pretty_text(
with_dynamic_shape=True,
with_shape=False,
with_min_max=False,
with_device=False,
with_inputs=False,
).replace("<_DimHint.DYNAMIC: 3>", "DYN")
)
>>> __main__: Phi3ForCausalLM
DS=((), {'attention_mask': {0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, 'input_ids': {0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, 'past_key_values': [[{0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 2: _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)}], [{0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 2: _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)}]]})
>>> model: Phi3Model
DS=((), {'attention_mask': {0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, 'cache_position': None, 'input_ids': {0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, 'inputs_embeds': None, 'output_attentions': None, 'output_hidden_states': None, 'past_key_values': [[{0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 2: _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)}], [{0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 2: _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)}]], 'position_ids': None, 'use_cache': None})
>>> embed_tokens: Embedding: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
>>> layers[0]: Phi3DecoderLayer
DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {'attention_mask': {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)}, 'cache_position': {0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, 'output_attentions': None, 'past_key_value': [[{0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 2: _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)}], [{0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 2: _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)}]], 'position_embeddings': ({1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, {1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}), 'position_ids': {1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, 'use_cache': None})
>>> self_attn: Phi3Attention
DS=((), {'attention_mask': {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)}, 'cache_position': {0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, 'hidden_states': {0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, 'output_attentions': None, 'past_key_value': [[{0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 2: _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)}], [{0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 2: _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)}]], 'position_embeddings': ({1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, {1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}), 'position_ids': {1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, 'use_cache': None})
>>> o_proj: Linear: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
>>> qkv_proj: Linear: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
<<<
>>> mlp: Phi3MLP
DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {})
>>> gate_up_proj: Linear: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
>>> down_proj: Linear: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
>>> activation_fn: SiLU: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
<<<
>>> input_layernorm: Phi3RMSNorm: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
>>> post_attention_layernorm: Phi3RMSNorm: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
>>> resid_attn_dropout: Dropout: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
>>> resid_mlp_dropout: Dropout: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
<<<
>>> layers[1]: Phi3DecoderLayer
DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {'attention_mask': {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)}, 'cache_position': {0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, 'output_attentions': None, 'past_key_value': [[{0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 2: _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)}], [{0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 2: _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)}]], 'position_embeddings': ({1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, {1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}), 'position_ids': {1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, 'use_cache': None})
>>> self_attn: Phi3Attention
DS=((), {'attention_mask': {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)}, 'cache_position': {0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, 'hidden_states': {0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, 'output_attentions': None, 'past_key_value': [[{0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 2: _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)}], [{0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 2: _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)}]], 'position_embeddings': ({1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, {1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}), 'position_ids': {1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, 'use_cache': None})
>>> o_proj: Linear: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
>>> qkv_proj: Linear: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
<<<
>>> mlp: Phi3MLP
DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {})
>>> gate_up_proj: Linear: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
>>> down_proj: Linear: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
>>> activation_fn: SiLU: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
<<<
>>> input_layernorm: Phi3RMSNorm: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
>>> post_attention_layernorm: Phi3RMSNorm: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
>>> resid_attn_dropout: Dropout: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
>>> resid_mlp_dropout: Dropout: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
<<<
>>> norm: Phi3RMSNorm: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
>>> rotary_emb: Phi3RotaryEmbedding: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}, {1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)}), {}) <<<
<<<
>>> lm_head: Linear: DS=(({0: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True), 1: _DimHint(type=<_DimHintType.DYNAMIC: 3>, min=None, max=None, _factory=True)},), {}) <<<
<<<
Evaluate the export¶
In many cases, the export (to torch.fx.Graph, to ONNX)
does not work on the first try. We need a way to understand
how much the model can be exported. It can be used to evaluate
the how much code needs to be rewritten or patched to be exportable.
The verbosity can be increase to show dynamic shapes, results
of the discrepancies.
Let’s display the module and its submodule first.
print(
diag.pretty_text(
with_dynamic_shape=False,
with_shape=False,
with_min_max=False,
with_device=False,
with_inputs=False,
)
)
>>> __main__: Phi3ForCausalLM
>>> model: Phi3Model
>>> embed_tokens: Embedding <<<
>>> layers[0]: Phi3DecoderLayer
>>> self_attn: Phi3Attention
>>> o_proj: Linear <<<
>>> qkv_proj: Linear <<<
<<<
>>> mlp: Phi3MLP
>>> gate_up_proj: Linear <<<
>>> down_proj: Linear <<<
>>> activation_fn: SiLU <<<
<<<
>>> input_layernorm: Phi3RMSNorm <<<
>>> post_attention_layernorm: Phi3RMSNorm <<<
>>> resid_attn_dropout: Dropout <<<
>>> resid_mlp_dropout: Dropout <<<
<<<
>>> layers[1]: Phi3DecoderLayer
>>> self_attn: Phi3Attention
>>> o_proj: Linear <<<
>>> qkv_proj: Linear <<<
<<<
>>> mlp: Phi3MLP
>>> gate_up_proj: Linear <<<
>>> down_proj: Linear <<<
>>> activation_fn: SiLU <<<
<<<
>>> input_layernorm: Phi3RMSNorm <<<
>>> post_attention_layernorm: Phi3RMSNorm <<<
>>> resid_attn_dropout: Dropout <<<
>>> resid_mlp_dropout: Dropout <<<
<<<
>>> norm: Phi3RMSNorm <<<
>>> rotary_emb: Phi3RotaryEmbedding <<<
<<<
>>> lm_head: Linear <<<
<<<
The we try to export to see the submodule failing the whole model. We can pickle the failing model and restore it to speedup the refactoring to make it work.
print("----------------------")
ep = diag.try_export(
exporter="fx",
use_dynamic_shapes=True,
exporter_kwargs=dict(strict=False),
verbose=1,
)
----------------------
