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-SiLUActivation.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-SiLUActivation.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,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:T9r4,position_ids:T7r2,past_key_values:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]),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:T9r4,position_ids:T7r2,past_key_values:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]),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:SiLUActivation] > T1r3
[activation_fn:SiLUActivation] < 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:T9r4,position_ids:T7r2,past_key_values:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]),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:T9r4,position_ids:T7r2,past_key_values:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]),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:SiLUActivation] > T1r3
[activation_fn:SiLUActivation] < 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,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:T9r4,position_ids:T7r2,past_key_values:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]),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:T9r4,position_ids:T7r2,past_key_values:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]),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:SiLUActivation] > T1r3
[activation_fn:SiLUActivation] < 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:T9r4,position_ids:T7r2,past_key_values:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]),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:T9r4,position_ids:T7r2,past_key_values:DynamicCache(key_cache=#2[T1r4,T1r4], value_cache=#2[T1r4,T1r4]),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:SiLUActivation] > T1r3
[activation_fn:SiLUActivation] < 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[507,31319:A16171.5],attention_mask:CT7s2x33[1,1:A1.0],past_key_values:DynamicCache(key_cache=#2[CT1s2x32x30x96[-4.465545177459717,4.057265758514404:A0.0007159979157076545],CT1s2x32x30x96[-4.467886924743652,4.720084190368652:A-2.9030142023030015e-06]], value_cache=#2[CT1s2x32x30x96[-4.517740726470947,4.218741416931152:A-0.0006990449411434267],CT1s2x32x30x96[-4.35960054397583,4.4602131843566895:A0.0017125505371854471]])))
> ((),dict(input_ids:CT7s3x4[3082,29391:A18824.416666666668],attention_mask:CT7s3x35[1,1:A1.0],past_key_values:DynamicCache(key_cache=#2[CT1s3x32x31x96[-4.662174701690674,4.428377628326416:A-0.0034959197779404635],CT1s3x32x31x96[-4.522449970245361,4.264554977416992:A-0.002481214040555478]], value_cache=#2[CT1s3x32x31x96[-4.631809234619141,4.268094539642334:A-0.00029668831490536814],CT1s3x32x31x96[-5.316957473754883,4.8129777908325195:A-0.003804903293625481]])))
>>> model: Phi3Model
> ((),dict(input_ids:CT7s2x3[507,31319:A16171.5],attention_mask:CT7s2x33[1,1:A1.0],position_ids:None,past_key_values:DynamicCache(key_cache=#2[CT1s2x32x30x96[-4.465545177459717,4.057265758514404:A0.0007159979157076545],CT1s2x32x30x96[-4.467886924743652,4.720084190368652:A-2.9030142023030015e-06]], value_cache=#2[CT1s2x32x30x96[-4.517740726470947,4.218741416931152:A-0.0006990449411434267],CT1s2x32x30x96[-4.35960054397583,4.4602131843566895:A0.0017125505371854471]]),inputs_embeds:None,use_cache:None,cache_position:None))
> ((),dict(input_ids:CT7s3x4[3082,29391:A18824.416666666668],attention_mask:CT7s3x35[1,1:A1.0],position_ids:None,past_key_values:DynamicCache(key_cache=#2[CT1s3x32x31x96[-4.662174701690674,4.428377628326416:A-0.0034959197779404635],CT1s3x32x31x96[-4.522449970245361,4.264554977416992:A-0.002481214040555478]], value_cache=#2[CT1s3x32x31x96[-4.631809234619141,4.268094539642334:A-0.00029668831490536814],CT1s3x32x31x96[-5.316957473754883,4.8129777908325195:A-0.003804903293625481]]),inputs_embeds:None,use_cache:None,cache_position:None))
>>> embed_tokens: Embedding
> ((CT7s2x3[507,31319:A16171.5],),{})
> ((CT7s3x4[3082,29391:A18824.416666666668],),{})
< (CT1s2x3x3072[-0.07626397162675858,0.07296812534332275:A-3.689262259222293e-05],)
< (CT1s3x4x3072[-0.09224457293748856,0.08149274438619614:A-0.00010771516626686652],)
<<<
>>> layers[0]: Phi3DecoderLayer
> ((CT1s2x3x3072[-0.07626397162675858,0.07296812534332275:A-3.689262259222293e-05],),dict(attention_mask:CT9s2x1x3x33[False,True:A0.9696969696969697],position_ids:CT7s1x3[30,32:A31.0],past_key_values:DynamicCache(key_cache=#2[CT1s2x32x30x96[-4.465545177459717,4.057265758514404:A0.0007159979157076545],CT1s2x32x30x96[-4.467886924743652,4.720084190368652:A-2.9030142023030015e-06]], value_cache=#2[CT1s2x32x30x96[-4.517740726470947,4.218741416931152:A-0.0006990449411434267],CT1s2x32x30x96[-4.35960054397583,4.4602131843566895:A0.0017125505371854471]]),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[-0.09224457293748856,0.08149274438619614:A-0.00010771516626686652],),dict(attention_mask:CT9s3x1x4x35[False,True:A0.9571428571428572],position_ids:CT7s1x4[31,34:A32.5],past_key_values:DynamicCache(key_cache=#2[CT1s3x32x31x96[-4.662174701690674,4.428377628326416:A-0.0034959197779404635],CT1s3x32x31x96[-4.522449970245361,4.264554977416992:A-0.002481214040555478]], value_cache=#2[CT1s3x32x31x96[-4.631809234619141,4.268094539642334:A-0.00029668831490536814],CT1s3x32x31x96[-5.316957473754883,4.8129777908325195:A-0.003804903293625481]]),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[-3.783019781112671,3.565221071243286:A-0.0018069703635737887],attention_mask:CT9s2x1x3x33[False,True:A0.9696969696969697],position_ids:CT7s1x3[30,32:A31.0],past_key_values:DynamicCache(key_cache=#2[CT1s2x32x30x96[-4.465545177459717,4.057265758514404:A0.0007159979157076545],CT1s2x32x30x96[-4.467886924743652,4.720084190368652:A-2.9030142023030015e-06]], value_cache=#2[CT1s2x32x30x96[-4.517740726470947,4.218741416931152:A-0.0006990449411434267],CT1s2x32x30x96[-4.35960054397583,4.4602131843566895:A0.0017125505371854471]]),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[-4.443816661834717,4.056170463562012:A-0.005361890270728864],attention_mask:CT9s3x1x4x35[False,True:A0.9571428571428572],position_ids:CT7s1x4[31,34:A32.5],past_key_values:DynamicCache(key_cache=#2[CT1s3x32x31x96[-4.662174701690674,4.428377628326416:A-0.0034959197779404635],CT1s3x32x31x96[-4.522449970245361,4.264554977416992:A-0.002481214040555478]], value_cache=#2[CT1s3x32x31x96[-4.631809234619141,4.268094539642334:A-0.00029668831490536814],CT1s3x32x31x96[-5.316957473754883,4.8129777908325195:A-0.003804903293625481]]),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[-2.2603085041046143,2.0475239753723145:A-0.0008883428078339466],),{})
> ((CT1s3x4x3072[-2.245011568069458,2.13265061378479:A-0.0008563481390419082],),{})
< (CT1s2x3x3072[-1.569084882736206,1.5451966524124146:A-0.0039206560408931385],)
< (CT1s3x4x3072[-1.807442545890808,1.5120015144348145:A0.000409557451929585],)
<<<
>>> qkv_proj: Linear
> ((CT1s2x3x3072[-3.783019781112671,3.565221071243286:A-0.0018069703635737887],),{})
> ((CT1s3x4x3072[-4.443816661834717,4.056170463562012:A-0.005361890270728864],),{})
< (CT1s2x3x9216[-4.903987884521484,5.047000408172607:A-0.0037297510212797366],)
< (CT1s3x4x9216[-4.425784587860107,4.825686454772949:A0.0015161730776901128],)
<<<
< (CT1s2x3x3072[-1.569084882736206,1.5451966524124146:A-0.0039206560408931385],None)
< (CT1s3x4x3072[-1.807442545890808,1.5120015144348145:A0.000409557451929585],None)
<<<
>>> mlp: Phi3MLP
> ((CT1s2x3x3072[-4.080776214599609,3.891044855117798:A-0.01037505980212493],),{})
> ((CT1s3x4x3072[-4.547957897186279,3.9305286407470703:A0.0004034293294156536],),{})
>>> gate_up_proj: Linear
> ((CT1s2x3x3072[-4.080776214599609,3.891044855117798:A-0.01037505980212493],),{})
> ((CT1s3x4x3072[-4.547957897186279,3.9305286407470703:A0.0004034293294156536],),{})
< (CT1s2x3x16384[-4.730355262756348,4.846550464630127:A-0.000785505145168249],)
< (CT1s3x4x16384[-4.877781867980957,4.554588794708252:A0.0016664399843620004],)
<<<
>>> down_proj: Linear
> ((CT1s2x3x8192[-9.154955863952637,12.709978103637695:A-0.004634474402878486],),{})
> ((CT1s3x4x8192[-9.155027389526367,11.09277057647705:A0.0019418056024517155],),{})
< (CT1s2x3x3072[-5.099157810211182,5.868416786193848:A-0.005633440847683839],)
< (CT1s3x4x3072[-5.447781085968018,5.121474742889404:A0.004263810524741353],)
<<<
>>> activation_fn: SiLUActivation
> ((CT1s2x3x8192[-4.490071773529053,4.846550464630127:A-0.003992798204258463],),{})
> ((CT1s3x4x8192[-4.877781867980957,4.554588794708252:A0.00020859190073470776],),{})
< (CT1s2x3x8192[-0.27846455574035645,4.808775424957275:A0.24274485934891235],)
< (CT1s3x4x8192[-0.27846455574035645,4.507178783416748:A0.2457273621407878],)
<<<
< (CT1s2x3x3072[-5.099157810211182,5.868416786193848:A-0.005633440847683839],)
< (CT1s3x4x3072[-5.447781085968018,5.121474742889404:A0.004263810524741353],)
<<<
>>> input_layernorm: Phi3RMSNorm
> ((CT1s2x3x3072[-0.07626397162675858,0.07296812534332275:A-3.689262259222293e-05],),{})
> ((CT1s3x4x3072[-0.09224457293748856,0.08149274438619614:A-0.00010771516626686652],),{})
< (CT1s2x3x3072[-3.783019781112671,3.565221071243286:A-0.0018069703635737887],)
< (CT1s3x4x3072[-4.443816661834717,4.056170463562012:A-0.005361890270728864],)
<<<
>>> post_attention_layernorm: Phi3RMSNorm
> ((CT1s2x3x3072[-1.6129931211471558,1.5474786758422852:A-0.003957548700933937],),{})
> ((CT1s3x4x3072[-1.7693893909454346,1.5163228511810303:A0.0003018422545753512],),{})
< (CT1s2x3x3072[-4.080776214599609,3.891044855117798:A-0.01037505980212493],)
< (CT1s3x4x3072[-4.547957897186279,3.9305286407470703:A0.0004034293294156536],)
<<<
>>> resid_attn_dropout: Dropout
> ((CT1s2x3x3072[-1.569084882736206,1.5451966524124146:A-0.0039206560408931385],),{})
> ((CT1s3x4x3072[-1.807442545890808,1.5120015144348145:A0.000409557451929585],),{})
< (CT1s2x3x3072[-1.569084882736206,1.5451966524124146:A-0.0039206560408931385],)
< (CT1s3x4x3072[-1.807442545890808,1.5120015144348145:A0.000409557451929585],)
<<<
>>> resid_mlp_dropout: Dropout
> ((CT1s2x3x3072[-5.099157810211182,5.868416786193848:A-0.005633440847683839],),{})
> ((CT1s3x4x3072[-5.447781085968018,5.121474742889404:A0.004263810524741353],),{})
< (CT1s2x3x3072[-5.099157810211182,5.868416786193848:A-0.005633440847683839],)
< (CT1s3x4x3072[-5.447781085968018,5.121474742889404:A0.004263810524741353],)
<<<
< (CT1s2x3x3072[-5.115391254425049,6.402807235717773:A-0.00959098940287125],)
< (CT1s3x4x3072[-5.675037384033203,5.982346534729004:A0.004565652693991574],)
<<<
>>> layers[1]: Phi3DecoderLayer
> ((CT1s2x3x3072[-5.115391254425049,6.402807235717773:A-0.00959098940287125],),dict(attention_mask:CT9s2x1x3x33[False,True:A0.9696969696969697],position_ids:CT7s1x3[30,32:A31.0],past_key_values:DynamicCache(key_cache=#2[CT1s2x32x33x96[-5.243246555328369,5.216899394989014:A-0.0008380057157270766],CT1s2x32x30x96[-4.467886924743652,4.720084190368652:A-2.9030142023030015e-06]], value_cache=#2[CT1s2x32x33x96[-4.903987884521484,4.691673755645752:A-0.0011286081004508537],CT1s2x32x30x96[-4.35960054397583,4.4602131843566895:A0.0017125505371854471]]),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[-5.675037384033203,5.982346534729004:A0.004565652693991574],),dict(attention_mask:CT9s3x1x4x35[False,True:A0.9571428571428572],position_ids:CT7s1x4[31,34:A32.5],past_key_values:DynamicCache(key_cache=#2[CT1s3x32x35x96[-5.2977495193481445,5.638122081756592:A-0.0022213943482518434],CT1s3x32x31x96[-4.522449970245361,4.264554977416992:A-0.002481214040555478]], value_cache=#2[CT1s3x32x35x96[-4.631809234619141,4.825686454772949:A0.00020694482039367723],CT1s3x32x31x96[-5.316957473754883,4.8129777908325195:A-0.003804903293625481]]),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[-3.692824363708496,4.645721912384033:A-0.006849586979686567],attention_mask:CT9s2x1x3x33[False,True:A0.9696969696969697],position_ids:CT7s1x3[30,32:A31.0],past_key_values:DynamicCache(key_cache=#2[CT1s2x32x33x96[-5.243246555328369,5.216899394989014:A-0.0008380057157270766],CT1s2x32x30x96[-4.467886924743652,4.720084190368652:A-2.9030142023030015e-06]], value_cache=#2[CT1s2x32x33x96[-4.903987884521484,4.691673755645752:A-0.0011286081004508537],CT1s2x32x30x96[-4.35960054397583,4.4602131843566895:A0.0017125505371854471]]),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[-4.032126426696777,4.3049540519714355:A0.0032200051591207116],attention_mask:CT9s3x1x4x35[False,True:A0.9571428571428572],position_ids:CT7s1x4[31,34:A32.5],past_key_values:DynamicCache(key_cache=#2[CT1s3x32x35x96[-5.2977495193481445,5.638122081756592:A-0.0022213943482518434],CT1s3x32x31x96[-4.522449970245361,4.264554977416992:A-0.002481214040555478]], value_cache=#2[CT1s3x32x35x96[-4.631809234619141,4.825686454772949:A0.00020694482039367723],CT1s3x32x31x96[-5.316957473754883,4.8129777908325195:A-0.003804903293625481]]),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[-1.9148494005203247,1.9142004251480103:A0.002065079880343706],),{})