def forward(self, arg0_1: "f32[32064, 3072]", arg1_1: "f32[3072, 3072]", arg2_1: "f32[9216, 3072]", arg3_1: "f32[16384, 3072]", arg4_1: "f32[3072, 8192]", arg5_1: "f32[3072]", arg6_1: "f32[3072]", arg7_1: "f32[3072, 3072]", arg8_1: "f32[9216, 3072]", arg9_1: "f32[16384, 3072]", arg10_1: "f32[3072, 8192]", arg11_1: "f32[3072]", arg12_1: "f32[3072]", arg13_1: "f32[3072]", arg14_1: "f32[32064, 3072]", arg15_1: "f32[48]", arg16_1: "i64[s47, s2]", arg17_1: "i64[s47, s10]", arg18_1: "f32[s89, 32, s66, 96]", arg19_1: "f32[s14, 32, s80, 96]", arg20_1: "f32[s62, 32, s96, 96]", arg21_1: "f32[s58, 32, s81, 96]"):
# File: ~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/sparse.py:190 in forward, code: return F.embedding(
embedding: "f32[s47, s2, 3072]" = torch.ops.aten.embedding.default(arg0_1, arg16_1, 32000); arg0_1 = embedding = None
# File: ~/vv/this312/lib/python3.12/site-packages/torch/__init__.py:435 in __bool__, code: return builtins.bool(self != 0)
sym_numel_default: "Sym(3072*s66*s89)" = torch.ops.aten.sym_numel.default(arg18_1)
ne: "Sym(True)" = sym_numel_default != 0; ne = None
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:455 in forward, code: past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
sym_size_int: "Sym(s66)" = torch.ops.aten.sym_size.int(arg18_1, 2); arg18_1 = None
sym_size_int_1: "Sym(s2)" = torch.ops.aten.sym_size.int(arg16_1, 1)
add: "Sym(s2 + s66)" = sym_size_int + sym_size_int_1
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:454 in forward, code: cache_position = torch.arange(
arange: "i64[s2]" = torch.ops.aten.arange.start(sym_size_int, add, device = device(type='cpu'), pin_memory = False); add = None
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:459 in forward, code: position_ids = cache_position.unsqueeze(0)
unsqueeze: "i64[1, s2]" = torch.ops.aten.unsqueeze.default(arange, 0)
# File: ~/vv/this312/lib/python3.12/site-packages/torch/__init__.py:435 in __bool__, code: return builtins.bool(self != 0)
ne_1: "Sym(True)" = sym_numel_default != 0; sym_numel_default = ne_1 = None
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:461 in forward, code: causal_mask = self._update_causal_mask(
add_1: "Sym(s2 + s66)" = sym_size_int_1 + sym_size_int; sym_size_int = None
lt: "Sym(s2 + s66 < 262144)" = add_1 < 262144; add_1 = lt = None
sym_size_int_2: "Sym(s10)" = torch.ops.aten.sym_size.int(arg17_1, 1)
full: "f32[s2, s10]" = torch.ops.aten.full.default([sym_size_int_1, sym_size_int_2], -3.4028234663852886e+38, dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
arange_1: "i64[s10]" = torch.ops.aten.arange.default(sym_size_int_2, device = device(type='cpu'), pin_memory = False)
reshape: "i64[s2, 1]" = torch.ops.aten.reshape.default(arange, [-1, 1])
gt: "b8[s2, s10]" = torch.ops.aten.gt.Tensor(arange_1, reshape); arange_1 = reshape = None
arange_2: "i64[s10]" = torch.ops.aten.arange.default(sym_size_int_2, device = device(type='cpu'), pin_memory = False)
reshape_1: "i64[s2, 1]" = torch.ops.aten.reshape.default(arange, [-1, 1]); arange = None
sub: "i64[s2, 1]" = torch.ops.aten.sub.Tensor(reshape_1, 262144); reshape_1 = None
le: "b8[s2, s10]" = torch.ops.aten.le.Tensor(arange_2, sub); arange_2 = sub = None
bitwise_or_: "b8[s2, s10]" = torch.ops.aten.bitwise_or_.Tensor(gt, le); gt = le = None
mul_: "f32[s2, s10]" = torch.ops.aten.mul_.Tensor(full, bitwise_or_); full = bitwise_or_ = None
unsqueeze_1: "f32[1, s2, s10]" = torch.ops.aten.unsqueeze.default(mul_, 0); mul_ = None
unsqueeze_2: "f32[1, 1, s2, s10]" = torch.ops.aten.unsqueeze.default(unsqueeze_1, 1); unsqueeze_1 = None
eq: "Sym(Eq(s2, 9223372036854775807))" = sym_size_int_1 == 9223372036854775807; sym_size_int_1 = eq = None
slice_1: "f32[1, 1, s2, s10]" = torch.ops.aten.slice.Tensor(unsqueeze_2, 2, 0, 9223372036854775807); unsqueeze_2 = None
eq_1: "Sym(Eq(s10, 9223372036854775807))" = sym_size_int_2 == 9223372036854775807; eq_1 = None
slice_2: "f32[1, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_1, 3, 0, 9223372036854775807)
sym_size_int_3: "Sym(s47)" = torch.ops.aten.sym_size.int(arg16_1, 0); arg16_1 = None
expand: "f32[s47, 1, s2, s10]" = torch.ops.aten.expand.default(slice_2, [sym_size_int_3, 1, -1, -1])
clone: "f32[s47, 1, s2, s10]" = torch.ops.aten.clone.default(expand); expand = None
gt_1: "Sym(False)" = sym_size_int_2 > sym_size_int_2; gt_1 = None
slice_3: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(clone)
slice_4: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_3, 1)
slice_5: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_4, 2); slice_4 = None
slice_6: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_5, 3, None, sym_size_int_2)
sym_size_int_4: "Sym(s47)" = torch.ops.aten.sym_size.int(arg17_1, 0)
eq_2: "Sym(Eq(s47, 9223372036854775807))" = sym_size_int_4 == 9223372036854775807; sym_size_int_4 = eq_2 = None
slice_7: "i64[s47, s10]" = torch.ops.aten.slice.Tensor(arg17_1, 0, 0, 9223372036854775807); arg17_1 = None
unsqueeze_3: "i64[s47, 1, s10]" = torch.ops.aten.unsqueeze.default(slice_7, 1); slice_7 = None
unsqueeze_4: "i64[s47, 1, 1, s10]" = torch.ops.aten.unsqueeze.default(unsqueeze_3, 2); unsqueeze_3 = None
eq_3: "Sym(Eq(s10, 9223372036854775807))" = sym_size_int_2 == 9223372036854775807; eq_3 = None
slice_8: "i64[s47, 1, 1, s10]" = torch.ops.aten.slice.Tensor(unsqueeze_4, 3, 0, 9223372036854775807); unsqueeze_4 = None
to: "i64[s47, 1, 1, s10]" = torch.ops.aten.to.dtype_layout(slice_8, dtype = torch.int64, layout = torch.strided, device = device(type='cpu')); slice_8 = None
add_2: "f32[s47, 1, s2, s10]" = torch.ops.aten.add.Tensor(slice_6, to); to = None
eq_4: "b8[s47, 1, s2, s10]" = torch.ops.aten.eq.Scalar(add_2, 0); add_2 = None
slice_9: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(clone)
slice_10: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_9, 1); slice_9 = None
slice_11: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_10, 2); slice_10 = None
slice_12: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_11, 3, None, sym_size_int_2); slice_11 = None
masked_fill: "f32[s47, 1, s2, s10]" = torch.ops.aten.masked_fill.Scalar(slice_12, eq_4, -3.4028234663852886e+38); slice_12 = eq_4 = None
eq_5: "Sym(Eq(s47, 9223372036854775807))" = sym_size_int_3 == 9223372036854775807; sym_size_int_3 = eq_5 = None
slice_13: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(clone, 0, 0, 9223372036854775807); clone = None
slice_14: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_13, 1, 0, 9223372036854775807)
sym_size_int_5: "Sym(s2)" = torch.ops.aten.sym_size.int(slice_1, 2); slice_1 = None
eq_6: "Sym(Eq(s2, 9223372036854775807))" = sym_size_int_5 == 9223372036854775807; sym_size_int_5 = eq_6 = None
slice_15: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_14, 2, 0, 9223372036854775807); slice_14 = None
sym_size_int_6: "Sym(s10)" = torch.ops.aten.sym_size.int(slice_2, 3); slice_2 = None
eq_7: "Sym(True)" = sym_size_int_6 == sym_size_int_2; sym_size_int_2 = eq_7 = None
sym_size_int_7: "Sym(s47)" = torch.ops.aten.sym_size.int(slice_13, 0); slice_13 = None
sym_size_int_8: "Sym(s47)" = torch.ops.aten.sym_size.int(slice_3, 0); slice_3 = None
eq_8: "Sym(True)" = sym_size_int_7 == sym_size_int_8; sym_size_int_7 = sym_size_int_8 = eq_8 = None
sym_size_int_9: "Sym(s2)" = torch.ops.aten.sym_size.int(slice_15, 2)
sym_size_int_10: "Sym(s2)" = torch.ops.aten.sym_size.int(slice_5, 2); slice_5 = None
eq_9: "Sym(True)" = sym_size_int_9 == sym_size_int_10; sym_size_int_9 = sym_size_int_10 = eq_9 = None
sym_size_int_11: "Sym(s10)" = torch.ops.aten.sym_size.int(slice_6, 3); slice_6 = None
eq_10: "Sym(True)" = sym_size_int_6 == sym_size_int_11; sym_size_int_6 = sym_size_int_11 = eq_10 = None
copy_: "f32[s47, 1, s2, s10]" = torch.ops.aten.copy_.default(slice_15, masked_fill); slice_15 = masked_fill = copy_ = None
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:468 in forward, code: position_embeddings = self.rotary_emb(hidden_states, position_ids)
_set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None
max_1: "i64[]" = torch.ops.aten.max.default(unsqueeze); unsqueeze = None
add_3: "i64[]" = torch.ops.aten.add.Tensor(max_1, 1); max_1 = None
gt_2: "b8[]" = torch.ops.aten.gt.Scalar(add_3, 4096); add_3 = None
ne_2: "b8[]" = torch.ops.aten.ne.Scalar(gt_2, 0); gt_2 = None
item: "Sym(Eq(u0, 1))" = torch.ops.aten.item.default(ne_2); ne_2 = item = None
_set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None
def forward(self, arg0_1: "f32[32064, 3072]", arg1_1: "f32[3072, 3072]", arg2_1: "f32[9216, 3072]", arg3_1: "f32[16384, 3072]", arg4_1: "f32[3072, 8192]", arg5_1: "f32[3072]", arg6_1: "f32[3072]", arg7_1: "f32[3072, 3072]", arg8_1: "f32[9216, 3072]", arg9_1: "f32[16384, 3072]", arg10_1: "f32[3072, 8192]", arg11_1: "f32[3072]", arg12_1: "f32[3072]", arg13_1: "f32[3072]", arg14_1: "f32[32064, 3072]", arg15_1: "f32[48]", arg16_1: "i64[s47, s2]", arg17_1: "i64[s47, s10]", arg18_1: "f32[s89, 32, s66, 96]", arg19_1: "f32[s14, 32, s80, 96]", arg20_1: "f32[s62, 32, s96, 96]", arg21_1: "f32[s58, 32, s81, 96]"):