> ((CT1s3x4x3072[-2.328655481338501,1.8774923086166382:A-0.0018731078297801556],),{})
< (CT1s2x3x3072[-1.719269037246704,1.4660935401916504:A-0.0038470371489564867],)
< (CT1s3x4x3072[-1.5977507829666138,1.5770745277404785:A0.004415256080695447],)
<<<
>>> qkv_proj: Linear
> ((CT1s2x3x3072[-3.692824363708496,4.645721912384033:A-0.006849586979686567],),{})
> ((CT1s3x4x3072[-4.032126426696777,4.3049540519714355:A0.0032200051591207116],),{})
< (CT1s2x3x9216[-4.772463798522949,4.502849578857422:A-0.011448141658581368],)
< (CT1s3x4x9216[-4.546624183654785,5.012377738952637:A-0.006584659571861569],)
<<<
< (CT1s2x3x3072[-1.719269037246704,1.4660935401916504:A-0.0038470371489564867],None)
< (CT1s3x4x3072[-1.5977507829666138,1.5770745277404785:A0.004415256080695447],None)
<<<
>>> mlp: Phi3MLP
> ((CT1s2x3x3072[-3.7153704166412354,4.575687408447266:A-0.009262047859905524],),{})
> ((CT1s3x4x3072[-3.7643024921417236,4.266655921936035:A0.006118759006482939],),{})
>>> gate_up_proj: Linear
> ((CT1s2x3x3072[-3.7153704166412354,4.575687408447266:A-0.009262047859905524],),{})
> ((CT1s3x4x3072[-3.7643024921417236,4.266655921936035:A0.006118759006482939],),{})
< (CT1s2x3x16384[-5.145788192749023,4.845174789428711:A0.009646031746124587],)
< (CT1s3x4x16384[-5.195202350616455,4.568906307220459:A0.0014517907274621915],)
<<<
>>> down_proj: Linear
> ((CT1s2x3x8192[-10.810708045959473,9.12816047668457:A-0.00023827384149119565],),{})
> ((CT1s3x4x8192[-9.432348251342773,12.139277458190918:A0.0004174245607357985],),{})
< (CT1s2x3x3072[-5.995305061340332,5.33147668838501:A-0.003057508847834672],)
< (CT1s3x4x3072[-5.230845928192139,5.353349685668945:A-0.016111877306924625],)
<<<
>>> activation_fn: SiLUActivation
> ((CT1s2x3x8192[-4.308592796325684,4.845174789428711:A0.008163898182194393],),{})
> ((CT1s3x4x8192[-4.877556800842285,4.540010929107666:A0.005127051427541811],),{})
< (CT1s2x3x8192[-0.27846455574035645,4.807358741760254:A0.24811603383206315],)
< (CT1s3x4x8192[-0.27846455574035645,4.492065906524658:A0.24814261865139484],)
<<<
< (CT1s2x3x3072[-5.995305061340332,5.33147668838501:A-0.003057508847834672],)
< (CT1s3x4x3072[-5.230845928192139,5.353349685668945:A-0.016111877306924625],)
<<<
>>> input_layernorm: Phi3RMSNorm
> ((CT1s2x3x3072[-5.115391254425049,6.402807235717773:A-0.00959098940287125],),{})
> ((CT1s3x4x3072[-5.675037384033203,5.982346534729004:A0.004565652693991574],),{})
< (CT1s2x3x3072[-3.692824363708496,4.645721912384033:A-0.006849586979686567],)
< (CT1s3x4x3072[-4.032126426696777,4.3049540519714355:A0.0032200051591207116],)
<<<
>>> post_attention_layernorm: Phi3RMSNorm
> ((CT1s2x3x3072[-5.4467339515686035,6.48449182510376:A-0.013438026726109657],),{})
> ((CT1s3x4x3072[-5.403885364532471,6.139309883117676:A0.008980908598965066],),{})
< (CT1s2x3x3072[-3.7153704166412354,4.575687408447266:A-0.009262047859905524],)
< (CT1s3x4x3072[-3.7643024921417236,4.266655921936035:A0.006118759006482939],)
<<<
>>> resid_attn_dropout: Dropout
> ((CT1s2x3x3072[-1.719269037246704,1.4660935401916504:A-0.0038470371489564867],),{})
> ((CT1s3x4x3072[-1.5977507829666138,1.5770745277404785:A0.004415256080695447],),{})
< (CT1s2x3x3072[-1.719269037246704,1.4660935401916504:A-0.0038470371489564867],)
< (CT1s3x4x3072[-1.5977507829666138,1.5770745277404785:A0.004415256080695447],)
<<<
>>> resid_mlp_dropout: Dropout
> ((CT1s2x3x3072[-5.995305061340332,5.33147668838501:A-0.003057508847834672],),{})
> ((CT1s3x4x3072[-5.230845928192139,5.353349685668945:A-0.016111877306924625],),{})
< (CT1s2x3x3072[-5.995305061340332,5.33147668838501:A-0.003057508847834672],)
< (CT1s3x4x3072[-5.230845928192139,5.353349685668945:A-0.016111877306924625],)
<<<
< (CT1s2x3x3072[-8.706647872924805,8.32397747039795:A-0.01649553544161285],)
< (CT1s3x4x3072[-8.708154678344727,9.680032730102539:A-0.007130968992618768],)
<<<
>>> norm: Phi3RMSNorm
> ((CT1s2x3x3072[-8.706647872924805,8.32397747039795:A-0.01649553544161285],),{})
> ((CT1s3x4x3072[-8.708154678344727,9.680032730102539:A-0.007130968992618768],),{})
< (CT1s2x3x3072[-4.431250095367432,4.241793155670166:A-0.008410615578096328],)
< (CT1s3x4x3072[-4.300024509429932,4.754427433013916:A-0.003576264563900761],)
<<<
>>> rotary_emb: Phi3RotaryEmbedding
> ((CT1s2x3x3072[-0.07626397162675858,0.07296812534332275:A-3.689262259222293e-05],CT7s1x3[30,32:A31.0]),{})
> ((CT1s3x4x3072[-0.09224457293748856,0.08149274438619614:A-0.00010771516626686652],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[-4.431250095367432,4.241793155670166:A-0.008410615578096328],past_key_values:DynamicCache(key_cache=#2[CT1s2x32x33x96[-5.243246555328369,5.216899394989014:A-0.0008380057157270766],CT1s2x32x33x96[-4.581390380859375,5.112067222595215:A-0.0005753138876627784]], value_cache=#2[CT1s2x32x33x96[-4.903987884521484,4.691673755645752:A-0.0011286081004508537],CT1s2x32x33x96[-4.35960054397583,4.502186298370361:A0.0023477824063049342]])),)
< (dict(last_hidden_state:CT1s3x4x3072[-4.300024509429932,4.754427433013916:A-0.003576264563900761],past_key_values:DynamicCache(key_cache=#2[CT1s3x32x35x96[-5.2977495193481445,5.638122081756592:A-0.0022213943482518434],CT1s3x32x35x96[-5.407881736755371,5.356963157653809:A-0.002788695160612269]], value_cache=#2[CT1s3x32x35x96[-4.631809234619141,4.825686454772949:A0.00020694482039367723],CT1s3x32x35x96[-5.316957473754883,5.012377738952637:A-0.004497091284961408]])),)
<<<
>>> lm_head: Linear
> ((CT1s2x3x3072[-4.431250095367432,4.241793155670166:A-0.008410615578096328],),{})
> ((CT1s3x4x3072[-4.300024509429932,4.754427433013916:A-0.003576264563900761],),{})
< (CT1s2x3x32064[-5.049499034881592,5.194516658782959:A-0.0010708118826330687],)
< (CT1s3x4x32064[-5.062286376953125,5.413645267486572:A0.00036001507699741825],)
<<<
< (dict(logits:CT1s2x3x32064[-5.049499034881592,5.194516658782959:A-0.0010708118826330687],past_key_values:DynamicCache(key_cache=#2[CT1s2x32x33x96[-5.243246555328369,5.216899394989014:A-0.0008380057157270766],CT1s2x32x33x96[-4.581390380859375,5.112067222595215:A-0.0005753138876627784]], value_cache=#2[CT1s2x32x33x96[-4.903987884521484,4.691673755645752:A-0.0011286081004508537],CT1s2x32x33x96[-4.35960054397583,4.502186298370361:A0.0023477824063049342]])),)
< (dict(logits:CT1s3x4x32064[-5.062286376953125,5.413645267486572:A0.00036001507699741825],past_key_values:DynamicCache(key_cache=#2[CT1s3x32x35x96[-5.2977495193481445,5.638122081756592:A-0.0022213943482518434],CT1s3x32x35x96[-5.407881736755371,5.356963157653809:A-0.002788695160612269]], value_cache=#2[CT1s3x32x35x96[-4.631809234619141,4.825686454772949:A0.00020694482039367723],CT1s3x32x35x96[-5.316957473754883,5.012377738952637:A-0.004497091284961408]])),)
<<<
[_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-SiLUActivation
[_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-SiLUActivation
[_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(DYNAMIC), 1: DimHint(DYNAMIC)},
'input_ids': {0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},
'past_key_values': [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)},
{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}],
[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)},
{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]]})
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(DYNAMIC), 1: DimHint(DYNAMIC)}, 'input_ids': {0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)}, 'past_key_values': [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}], [{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]]})
>>> model: Phi3Model
DS=((), {'attention_mask': {0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)}, 'cache_position': None, 'input_ids': {0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)}, 'inputs_embeds': None, 'past_key_values': [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}], [{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]], 'position_ids': None, 'use_cache': None})
>>> embed_tokens: Embedding: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
>>> layers[0]: Phi3DecoderLayer
DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {'attention_mask': {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC), 3: DimHint(DYNAMIC)}, 'cache_position': {0: DimHint(DYNAMIC)}, 'past_key_values': [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}], [{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]], 'position_embeddings': ({1: DimHint(DYNAMIC)}, {1: DimHint(DYNAMIC)}), 'position_ids': {1: DimHint(DYNAMIC)}, 'use_cache': None})
>>> self_attn: Phi3Attention
DS=((), {'attention_mask': {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC), 3: DimHint(DYNAMIC)}, 'cache_position': {0: DimHint(DYNAMIC)}, 'hidden_states': {0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)}, 'past_key_values': [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}], [{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]], 'position_embeddings': ({1: DimHint(DYNAMIC)}, {1: DimHint(DYNAMIC)}), 'position_ids': {1: DimHint(DYNAMIC)}, 'use_cache': None})
>>> o_proj: Linear: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
>>> qkv_proj: Linear: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
<<<
>>> mlp: Phi3MLP
DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {})
>>> gate_up_proj: Linear: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
>>> down_proj: Linear: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
>>> activation_fn: SiLUActivation: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
<<<
>>> input_layernorm: Phi3RMSNorm: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
>>> post_attention_layernorm: Phi3RMSNorm: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
>>> resid_attn_dropout: Dropout: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
>>> resid_mlp_dropout: Dropout: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
<<<
>>> layers[1]: Phi3DecoderLayer
DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {'attention_mask': {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC), 3: DimHint(DYNAMIC)}, 'cache_position': {0: DimHint(DYNAMIC)}, 'past_key_values': [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}], [{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]], 'position_embeddings': ({1: DimHint(DYNAMIC)}, {1: DimHint(DYNAMIC)}), 'position_ids': {1: DimHint(DYNAMIC)}, 'use_cache': None})
>>> self_attn: Phi3Attention
DS=((), {'attention_mask': {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC), 3: DimHint(DYNAMIC)}, 'cache_position': {0: DimHint(DYNAMIC)}, 'hidden_states': {0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)}, 'past_key_values': [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}], [{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]], 'position_embeddings': ({1: DimHint(DYNAMIC)}, {1: DimHint(DYNAMIC)}), 'position_ids': {1: DimHint(DYNAMIC)}, 'use_cache': None})
>>> o_proj: Linear: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
>>> qkv_proj: Linear: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
<<<
>>> mlp: Phi3MLP
DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {})
>>> gate_up_proj: Linear: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
>>> down_proj: Linear: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
>>> activation_fn: SiLUActivation: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
<<<
>>> input_layernorm: Phi3RMSNorm: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
>>> post_attention_layernorm: Phi3RMSNorm: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
>>> resid_attn_dropout: Dropout: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
>>> resid_mlp_dropout: Dropout: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
<<<
>>> norm: Phi3RMSNorm: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
>>> rotary_emb: Phi3RotaryEmbedding: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)}, {1: DimHint(DYNAMIC)}), {}) <<<
<<<
>>> lm_head: Linear: DS=(({0: DimHint(DYNAMIC), 1: DimHint(DYNAMIC)},), {}) <<<
<<<
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: SiLUActivation <<<
<<<
>>> 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: SiLUActivation <<<
<<<
>>> 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,
)
----------------------