# File: ~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/sparse.py:190 in forward, code: return F.embedding(
embedding: "f32[s47, s2, 3072]" = torch.ops.aten.embedding.default(arg0_1, arg16_1, 32000); arg0_1 = embedding = None
# File: ~/vv/this312/lib/python3.12/site-packages/torch/__init__.py:435 in __bool__, code: return builtins.bool(self != 0)
sym_numel_default: "Sym(3072*s66*s89)" = torch.ops.aten.sym_numel.default(arg18_1)
ne: "Sym(True)" = sym_numel_default != 0; ne = None
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:455 in forward, code: past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
sym_size_int: "Sym(s66)" = torch.ops.aten.sym_size.int(arg18_1, 2); arg18_1 = None
sym_size_int_1: "Sym(s2)" = torch.ops.aten.sym_size.int(arg16_1, 1)
add: "Sym(s2 + s66)" = sym_size_int + sym_size_int_1
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:454 in forward, code: cache_position = torch.arange(
arange: "i64[s2]" = torch.ops.aten.arange.start(sym_size_int, add, device = device(type='cpu'), pin_memory = False); add = None
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:459 in forward, code: position_ids = cache_position.unsqueeze(0)
unsqueeze: "i64[1, s2]" = torch.ops.aten.unsqueeze.default(arange, 0)
# File: ~/vv/this312/lib/python3.12/site-packages/torch/__init__.py:435 in __bool__, code: return builtins.bool(self != 0)
ne_1: "Sym(True)" = sym_numel_default != 0; sym_numel_default = ne_1 = None
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:461 in forward, code: causal_mask = self._update_causal_mask(
add_1: "Sym(s2 + s66)" = sym_size_int_1 + sym_size_int; sym_size_int = None
lt: "Sym(s2 + s66 < 262144)" = add_1 < 262144; add_1 = lt = None
sym_size_int_2: "Sym(s10)" = torch.ops.aten.sym_size.int(arg17_1, 1)
full: "f32[s2, s10]" = torch.ops.aten.full.default([sym_size_int_1, sym_size_int_2], -3.4028234663852886e+38, dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
arange_1: "i64[s10]" = torch.ops.aten.arange.default(sym_size_int_2, device = device(type='cpu'), pin_memory = False)
reshape: "i64[s2, 1]" = torch.ops.aten.reshape.default(arange, [-1, 1])
gt: "b8[s2, s10]" = torch.ops.aten.gt.Tensor(arange_1, reshape); arange_1 = reshape = None
arange_2: "i64[s10]" = torch.ops.aten.arange.default(sym_size_int_2, device = device(type='cpu'), pin_memory = False)
reshape_1: "i64[s2, 1]" = torch.ops.aten.reshape.default(arange, [-1, 1]); arange = None
sub: "i64[s2, 1]" = torch.ops.aten.sub.Tensor(reshape_1, 262144); reshape_1 = None
le: "b8[s2, s10]" = torch.ops.aten.le.Tensor(arange_2, sub); arange_2 = sub = None
bitwise_or_: "b8[s2, s10]" = torch.ops.aten.bitwise_or_.Tensor(gt, le); gt = le = None
mul_: "f32[s2, s10]" = torch.ops.aten.mul_.Tensor(full, bitwise_or_); full = bitwise_or_ = None
unsqueeze_1: "f32[1, s2, s10]" = torch.ops.aten.unsqueeze.default(mul_, 0); mul_ = None
unsqueeze_2: "f32[1, 1, s2, s10]" = torch.ops.aten.unsqueeze.default(unsqueeze_1, 1); unsqueeze_1 = None
eq: "Sym(Eq(s2, 9223372036854775807))" = sym_size_int_1 == 9223372036854775807; sym_size_int_1 = eq = None
slice_1: "f32[1, 1, s2, s10]" = torch.ops.aten.slice.Tensor(unsqueeze_2, 2, 0, 9223372036854775807); unsqueeze_2 = None
eq_1: "Sym(Eq(s10, 9223372036854775807))" = sym_size_int_2 == 9223372036854775807; eq_1 = None
slice_2: "f32[1, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_1, 3, 0, 9223372036854775807)
sym_size_int_3: "Sym(s47)" = torch.ops.aten.sym_size.int(arg16_1, 0); arg16_1 = None
expand: "f32[s47, 1, s2, s10]" = torch.ops.aten.expand.default(slice_2, [sym_size_int_3, 1, -1, -1])
clone: "f32[s47, 1, s2, s10]" = torch.ops.aten.clone.default(expand); expand = None
gt_1: "Sym(False)" = sym_size_int_2 > sym_size_int_2; gt_1 = None
slice_3: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(clone)
slice_4: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_3, 1)
slice_5: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_4, 2); slice_4 = None
slice_6: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_5, 3, None, sym_size_int_2)
sym_size_int_4: "Sym(s47)" = torch.ops.aten.sym_size.int(arg17_1, 0)
eq_2: "Sym(Eq(s47, 9223372036854775807))" = sym_size_int_4 == 9223372036854775807; sym_size_int_4 = eq_2 = None
slice_7: "i64[s47, s10]" = torch.ops.aten.slice.Tensor(arg17_1, 0, 0, 9223372036854775807); arg17_1 = None
unsqueeze_3: "i64[s47, 1, s10]" = torch.ops.aten.unsqueeze.default(slice_7, 1); slice_7 = None
unsqueeze_4: "i64[s47, 1, 1, s10]" = torch.ops.aten.unsqueeze.default(unsqueeze_3, 2); unsqueeze_3 = None
eq_3: "Sym(Eq(s10, 9223372036854775807))" = sym_size_int_2 == 9223372036854775807; eq_3 = None
slice_8: "i64[s47, 1, 1, s10]" = torch.ops.aten.slice.Tensor(unsqueeze_4, 3, 0, 9223372036854775807); unsqueeze_4 = None
to: "i64[s47, 1, 1, s10]" = torch.ops.aten.to.dtype_layout(slice_8, dtype = torch.int64, layout = torch.strided, device = device(type='cpu')); slice_8 = None
add_2: "f32[s47, 1, s2, s10]" = torch.ops.aten.add.Tensor(slice_6, to); to = None
eq_4: "b8[s47, 1, s2, s10]" = torch.ops.aten.eq.Scalar(add_2, 0); add_2 = None
slice_9: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(clone)
slice_10: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_9, 1); slice_9 = None
slice_11: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_10, 2); slice_10 = None
slice_12: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_11, 3, None, sym_size_int_2); slice_11 = None
masked_fill: "f32[s47, 1, s2, s10]" = torch.ops.aten.masked_fill.Scalar(slice_12, eq_4, -3.4028234663852886e+38); slice_12 = eq_4 = None
eq_5: "Sym(Eq(s47, 9223372036854775807))" = sym_size_int_3 == 9223372036854775807; sym_size_int_3 = eq_5 = None
slice_13: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(clone, 0, 0, 9223372036854775807); clone = None
slice_14: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_13, 1, 0, 9223372036854775807)
sym_size_int_5: "Sym(s2)" = torch.ops.aten.sym_size.int(slice_1, 2); slice_1 = None
eq_6: "Sym(Eq(s2, 9223372036854775807))" = sym_size_int_5 == 9223372036854775807; sym_size_int_5 = eq_6 = None
slice_15: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_14, 2, 0, 9223372036854775807); slice_14 = None
sym_size_int_6: "Sym(s10)" = torch.ops.aten.sym_size.int(slice_2, 3); slice_2 = None
eq_7: "Sym(True)" = sym_size_int_6 == sym_size_int_2; sym_size_int_2 = eq_7 = None
sym_size_int_7: "Sym(s47)" = torch.ops.aten.sym_size.int(slice_13, 0); slice_13 = None
sym_size_int_8: "Sym(s47)" = torch.ops.aten.sym_size.int(slice_3, 0); slice_3 = None
eq_8: "Sym(True)" = sym_size_int_7 == sym_size_int_8; sym_size_int_7 = sym_size_int_8 = eq_8 = None
sym_size_int_9: "Sym(s2)" = torch.ops.aten.sym_size.int(slice_15, 2)
sym_size_int_10: "Sym(s2)" = torch.ops.aten.sym_size.int(slice_5, 2); slice_5 = None
eq_9: "Sym(True)" = sym_size_int_9 == sym_size_int_10; sym_size_int_9 = sym_size_int_10 = eq_9 = None
sym_size_int_11: "Sym(s10)" = torch.ops.aten.sym_size.int(slice_6, 3); slice_6 = None
eq_10: "Sym(True)" = sym_size_int_6 == sym_size_int_11; sym_size_int_6 = sym_size_int_11 = eq_10 = None
copy_: "f32[s47, 1, s2, s10]" = torch.ops.aten.copy_.default(slice_15, masked_fill); slice_15 = masked_fill = copy_ = None
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:468 in forward, code: position_embeddings = self.rotary_emb(hidden_states, position_ids)
_set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None
max_1: "i64[]" = torch.ops.aten.max.default(unsqueeze); unsqueeze = None
add_3: "i64[]" = torch.ops.aten.add.Tensor(max_1, 1); max_1 = None
gt_2: "b8[]" = torch.ops.aten.gt.Scalar(add_3, 4096); add_3 = None
ne_2: "b8[]" = torch.ops.aten.ne.Scalar(gt_2, 0); gt_2 = None
item: "Sym(Eq(u0, 1))" = torch.ops.aten.item.default(ne_2); ne_2 = item = None
_set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None