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] M:__main__-Phi3ForCausalLM --- FAIL, step=EXPORT, reason=Cannot associate shape [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}], [{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]] specified at `dynamic_shapes['past_key_values']` to non-tensor type <class 'transformers.cache_utils.DynamicCache'> at `inputs['past_key_values']` (expected None) --- For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation --- --- 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 1572, in _try_export_no_bypass_export\n ep = torch_export(\n ^^^^^^^^^^^^^\n', ' File "~/github/experimental-experiment/experimental_experiment/export_helpers.py", line 152, in torch_export\n return torch.export.export(\n ^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 311, in export\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 277, in export\n return _export(\n ^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1190, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1156, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 124, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2292, in _export\n ep = _export_for_training(\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1190, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1156, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 124, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2101, in _export_for_training\n export_artifact = export_func(\n ^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1987, in _non_strict_export\n ) = make_fake_inputs(\n ^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_export/non_strict_utils.py", line 407, in make_fake_inputs\n _check_dynamic_shapes(combined_args, dynamic_shapes)\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 1049, in _check_dynamic_shapes\n _tree_map_with_path(check_shape, combined_args, dynamic_shapes, tree_name="inputs")\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 632, in _tree_map_with_path\n return tree_map_with_path(f, tree, *dynamic_shapes, is_leaf=is_leaf)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_pytree.py", line 2059, in tree_map_with_path\n return treespec.unflatten(func(*xs) for xs in zip(*all_keypath_leaves))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_pytree.py", line 1199, in unflatten\n leaves = list(leaves)\n ^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_pytree.py", line 2059, in <genexpr>\n return treespec.unflatten(func(*xs) for xs in zip(*all_keypath_leaves))\n ^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 629, in f\n return func(path, t, *dynamic_shapes)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 1042, in check_shape\n raise UserError(\n', "torch._dynamo.exc.UserError: Cannot associate shape [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}], [{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]] specified at `dynamic_shapes['past_key_values']` to non-tensor type <class 'transformers.cache_utils.DynamicCache'> at `inputs['past_key_values']` (expected None)\nFor more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation\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"]
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] .. M:model-Phi3Model --- FAIL, step=EXPORT, reason=Cannot associate shape [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}], [{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]] specified at `dynamic_shapes['past_key_values']` to non-tensor type <class 'transformers.cache_utils.DynamicCache'> at `inputs['past_key_values']` (expected None) --- For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation --- --- 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 1572, in _try_export_no_bypass_export\n ep = torch_export(\n ^^^^^^^^^^^^^\n', ' File "~/github/experimental-experiment/experimental_experiment/export_helpers.py", line 152, in torch_export\n return torch.export.export(\n ^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 311, in export\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 277, in export\n return _export(\n ^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1190, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1156, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 124, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2292, in _export\n ep = _export_for_training(\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1190, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1156, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 124, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2101, in _export_for_training\n export_artifact = export_func(\n ^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1987, in _non_strict_export\n ) = make_fake_inputs(\n ^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_export/non_strict_utils.py", line 407, in make_fake_inputs\n _check_dynamic_shapes(combined_args, dynamic_shapes)\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 1049, in _check_dynamic_shapes\n _tree_map_with_path(check_shape, combined_args, dynamic_shapes, tree_name="inputs")\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 632, in _tree_map_with_path\n return tree_map_with_path(f, tree, *dynamic_shapes, is_leaf=is_leaf)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_pytree.py", line 2059, in tree_map_with_path\n return treespec.unflatten(func(*xs) for xs in zip(*all_keypath_leaves))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_pytree.py", line 1199, in unflatten\n leaves = list(leaves)\n ^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_pytree.py", line 2059, in <genexpr>\n return treespec.unflatten(func(*xs) for xs in zip(*all_keypath_leaves))\n ^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 629, in f\n return func(path, t, *dynamic_shapes)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 1042, in check_shape\n raise UserError(\n', "torch._dynamo.exc.UserError: Cannot associate shape [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}], [{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]] specified at `dynamic_shapes['past_key_values']` to non-tensor type <class 'transformers.cache_utils.DynamicCache'> at `inputs['past_key_values']` (expected None)\nFor more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation\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"]
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] .... M:embed_tokens-Embedding --- OK:
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] .... M:layers[0]-Phi3DecoderLayer --- FAIL, step=EXPORT, reason=Cannot associate shape [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}], [{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]] specified at `dynamic_shapes['past_key_values']` to non-tensor type <class 'transformers.cache_utils.DynamicCache'> at `inputs['past_key_values']` (expected None) --- For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation --- --- 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 1572, in _try_export_no_bypass_export\n ep = torch_export(\n ^^^^^^^^^^^^^\n', ' File "~/github/experimental-experiment/experimental_experiment/export_helpers.py", line 152, in torch_export\n return torch.export.export(\n ^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 311, in export\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 277, in export\n return _export(\n ^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1190, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1156, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 124, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2292, in _export\n ep = _export_for_training(\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1190, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1156, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 124, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2101, in _export_for_training\n export_artifact = export_func(\n ^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1987, in _non_strict_export\n ) = make_fake_inputs(\n ^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_export/non_strict_utils.py", line 407, in make_fake_inputs\n _check_dynamic_shapes(combined_args, dynamic_shapes)\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 1049, in _check_dynamic_shapes\n _tree_map_with_path(check_shape, combined_args, dynamic_shapes, tree_name="inputs")\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 632, in _tree_map_with_path\n return tree_map_with_path(f, tree, *dynamic_shapes, is_leaf=is_leaf)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_pytree.py", line 2059, in tree_map_with_path\n return treespec.unflatten(func(*xs) for xs in zip(*all_keypath_leaves))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_pytree.py", line 1199, in unflatten\n leaves = list(leaves)\n ^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_pytree.py", line 2059, in <genexpr>\n return treespec.unflatten(func(*xs) for xs in zip(*all_keypath_leaves))\n ^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 629, in f\n return func(path, t, *dynamic_shapes)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 1042, in check_shape\n raise UserError(\n', "torch._dynamo.exc.UserError: Cannot associate shape [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}], [{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]] specified at `dynamic_shapes['past_key_values']` to non-tensor type <class 'transformers.cache_utils.DynamicCache'> at `inputs['past_key_values']` (expected None)\nFor more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation\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"]
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] ...... M:self_attn-Phi3Attention --- FAIL, step=EXPORT, reason=Cannot associate shape [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}], [{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]] specified at `dynamic_shapes['past_key_values']` to non-tensor type <class 'transformers.cache_utils.DynamicCache'> at `inputs['past_key_values']` (expected None) --- For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation --- --- 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 1572, in _try_export_no_bypass_export\n ep = torch_export(\n ^^^^^^^^^^^^^\n', ' File "~/github/experimental-experiment/experimental_experiment/export_helpers.py", line 152, in torch_export\n return torch.export.export(\n ^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 311, in export\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 277, in export\n return _export(\n ^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1190, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1156, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 124, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2292, in _export\n ep = _export_for_training(\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1190, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1156, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 124, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2101, in _export_for_training\n export_artifact = export_func(\n ^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1987, in _non_strict_export\n ) = make_fake_inputs(\n ^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_export/non_strict_utils.py", line 407, in make_fake_inputs\n _check_dynamic_shapes(combined_args, dynamic_shapes)\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 1049, in _check_dynamic_shapes\n _tree_map_with_path(check_shape, combined_args, dynamic_shapes, tree_name="inputs")\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 632, in _tree_map_with_path\n return tree_map_with_path(f, tree, *dynamic_shapes, is_leaf=is_leaf)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_pytree.py", line 2059, in tree_map_with_path\n return treespec.unflatten(func(*xs) for xs in zip(*all_keypath_leaves))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_pytree.py", line 1199, in unflatten\n leaves = list(leaves)\n ^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_pytree.py", line 2059, in <genexpr>\n return treespec.unflatten(func(*xs) for xs in zip(*all_keypath_leaves))\n ^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 629, in f\n return func(path, t, *dynamic_shapes)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 1042, in check_shape\n raise UserError(\n', "torch._dynamo.exc.UserError: Cannot associate shape [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}], [{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]] specified at `dynamic_shapes['past_key_values']` to non-tensor type <class 'transformers.cache_utils.DynamicCache'> at `inputs['past_key_values']` (expected None)\nFor more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation\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"]
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] ........ M:o_proj-Linear --- OK:
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] ........ M:qkv_proj-Linear --- OK:
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] ...... M:mlp-Phi3MLP --- OK:
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] ...... M:input_layernorm-Phi3RMSNorm --- OK:
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] ...... M:post_attention_layernorm-Phi3RMSNorm --- OK:
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] ...... M:resid_attn_dropout-Dropout --- OK:
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] ...... M:resid_mlp_dropout-Dropout --- OK:
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] .... M:layers[1]-Phi3DecoderLayer --- FAIL, step=EXPORT, reason=Cannot associate shape [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}], [{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]] specified at `dynamic_shapes['past_key_values']` to non-tensor type <class 'transformers.cache_utils.DynamicCache'> at `inputs['past_key_values']` (expected None) --- For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation --- --- 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 1572, in _try_export_no_bypass_export\n ep = torch_export(\n ^^^^^^^^^^^^^\n', ' File "~/github/experimental-experiment/experimental_experiment/export_helpers.py", line 152, in torch_export\n return torch.export.export(\n ^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 311, in export\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 277, in export\n return _export(\n ^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1190, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1156, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 124, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2292, in _export\n ep = _export_for_training(\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1190, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1156, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 124, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2101, in _export_for_training\n export_artifact = export_func(\n ^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1987, in _non_strict_export\n ) = make_fake_inputs(\n ^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_export/non_strict_utils.py", line 407, in make_fake_inputs\n _check_dynamic_shapes(combined_args, dynamic_shapes)\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 1049, in _check_dynamic_shapes\n _tree_map_with_path(check_shape, combined_args, dynamic_shapes, tree_name="inputs")\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 632, in _tree_map_with_path\n return tree_map_with_path(f, tree, *dynamic_shapes, is_leaf=is_leaf)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_pytree.py", line 2059, in tree_map_with_path\n return treespec.unflatten(func(*xs) for xs in zip(*all_keypath_leaves))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_pytree.py", line 1199, in unflatten\n leaves = list(leaves)\n ^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_pytree.py", line 2059, in <genexpr>\n return treespec.unflatten(func(*xs) for xs in zip(*all_keypath_leaves))\n ^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 629, in f\n return func(path, t, *dynamic_shapes)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 1042, in check_shape\n raise UserError(\n', "torch._dynamo.exc.UserError: Cannot associate shape [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}], [{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]] specified at `dynamic_shapes['past_key_values']` to non-tensor type <class 'transformers.cache_utils.DynamicCache'> at `inputs['past_key_values']` (expected None)\nFor more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation\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"]
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] ...... M:self_attn-Phi3Attention --- FAIL, step=EXPORT, reason=Cannot associate shape [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}], [{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]] specified at `dynamic_shapes['past_key_values']` to non-tensor type <class 'transformers.cache_utils.DynamicCache'> at `inputs['past_key_values']` (expected None) --- For more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation --- --- 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 1572, in _try_export_no_bypass_export\n ep = torch_export(\n ^^^^^^^^^^^^^\n', ' File "~/github/experimental-experiment/experimental_experiment/export_helpers.py", line 152, in torch_export\n return torch.export.export(\n ^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 311, in export\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 277, in export\n return _export(\n ^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1190, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1156, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 124, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2292, in _export\n ep = _export_for_training(\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1190, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1156, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 124, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2101, in _export_for_training\n export_artifact = export_func(\n ^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1987, in _non_strict_export\n ) = make_fake_inputs(\n ^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_export/non_strict_utils.py", line 407, in make_fake_inputs\n _check_dynamic_shapes(combined_args, dynamic_shapes)\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 1049, in _check_dynamic_shapes\n _tree_map_with_path(check_shape, combined_args, dynamic_shapes, tree_name="inputs")\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 632, in _tree_map_with_path\n return tree_map_with_path(f, tree, *dynamic_shapes, is_leaf=is_leaf)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_pytree.py", line 2059, in tree_map_with_path\n return treespec.unflatten(func(*xs) for xs in zip(*all_keypath_leaves))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_pytree.py", line 1199, in unflatten\n leaves = list(leaves)\n ^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_pytree.py", line 2059, in <genexpr>\n return treespec.unflatten(func(*xs) for xs in zip(*all_keypath_leaves))\n ^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 629, in f\n return func(path, t, *dynamic_shapes)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/dynamic_shapes.py", line 1042, in check_shape\n raise UserError(\n', "torch._dynamo.exc.UserError: Cannot associate shape [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}], [{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}]] specified at `dynamic_shapes['past_key_values']` to non-tensor type <class 'transformers.cache_utils.DynamicCache'> at `inputs['past_key_values']` (expected None)\nFor more information about this error, see: https://pytorch.org/docs/main/generated/exportdb/index.html#dynamic-shapes-validation\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"]