[try_export-FX] M:__main__-Phi3ForCausalLM --- FAIL, step=EXPORT, reason=Could not guard on data-dependent expression Eq(u0, 1) (unhinted: Eq(u0, 1)). (Size-like symbols: none) --- --- Caused by: (_export/non_strict_utils.py:973 in __torch_function__) --- For more information, run with TORCH_LOGS="dynamic" --- For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0" --- If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1 --- For more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing --- --- For C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1 --- --- The following call raised this error: --- File "~/github/transformers/src/transformers/modeling_rope_utils.py", line 50, in longrope_frequency_update --- if seq_len > original_max_position_embeddings: --- --- --- The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.['Traceback (most recent call last):\n', ' File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/piece_by_piece.py", line 1587, in _try_export_no_bypass_export\n ep = torch.export.export(\n ^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 319, in export\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 286, in export\n return _export(\n ^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1159, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1125, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 123, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2172, in _export\n ep = _export_for_training(\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1159, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1125, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 123, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2033, in _export_for_training\n export_artifact = export_func(\n ^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1975, in _non_strict_export\n aten_export_artifact = _to_aten_func( # type: ignore[operator]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1760, in _export_to_aten_ir_make_fx\n gm, graph_signature = transform(_make_fx_helper)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1901, in _aot_export_non_strict\n gm, sig = aot_export(wrapped_mod, args, kwargs=kwargs, **flags)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1679, in _make_fx_helper\n gm = make_fx(\n ^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 2290, in wrapped\n return make_fx_tracer.trace(f, *args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 2228, in trace\n return self._trace_inner(f, *args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 2199, in _trace_inner\n t = dispatch_trace(\n ^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_compile.py", line 51, in inner\n return disable_fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/eval_frame.py", line 893, in _fn\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1223, in dispatch_trace\n graph = tracer.trace(root, concrete_args) # type: ignore[arg-type]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1787, in trace\n res = super().trace(root, concrete_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/eval_frame.py", line 893, in _fn\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 850, in trace\n (self.create_arg(fn(*args)),),\n ^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1278, in wrapped\n out = f(*tensors) # type:ignore[call-arg]\n ^^^^^^^^^^^\n', ' File "<string>", line 1, in <lambda>\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1583, in wrapped_fn\n return tuple(flat_fn(*args))\n ^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/utils.py", line 184, in flat_fn\n tree_out = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/traced_function_transforms.py", line 906, in functional_call\n out = mod(*args[params_len:], **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 825, in module_call_wrapper\n return self.call_module(mod, forward, args, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1857, in call_module\n return Tracer.call_module(self, m, forward, args, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 542, in call_module\n ret_val = forward(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 818, in forward\n return _orig_module_call(mod, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1767, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1778, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1885, in forward\n tree_out = mod(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 825, in module_call_wrapper\n return self.call_module(mod, forward, args, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1857, in call_module\n return Tracer.call_module(self, m, forward, args, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 542, in call_module\n ret_val = forward(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 818, in forward\n return _orig_module_call(mod, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1767, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1778, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/github/transformers/src/transformers/utils/generic.py", line 969, in wrapper\n output = func(self, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/github/transformers/src/transformers/models/phi3/modeling_phi3.py", line 744, in forward\n outputs: BaseModelOutputWithPast = self.model(\n ^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 825, in module_call_wrapper\n return self.call_module(mod, forward, args, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1857, in call_module\n return Tracer.call_module(self, m, forward, args, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 542, in call_module\n ret_val = forward(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 818, in forward\n return _orig_module_call(mod, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1767, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1778, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/github/transformers/src/transformers/utils/generic.py", line 969, in wrapper\n output = func(self, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/github/transformers/src/transformers/models/phi3/modeling_phi3.py", line 468, in forward\n position_embeddings = self.rotary_emb(hidden_states, position_ids)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 825, in module_call_wrapper\n return self.call_module(mod, forward, args, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1857, in call_module\n return Tracer.call_module(self, m, forward, args, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 542, in call_module\n ret_val = forward(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 818, in forward\n return _orig_module_call(mod, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1767, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1778, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/github/transformers/src/transformers/modeling_rope_utils.py", line 86, in wrapper\n longrope_frequency_update(self, position_ids, device=x.device)\n', ' File "~/github/transformers/src/transformers/modeling_rope_utils.py", line 50, in longrope_frequency_update\n if seq_len > original_max_position_embeddings:\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1326, in __torch_function__\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1373, in __torch_function__\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_export/non_strict_utils.py", line 973, in __torch_function__\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/sym_node.py", line 536, in guard_bool\n r = self.evaluate()\n ^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/sym_node.py", line 510, in evaluate\n return self.shape_env.evaluate_sym_node(self, size_oblivious)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py", line 6857, in evaluate_sym_node\n return self.evaluate_expr(\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py", line 6876, in evaluate_expr\n return self._inner_evaluate_expr(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/recording.py", line 272, in wrapper\n return retlog(fn(*args, **kwargs))\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py", line 6892, in _inner_evaluate_expr\n return self._evaluate_expr(\n ^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py", line 7160, in _evaluate_expr\n raise self._make_data_dependent_error(\n', 'torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode: Could not guard on data-dependent expression Eq(u0, 1) (unhinted: Eq(u0, 1)). (Size-like symbols: none)\n\nCaused by: (_export/non_strict_utils.py:973 in __torch_function__)\nFor more information, run with TORCH_LOGS="dynamic"\nFor extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0"\nIf you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\nFor more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing\n\nFor C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\n\nThe following call raised this error:\n File "~/github/transformers/src/transformers/modeling_rope_utils.py", line 50, in longrope_frequency_update\n if seq_len > original_max_position_embeddings:\n\n\nThe error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.\n']
def forward(self, arg0_1: "f32[32064, 3072]", arg1_1: "f32[3072, 3072]", arg2_1: "f32[9216, 3072]", arg3_1: "f32[16384, 3072]", arg4_1: "f32[3072, 8192]", arg5_1: "f32[3072]", arg6_1: "f32[3072]", arg7_1: "f32[3072, 3072]", arg8_1: "f32[9216, 3072]", arg9_1: "f32[16384, 3072]", arg10_1: "f32[3072, 8192]", arg11_1: "f32[3072]", arg12_1: "f32[3072]", arg13_1: "f32[3072]", arg14_1: "f32[48]", arg15_1: "i64[s47, s2]", arg16_1: "i64[s47, s10]", arg17_1, arg18_1: "f32[s89, 32, s66, 96]", arg19_1: "f32[s14, 32, s80, 96]", arg20_1: "f32[s62, 32, s96, 96]", arg21_1: "f32[s58, 32, s81, 96]", arg22_1, arg23_1, arg24_1, arg25_1, arg26_1):
# File: ~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/sparse.py:190 in forward, code: return F.embedding(
embedding: "f32[s47, s2, 3072]" = torch.ops.aten.embedding.default(arg0_1, arg15_1, 32000); arg0_1 = embedding = None
# File: ~/vv/this312/lib/python3.12/site-packages/torch/__init__.py:435 in __bool__, code: return builtins.bool(self != 0)
sym_numel_default: "Sym(3072*s66*s89)" = torch.ops.aten.sym_numel.default(arg18_1)
ne: "Sym(True)" = sym_numel_default != 0; ne = None
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:455 in forward, code: past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
sym_size_int: "Sym(s66)" = torch.ops.aten.sym_size.int(arg18_1, 2); arg18_1 = None
sym_size_int_1: "Sym(s2)" = torch.ops.aten.sym_size.int(arg15_1, 1)
add: "Sym(s2 + s66)" = sym_size_int + sym_size_int_1
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:454 in forward, code: cache_position = torch.arange(
arange: "i64[s2]" = torch.ops.aten.arange.start(sym_size_int, add, device = device(type='cpu'), pin_memory = False); add = None
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:459 in forward, code: position_ids = cache_position.unsqueeze(0)
unsqueeze: "i64[1, s2]" = torch.ops.aten.unsqueeze.default(arange, 0)
# File: ~/vv/this312/lib/python3.12/site-packages/torch/__init__.py:435 in __bool__, code: return builtins.bool(self != 0)
ne_1: "Sym(True)" = sym_numel_default != 0; sym_numel_default = ne_1 = None
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:461 in forward, code: causal_mask = self._update_causal_mask(
add_1: "Sym(s2 + s66)" = sym_size_int_1 + sym_size_int; sym_size_int = None
lt: "Sym(s2 + s66 < 262144)" = add_1 < 262144; add_1 = lt = None
sym_size_int_2: "Sym(s10)" = torch.ops.aten.sym_size.int(arg16_1, 1)
full: "f32[s2, s10]" = torch.ops.aten.full.default([sym_size_int_1, sym_size_int_2], -3.4028234663852886e+38, dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
arange_1: "i64[s10]" = torch.ops.aten.arange.default(sym_size_int_2, device = device(type='cpu'), pin_memory = False)
reshape: "i64[s2, 1]" = torch.ops.aten.reshape.default(arange, [-1, 1])
gt: "b8[s2, s10]" = torch.ops.aten.gt.Tensor(arange_1, reshape); arange_1 = reshape = None
arange_2: "i64[s10]" = torch.ops.aten.arange.default(sym_size_int_2, device = device(type='cpu'), pin_memory = False)
reshape_1: "i64[s2, 1]" = torch.ops.aten.reshape.default(arange, [-1, 1]); arange = None
sub: "i64[s2, 1]" = torch.ops.aten.sub.Tensor(reshape_1, 262144); reshape_1 = None
le: "b8[s2, s10]" = torch.ops.aten.le.Tensor(arange_2, sub); arange_2 = sub = None
bitwise_or_: "b8[s2, s10]" = torch.ops.aten.bitwise_or_.Tensor(gt, le); gt = le = None
mul_: "f32[s2, s10]" = torch.ops.aten.mul_.Tensor(full, bitwise_or_); full = bitwise_or_ = None
unsqueeze_1: "f32[1, s2, s10]" = torch.ops.aten.unsqueeze.default(mul_, 0); mul_ = None
unsqueeze_2: "f32[1, 1, s2, s10]" = torch.ops.aten.unsqueeze.default(unsqueeze_1, 1); unsqueeze_1 = None
eq: "Sym(Eq(s2, 9223372036854775807))" = sym_size_int_1 == 9223372036854775807; sym_size_int_1 = eq = None
slice_1: "f32[1, 1, s2, s10]" = torch.ops.aten.slice.Tensor(unsqueeze_2, 2, 0, 9223372036854775807); unsqueeze_2 = None
eq_1: "Sym(Eq(s10, 9223372036854775807))" = sym_size_int_2 == 9223372036854775807; eq_1 = None
slice_2: "f32[1, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_1, 3, 0, 9223372036854775807)
sym_size_int_3: "Sym(s47)" = torch.ops.aten.sym_size.int(arg15_1, 0); arg15_1 = None
expand: "f32[s47, 1, s2, s10]" = torch.ops.aten.expand.default(slice_2, [sym_size_int_3, 1, -1, -1])
clone: "f32[s47, 1, s2, s10]" = torch.ops.aten.clone.default(expand); expand = None
gt_1: "Sym(False)" = sym_size_int_2 > sym_size_int_2; gt_1 = None
slice_3: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(clone)
slice_4: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_3, 1)
slice_5: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_4, 2); slice_4 = None
slice_6: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_5, 3, None, sym_size_int_2)
sym_size_int_4: "Sym(s47)" = torch.ops.aten.sym_size.int(arg16_1, 0)
eq_2: "Sym(Eq(s47, 9223372036854775807))" = sym_size_int_4 == 9223372036854775807; sym_size_int_4 = eq_2 = None
slice_7: "i64[s47, s10]" = torch.ops.aten.slice.Tensor(arg16_1, 0, 0, 9223372036854775807); arg16_1 = None
unsqueeze_3: "i64[s47, 1, s10]" = torch.ops.aten.unsqueeze.default(slice_7, 1); slice_7 = None
unsqueeze_4: "i64[s47, 1, 1, s10]" = torch.ops.aten.unsqueeze.default(unsqueeze_3, 2); unsqueeze_3 = None
eq_3: "Sym(Eq(s10, 9223372036854775807))" = sym_size_int_2 == 9223372036854775807; eq_3 = None
slice_8: "i64[s47, 1, 1, s10]" = torch.ops.aten.slice.Tensor(unsqueeze_4, 3, 0, 9223372036854775807); unsqueeze_4 = None
to: "i64[s47, 1, 1, s10]" = torch.ops.aten.to.dtype_layout(slice_8, dtype = torch.int64, layout = torch.strided, device = device(type='cpu')); slice_8 = None
add_2: "f32[s47, 1, s2, s10]" = torch.ops.aten.add.Tensor(slice_6, to); to = None
eq_4: "b8[s47, 1, s2, s10]" = torch.ops.aten.eq.Scalar(add_2, 0); add_2 = None
slice_9: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(clone)
slice_10: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_9, 1); slice_9 = None
slice_11: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_10, 2); slice_10 = None
slice_12: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_11, 3, None, sym_size_int_2); slice_11 = None
masked_fill: "f32[s47, 1, s2, s10]" = torch.ops.aten.masked_fill.Scalar(slice_12, eq_4, -3.4028234663852886e+38); slice_12 = eq_4 = None
eq_5: "Sym(Eq(s47, 9223372036854775807))" = sym_size_int_3 == 9223372036854775807; sym_size_int_3 = eq_5 = None
slice_13: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(clone, 0, 0, 9223372036854775807); clone = None
slice_14: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_13, 1, 0, 9223372036854775807)
sym_size_int_5: "Sym(s2)" = torch.ops.aten.sym_size.int(slice_1, 2); slice_1 = None
eq_6: "Sym(Eq(s2, 9223372036854775807))" = sym_size_int_5 == 9223372036854775807; sym_size_int_5 = eq_6 = None
slice_15: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_14, 2, 0, 9223372036854775807); slice_14 = None
sym_size_int_6: "Sym(s10)" = torch.ops.aten.sym_size.int(slice_2, 3); slice_2 = None
eq_7: "Sym(True)" = sym_size_int_6 == sym_size_int_2; sym_size_int_2 = eq_7 = None
sym_size_int_7: "Sym(s47)" = torch.ops.aten.sym_size.int(slice_13, 0); slice_13 = None
sym_size_int_8: "Sym(s47)" = torch.ops.aten.sym_size.int(slice_3, 0); slice_3 = None
eq_8: "Sym(True)" = sym_size_int_7 == sym_size_int_8; sym_size_int_7 = sym_size_int_8 = eq_8 = None
sym_size_int_9: "Sym(s2)" = torch.ops.aten.sym_size.int(slice_15, 2)
sym_size_int_10: "Sym(s2)" = torch.ops.aten.sym_size.int(slice_5, 2); slice_5 = None
eq_9: "Sym(True)" = sym_size_int_9 == sym_size_int_10; sym_size_int_9 = sym_size_int_10 = eq_9 = None
sym_size_int_11: "Sym(s10)" = torch.ops.aten.sym_size.int(slice_6, 3); slice_6 = None
eq_10: "Sym(True)" = sym_size_int_6 == sym_size_int_11; sym_size_int_6 = sym_size_int_11 = eq_10 = None
copy_: "f32[s47, 1, s2, s10]" = torch.ops.aten.copy_.default(slice_15, masked_fill); slice_15 = masked_fill = copy_ = None
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:468 in forward, code: position_embeddings = self.rotary_emb(hidden_states, position_ids)
_set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None
max_1: "i64[]" = torch.ops.aten.max.default(unsqueeze); unsqueeze = None
add_3: "i64[]" = torch.ops.aten.add.Tensor(max_1, 1); max_1 = None
gt_2: "b8[]" = torch.ops.aten.gt.Scalar(add_3, 4096); add_3 = None
ne_2: "b8[]" = torch.ops.aten.ne.Scalar(gt_2, 0); gt_2 = None
item: "Sym(Eq(u0, 1))" = torch.ops.aten.item.default(ne_2); ne_2 = item = None
_set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None
def forward(self, arg0_1: "f32[32064, 3072]", arg1_1: "f32[3072, 3072]", arg2_1: "f32[9216, 3072]", arg3_1: "f32[16384, 3072]", arg4_1: "f32[3072, 8192]", arg5_1: "f32[3072]", arg6_1: "f32[3072]", arg7_1: "f32[3072, 3072]", arg8_1: "f32[9216, 3072]", arg9_1: "f32[16384, 3072]", arg10_1: "f32[3072, 8192]", arg11_1: "f32[3072]", arg12_1: "f32[3072]", arg13_1: "f32[3072]", arg14_1: "f32[48]", arg15_1: "i64[s47, s2]", arg16_1: "i64[s47, s10]", arg17_1, arg18_1: "f32[s89, 32, s66, 96]", arg19_1: "f32[s14, 32, s80, 96]", arg20_1: "f32[s62, 32, s96, 96]", arg21_1: "f32[s58, 32, s81, 96]", arg22_1, arg23_1, arg24_1, arg25_1, arg26_1):
# File: ~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/sparse.py:190 in forward, code: return F.embedding(
embedding: "f32[s47, s2, 3072]" = torch.ops.aten.embedding.default(arg0_1, arg15_1, 32000); arg0_1 = embedding = None
# File: ~/vv/this312/lib/python3.12/site-packages/torch/__init__.py:435 in __bool__, code: return builtins.bool(self != 0)
sym_numel_default: "Sym(3072*s66*s89)" = torch.ops.aten.sym_numel.default(arg18_1)
ne: "Sym(True)" = sym_numel_default != 0; ne = None
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:455 in forward, code: past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
sym_size_int: "Sym(s66)" = torch.ops.aten.sym_size.int(arg18_1, 2); arg18_1 = None
sym_size_int_1: "Sym(s2)" = torch.ops.aten.sym_size.int(arg15_1, 1)
add: "Sym(s2 + s66)" = sym_size_int + sym_size_int_1
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:454 in forward, code: cache_position = torch.arange(
arange: "i64[s2]" = torch.ops.aten.arange.start(sym_size_int, add, device = device(type='cpu'), pin_memory = False); add = None
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:459 in forward, code: position_ids = cache_position.unsqueeze(0)
unsqueeze: "i64[1, s2]" = torch.ops.aten.unsqueeze.default(arange, 0)
# File: ~/vv/this312/lib/python3.12/site-packages/torch/__init__.py:435 in __bool__, code: return builtins.bool(self != 0)
ne_1: "Sym(True)" = sym_numel_default != 0; sym_numel_default = ne_1 = None
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:461 in forward, code: causal_mask = self._update_causal_mask(
add_1: "Sym(s2 + s66)" = sym_size_int_1 + sym_size_int; sym_size_int = None
lt: "Sym(s2 + s66 < 262144)" = add_1 < 262144; add_1 = lt = None
sym_size_int_2: "Sym(s10)" = torch.ops.aten.sym_size.int(arg16_1, 1)
full: "f32[s2, s10]" = torch.ops.aten.full.default([sym_size_int_1, sym_size_int_2], -3.4028234663852886e+38, dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
arange_1: "i64[s10]" = torch.ops.aten.arange.default(sym_size_int_2, device = device(type='cpu'), pin_memory = False)
reshape: "i64[s2, 1]" = torch.ops.aten.reshape.default(arange, [-1, 1])
gt: "b8[s2, s10]" = torch.ops.aten.gt.Tensor(arange_1, reshape); arange_1 = reshape = None
arange_2: "i64[s10]" = torch.ops.aten.arange.default(sym_size_int_2, device = device(type='cpu'), pin_memory = False)
reshape_1: "i64[s2, 1]" = torch.ops.aten.reshape.default(arange, [-1, 1]); arange = None
sub: "i64[s2, 1]" = torch.ops.aten.sub.Tensor(reshape_1, 262144); reshape_1 = None
le: "b8[s2, s10]" = torch.ops.aten.le.Tensor(arange_2, sub); arange_2 = sub = None
bitwise_or_: "b8[s2, s10]" = torch.ops.aten.bitwise_or_.Tensor(gt, le); gt = le = None
mul_: "f32[s2, s10]" = torch.ops.aten.mul_.Tensor(full, bitwise_or_); full = bitwise_or_ = None
unsqueeze_1: "f32[1, s2, s10]" = torch.ops.aten.unsqueeze.default(mul_, 0); mul_ = None
unsqueeze_2: "f32[1, 1, s2, s10]" = torch.ops.aten.unsqueeze.default(unsqueeze_1, 1); unsqueeze_1 = None
eq: "Sym(Eq(s2, 9223372036854775807))" = sym_size_int_1 == 9223372036854775807; sym_size_int_1 = eq = None
slice_1: "f32[1, 1, s2, s10]" = torch.ops.aten.slice.Tensor(unsqueeze_2, 2, 0, 9223372036854775807); unsqueeze_2 = None
eq_1: "Sym(Eq(s10, 9223372036854775807))" = sym_size_int_2 == 9223372036854775807; eq_1 = None
slice_2: "f32[1, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_1, 3, 0, 9223372036854775807)
sym_size_int_3: "Sym(s47)" = torch.ops.aten.sym_size.int(arg15_1, 0); arg15_1 = None
expand: "f32[s47, 1, s2, s10]" = torch.ops.aten.expand.default(slice_2, [sym_size_int_3, 1, -1, -1])
clone: "f32[s47, 1, s2, s10]" = torch.ops.aten.clone.default(expand); expand = None
gt_1: "Sym(False)" = sym_size_int_2 > sym_size_int_2; gt_1 = None
slice_3: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(clone)
slice_4: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_3, 1)
slice_5: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_4, 2); slice_4 = None
slice_6: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_5, 3, None, sym_size_int_2)
sym_size_int_4: "Sym(s47)" = torch.ops.aten.sym_size.int(arg16_1, 0)
eq_2: "Sym(Eq(s47, 9223372036854775807))" = sym_size_int_4 == 9223372036854775807; sym_size_int_4 = eq_2 = None
slice_7: "i64[s47, s10]" = torch.ops.aten.slice.Tensor(arg16_1, 0, 0, 9223372036854775807); arg16_1 = None
unsqueeze_3: "i64[s47, 1, s10]" = torch.ops.aten.unsqueeze.default(slice_7, 1); slice_7 = None
unsqueeze_4: "i64[s47, 1, 1, s10]" = torch.ops.aten.unsqueeze.default(unsqueeze_3, 2); unsqueeze_3 = None
eq_3: "Sym(Eq(s10, 9223372036854775807))" = sym_size_int_2 == 9223372036854775807; eq_3 = None
slice_8: "i64[s47, 1, 1, s10]" = torch.ops.aten.slice.Tensor(unsqueeze_4, 3, 0, 9223372036854775807); unsqueeze_4 = None
to: "i64[s47, 1, 1, s10]" = torch.ops.aten.to.dtype_layout(slice_8, dtype = torch.int64, layout = torch.strided, device = device(type='cpu')); slice_8 = None
add_2: "f32[s47, 1, s2, s10]" = torch.ops.aten.add.Tensor(slice_6, to); to = None
eq_4: "b8[s47, 1, s2, s10]" = torch.ops.aten.eq.Scalar(add_2, 0); add_2 = None
slice_9: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(clone)
slice_10: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_9, 1); slice_9 = None
slice_11: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_10, 2); slice_10 = None
slice_12: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_11, 3, None, sym_size_int_2); slice_11 = None
masked_fill: "f32[s47, 1, s2, s10]" = torch.ops.aten.masked_fill.Scalar(slice_12, eq_4, -3.4028234663852886e+38); slice_12 = eq_4 = None
eq_5: "Sym(Eq(s47, 9223372036854775807))" = sym_size_int_3 == 9223372036854775807; sym_size_int_3 = eq_5 = None
slice_13: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(clone, 0, 0, 9223372036854775807); clone = None
slice_14: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_13, 1, 0, 9223372036854775807)
sym_size_int_5: "Sym(s2)" = torch.ops.aten.sym_size.int(slice_1, 2); slice_1 = None
eq_6: "Sym(Eq(s2, 9223372036854775807))" = sym_size_int_5 == 9223372036854775807; sym_size_int_5 = eq_6 = None
slice_15: "f32[s47, 1, s2, s10]" = torch.ops.aten.slice.Tensor(slice_14, 2, 0, 9223372036854775807); slice_14 = None
sym_size_int_6: "Sym(s10)" = torch.ops.aten.sym_size.int(slice_2, 3); slice_2 = None
eq_7: "Sym(True)" = sym_size_int_6 == sym_size_int_2; sym_size_int_2 = eq_7 = None
sym_size_int_7: "Sym(s47)" = torch.ops.aten.sym_size.int(slice_13, 0); slice_13 = None
sym_size_int_8: "Sym(s47)" = torch.ops.aten.sym_size.int(slice_3, 0); slice_3 = None
eq_8: "Sym(True)" = sym_size_int_7 == sym_size_int_8; sym_size_int_7 = sym_size_int_8 = eq_8 = None
sym_size_int_9: "Sym(s2)" = torch.ops.aten.sym_size.int(slice_15, 2)
sym_size_int_10: "Sym(s2)" = torch.ops.aten.sym_size.int(slice_5, 2); slice_5 = None
eq_9: "Sym(True)" = sym_size_int_9 == sym_size_int_10; sym_size_int_9 = sym_size_int_10 = eq_9 = None
sym_size_int_11: "Sym(s10)" = torch.ops.aten.sym_size.int(slice_6, 3); slice_6 = None
eq_10: "Sym(True)" = sym_size_int_6 == sym_size_int_11; sym_size_int_6 = sym_size_int_11 = eq_10 = None
copy_: "f32[s47, 1, s2, s10]" = torch.ops.aten.copy_.default(slice_15, masked_fill); slice_15 = masked_fill = copy_ = None
# File: ~/github/transformers/src/transformers/models/phi3/modeling_phi3.py:468 in forward, code: position_embeddings = self.rotary_emb(hidden_states, position_ids)
_set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None
max_1: "i64[]" = torch.ops.aten.max.default(unsqueeze); unsqueeze = None
add_3: "i64[]" = torch.ops.aten.add.Tensor(max_1, 1); max_1 = None
gt_2: "b8[]" = torch.ops.aten.gt.Scalar(add_3, 4096); add_3 = None
ne_2: "b8[]" = torch.ops.aten.ne.Scalar(gt_2, 0); gt_2 = None
item: "Sym(Eq(u0, 1))" = torch.ops.aten.item.default(ne_2); ne_2 = item = None
_set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None
[try_export-FX] .. M:model-Phi3Model --- FAIL, step=EXPORT, reason=Could not guard on data-dependent expression Eq(u0, 1) (unhinted: Eq(u0, 1)). (Size-like symbols: none) --- --- Caused by: (_export/non_strict_utils.py:973 in __torch_function__) --- For more information, run with TORCH_LOGS="dynamic" --- For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0" --- If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1 --- For more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing --- --- For C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1 --- --- The following call raised this error: --- File "~/github/transformers/src/transformers/modeling_rope_utils.py", line 50, in longrope_frequency_update --- if seq_len > original_max_position_embeddings: --- --- --- The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.['Traceback (most recent call last):\n', ' File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/piece_by_piece.py", line 1587, in _try_export_no_bypass_export\n ep = torch.export.export(\n ^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 319, in export\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 286, in export\n return _export(\n ^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1159, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1125, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 123, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2172, in _export\n ep = _export_for_training(\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1159, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1125, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 123, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2033, in _export_for_training\n export_artifact = export_func(\n ^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1975, in _non_strict_export\n aten_export_artifact = _to_aten_func( # type: ignore[operator]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1760, in _export_to_aten_ir_make_fx\n gm, graph_signature = transform(_make_fx_helper)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1901, in _aot_export_non_strict\n gm, sig = aot_export(wrapped_mod, args, kwargs=kwargs, **flags)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1679, in _make_fx_helper\n gm = make_fx(\n ^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 2290, in wrapped\n return make_fx_tracer.trace(f, *args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 2228, in trace\n return self._trace_inner(f, *args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 2199, in _trace_inner\n t = dispatch_trace(\n ^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_compile.py", line 51, in inner\n return disable_fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/eval_frame.py", line 893, in _fn\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1223, in dispatch_trace\n graph = tracer.trace(root, concrete_args) # type: ignore[arg-type]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1787, in trace\n res = super().trace(root, concrete_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/eval_frame.py", line 893, in _fn\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 850, in trace\n (self.create_arg(fn(*args)),),\n ^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1278, in wrapped\n out = f(*tensors) # type:ignore[call-arg]\n ^^^^^^^^^^^\n', ' File "<string>", line 1, in <lambda>\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1583, in wrapped_fn\n return tuple(flat_fn(*args))\n ^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/utils.py", line 184, in flat_fn\n tree_out = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/traced_function_transforms.py", line 906, in functional_call\n out = mod(*args[params_len:], **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 825, in module_call_wrapper\n return self.call_module(mod, forward, args, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1857, in call_module\n return Tracer.call_module(self, m, forward, args, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 542, in call_module\n ret_val = forward(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 818, in forward\n return _orig_module_call(mod, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1767, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1778, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1885, in forward\n tree_out = mod(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 825, in module_call_wrapper\n return self.call_module(mod, forward, args, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1857, in call_module\n return Tracer.call_module(self, m, forward, args, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 542, in call_module\n ret_val = forward(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 818, in forward\n return _orig_module_call(mod, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1767, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1778, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/github/transformers/src/transformers/utils/generic.py", line 969, in wrapper\n output = func(self, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/github/transformers/src/transformers/models/phi3/modeling_phi3.py", line 468, in forward\n position_embeddings = self.rotary_emb(hidden_states, position_ids)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 825, in module_call_wrapper\n return self.call_module(mod, forward, args, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1857, in call_module\n return Tracer.call_module(self, m, forward, args, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 542, in call_module\n ret_val = forward(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 818, in forward\n return _orig_module_call(mod, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1767, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1778, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/github/transformers/src/transformers/modeling_rope_utils.py", line 86, in wrapper\n longrope_frequency_update(self, position_ids, device=x.device)\n', ' File "~/github/transformers/src/transformers/modeling_rope_utils.py", line 50, in longrope_frequency_update\n if seq_len > original_max_position_embeddings:\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1326, in __torch_function__\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1373, in __torch_function__\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_export/non_strict_utils.py", line 973, in __torch_function__\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/sym_node.py", line 536, in guard_bool\n r = self.evaluate()\n ^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/sym_node.py", line 510, in evaluate\n return self.shape_env.evaluate_sym_node(self, size_oblivious)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py", line 6857, in evaluate_sym_node\n return self.evaluate_expr(\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py", line 6876, in evaluate_expr\n return self._inner_evaluate_expr(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/recording.py", line 272, in wrapper\n return retlog(fn(*args, **kwargs))\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py", line 6892, in _inner_evaluate_expr\n return self._evaluate_expr(\n ^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py", line 7160, in _evaluate_expr\n raise self._make_data_dependent_error(\n', 'torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode: Could not guard on data-dependent expression Eq(u0, 1) (unhinted: Eq(u0, 1)). (Size-like symbols: none)\n\nCaused by: (_export/non_strict_utils.py:973 in __torch_function__)\nFor more information, run with TORCH_LOGS="dynamic"\nFor extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0"\nIf you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\nFor more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing\n\nFor C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\n\nThe following call raised this error:\n File "~/github/transformers/src/transformers/modeling_rope_utils.py", line 50, in longrope_frequency_update\n if seq_len > original_max_position_embeddings:\n\n\nThe error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.\n']
~/github/onnx-diagnostic/onnx_diagnostic/helpers/helper.py:1285: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.
Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)
float(diff.max()),
[try_export-FX] .... M:embed_tokens-Embedding --- OK:
[try_export-FX] .... M:layers[0]-Phi3DecoderLayer --- OK:
[try_export-FX] .... M:layers[1]-Phi3DecoderLayer --- OK:
[try_export-FX] .... M:norm-Phi3RMSNorm --- OK:
def forward(self, arg0_1: "f32[48]", arg1_1: "f32[s35, s16, 3072]", arg2_1: "i64[1, s43]"):
# No stacktrace found for following nodes
_set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None
max_1: "i64[]" = torch.ops.aten.max.default(arg2_1); arg2_1 = None
add: "i64[]" = torch.ops.aten.add.Tensor(max_1, 1); max_1 = None
gt: "b8[]" = torch.ops.aten.gt.Scalar(add, 4096); add = None
ne: "b8[]" = torch.ops.aten.ne.Scalar(gt, 0); gt = None
item: "Sym(Eq(u0, 1))" = torch.ops.aten.item.default(ne); ne = item = None
_set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None
def forward(self, arg0_1: "f32[48]", arg1_1: "f32[s35, s16, 3072]", arg2_1: "i64[1, s43]"):
# No stacktrace found for following nodes
_set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None
max_1: "i64[]" = torch.ops.aten.max.default(arg2_1); arg2_1 = None
add: "i64[]" = torch.ops.aten.add.Tensor(max_1, 1); max_1 = None
gt: "b8[]" = torch.ops.aten.gt.Scalar(add, 4096); add = None
ne: "b8[]" = torch.ops.aten.ne.Scalar(gt, 0); gt = None
item: "Sym(Eq(u0, 1))" = torch.ops.aten.item.default(ne); ne = item = None
_set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None
[try_export-FX] .... M:rotary_emb-Phi3RotaryEmbedding --- FAIL, step=EXPORT, reason=Could not guard on data-dependent expression Eq(u0, 1) (unhinted: Eq(u0, 1)). (Size-like symbols: none) --- --- Caused by: (_export/non_strict_utils.py:973 in __torch_function__) --- For more information, run with TORCH_LOGS="dynamic" --- For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0" --- If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1 --- For more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing --- --- For C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1 --- --- The following call raised this error: --- File "~/github/transformers/src/transformers/modeling_rope_utils.py", line 50, in longrope_frequency_update --- if seq_len > original_max_position_embeddings: --- --- --- The error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.['Traceback (most recent call last):\n', ' File "~/github/experimental-experiment/experimental_experiment/torch_interpreter/piece_by_piece.py", line 1587, in _try_export_no_bypass_export\n ep = torch.export.export(\n ^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 319, in export\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 286, in export\n return _export(\n ^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1159, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1125, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 123, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2172, in _export\n ep = _export_for_training(\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1159, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1125, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 123, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2033, in _export_for_training\n export_artifact = export_func(\n ^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1975, in _non_strict_export\n aten_export_artifact = _to_aten_func( # type: ignore[operator]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1760, in _export_to_aten_ir_make_fx\n gm, graph_signature = transform(_make_fx_helper)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1901, in _aot_export_non_strict\n gm, sig = aot_export(wrapped_mod, args, kwargs=kwargs, **flags)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1679, in _make_fx_helper\n gm = make_fx(\n ^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 2290, in wrapped\n return make_fx_tracer.trace(f, *args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 2228, in trace\n return self._trace_inner(f, *args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 2199, in _trace_inner\n t = dispatch_trace(\n ^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_compile.py", line 51, in inner\n return disable_fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/eval_frame.py", line 893, in _fn\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1223, in dispatch_trace\n graph = tracer.trace(root, concrete_args) # type: ignore[arg-type]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1787, in trace\n res = super().trace(root, concrete_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/eval_frame.py", line 893, in _fn\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 850, in trace\n (self.create_arg(fn(*args)),),\n ^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1278, in wrapped\n out = f(*tensors) # type:ignore[call-arg]\n ^^^^^^^^^^^\n', ' File "<string>", line 1, in <lambda>\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1583, in wrapped_fn\n return tuple(flat_fn(*args))\n ^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/utils.py", line 184, in flat_fn\n tree_out = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/traced_function_transforms.py", line 906, in functional_call\n out = mod(*args[params_len:], **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 825, in module_call_wrapper\n return self.call_module(mod, forward, args, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1857, in call_module\n return Tracer.call_module(self, m, forward, args, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 542, in call_module\n ret_val = forward(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 818, in forward\n return _orig_module_call(mod, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1767, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1778, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1885, in forward\n tree_out = mod(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 825, in module_call_wrapper\n return self.call_module(mod, forward, args, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1857, in call_module\n return Tracer.call_module(self, m, forward, args, kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 542, in call_module\n ret_val = forward(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 818, in forward\n return _orig_module_call(mod, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1767, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1778, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/github/transformers/src/transformers/modeling_rope_utils.py", line 86, in wrapper\n longrope_frequency_update(self, position_ids, device=x.device)\n', ' File "~/github/transformers/src/transformers/modeling_rope_utils.py", line 50, in longrope_frequency_update\n if seq_len > original_max_position_embeddings:\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1326, in __torch_function__\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1373, in __torch_function__\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_export/non_strict_utils.py", line 973, in __torch_function__\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/sym_node.py", line 536, in guard_bool\n r = self.evaluate()\n ^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/sym_node.py", line 510, in evaluate\n return self.shape_env.evaluate_sym_node(self, size_oblivious)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py", line 6857, in evaluate_sym_node\n return self.evaluate_expr(\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py", line 6876, in evaluate_expr\n return self._inner_evaluate_expr(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/recording.py", line 272, in wrapper\n return retlog(fn(*args, **kwargs))\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py", line 6892, in _inner_evaluate_expr\n return self._evaluate_expr(\n ^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/symbolic_shapes.py", line 7160, in _evaluate_expr\n raise self._make_data_dependent_error(\n', 'torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode: Could not guard on data-dependent expression Eq(u0, 1) (unhinted: Eq(u0, 1)). (Size-like symbols: none)\n\nCaused by: (_export/non_strict_utils.py:973 in __torch_function__)\nFor more information, run with TORCH_LOGS="dynamic"\nFor extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0"\nIf you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\nFor more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing\n\nFor C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\n\nThe following call raised this error:\n File "~/github/transformers/src/transformers/modeling_rope_utils.py", line 50, in longrope_frequency_update\n if seq_len > original_max_position_embeddings:\n\n\nThe error above occurred when calling torch.export.export. If you would like to view some more information about this error, and get a list of all other errors that may occur in your export call, you can replace your `export()` call with `draft_export()`.\n']
[try_export-FX] .... M:rotary_emb-Phi3RotaryEmbedding --- FAIL: Could not guard on data-depend...
[try_export-FX] .. M:lm_head-Linear --- OK:
Let’s display a report.
print(f"success: {ep.status}")
print(diag.get_export_report())
success: 2
__main__ Phi3ForCausalLM FAIL -- step=EXPORT, reason='Could not guard on data-dependent expression Eq(u0, 1) (unhinted: Eq(u0, 1)). (Size-like symbols: n...'
..model Phi3Model FAIL -- step=EXPORT, reason='Could not guard on data-dependent expression Eq(u0, 1) (unhinted: Eq(u0, 1)). (Size-like symbols: n...'
....embed_tokens Embedding OK -- ExportedProgram
....layers[0] Phi3DecoderLayer OK -- ExportedProgram
......self_attn Phi3Attention <OK-2i-0>
........o_proj Linear <OK-2i-0>
........qkv_proj Linear <OK-2i-0>
......mlp Phi3MLP <OK-2i-0>
........gate_up_proj Linear <OK-2i-0>
........down_proj Linear <OK-2i-0>
........activation_fn SiLU <OK-2i-0>
......input_layernorm Phi3RMSNorm <OK-2i-0>
......post_attention_layernorm Phi3RMSNorm <OK-2i-0>
......resid_attn_dropout Dropout <OK-2i-0>
......resid_mlp_dropout Dropout <OK-2i-0>
....layers[1] Phi3DecoderLayer OK -- ExportedProgram
......self_attn Phi3Attention <OK-2i-0>
........o_proj Linear <OK-2i-0>
........qkv_proj Linear <OK-2i-0>
......mlp Phi3MLP <OK-2i-0>
........gate_up_proj Linear <OK-2i-0>
........down_proj Linear <OK-2i-0>
........activation_fn SiLU <OK-2i-0>
......input_layernorm Phi3RMSNorm <OK-2i-0>
......post_attention_layernorm Phi3RMSNorm <OK-2i-0>
......resid_attn_dropout Dropout <OK-2i-0>
......resid_mlp_dropout Dropout <OK-2i-0>
....norm Phi3RMSNorm OK -- ExportedProgram
....rotary_emb Phi3RotaryEmbedding FAIL -- step=EXPORT, reason='Could not guard on data-dependent expression Eq(u0, 1) (unhinted: Eq(u0, 1)). (Size-like symbols: n...'
..lm_head Linear OK -- ExportedProgram
Replace the failing module by a custom op¶
The main module is not exportable because one piece cannot be exported. But maybe if we assume it works, maybe everything else is working. So let’s try to replace this class by a custom op. This will be something for another example.
Total running time of the script: (0 minutes 7.551 seconds)
Related examples
Export Phi-3.5-mini-instruct with report_exportability
Export Phi-3.5-mini-instruct with report_exportability