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] ........ M:o_proj-Linear --- OK:
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] ........ M:qkv_proj-Linear --- OK:
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] ...... M:mlp-Phi3MLP --- OK:
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] ...... M:input_layernorm-Phi3RMSNorm --- OK:
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] ...... M:post_attention_layernorm-Phi3RMSNorm --- OK:
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] ...... M:resid_attn_dropout-Dropout --- OK:
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] ...... M:resid_mlp_dropout-Dropout --- OK:
[torch_export] export starts with backed_size_oblivious=False
[try_export-FX] .... M:norm-Phi3RMSNorm --- OK:
[torch_export] export starts with backed_size_oblivious=False
def forward(self, arg0_1: "f32[48]", arg1_1: "f32[s77, s27, 3072]", arg2_1: "i64[1, s9]"):
# 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[s77, s27, 3072]", arg2_1: "i64[1, s9]"):
# 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) --- --- consider using data-dependent friendly APIs such as guard_or_false, guard_or_true and statically_known_trueCaused by: (_export/non_strict_utils.py:1118 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 1572, in _try_export_no_bypass_export\n ep = torch_export(\n ^^^^^^^^^^^^^\n', ' File "~/github/experimental-experiment/experimental_experiment/export_helpers.py", line 152, in torch_export\n return torch.export.export(\n ^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 311, in export\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/__init__.py", line 277, in export\n return _export(\n ^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1190, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1156, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 124, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2292, in _export\n ep = _export_for_training(\n ^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1190, in wrapper\n raise e\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1156, in wrapper\n ep = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/exported_program.py", line 124, in wrapper\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2101, in _export_for_training\n export_artifact = export_func(\n ^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 2032, 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 1823, 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 1952, 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 1736, 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 2455, 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 2382, 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 2343, in _trace_inner\n t = dispatch_trace(\n ^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_compile.py", line 54, in inner\n return disable_fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_dynamo/eval_frame.py", line 1104, in _fn\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/proxy_tensor.py", line 1321, 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 1935, in trace\n res = super().trace(root, concrete_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 869, 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 1379, 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 1623, 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 189, in flat_fn\n tree_out = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/graph_capture_wrappers.py", line 1357, 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 844, 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 2022, 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 560, 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 837, 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 1780, 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 1791, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/export/_trace.py", line 1936, in forward\n tree_out = mod(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/_symbolic_trace.py", line 844, 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 2022, 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 560, 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 837, 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 1780, 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 1791, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 122, 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 1428, 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 1499, 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 1118, 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 538, in guard_bool\n r = self.evaluate()\n ^^^^^^^^^^^^^^^\n', ' File "~/vv/this312/lib/python3.12/site-packages/torch/fx/experimental/sym_node.py", line 512, 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 7296, 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 7396, 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 7419, 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 7640, 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\nconsider using data-dependent friendly APIs such as guard_or_false, guard_or_true and statically_known_trueCaused by: (_export/non_strict_utils.py:1118 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...
[torch_export] export starts with backed_size_oblivious=False
[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='Cannot associate shape [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHin...'
..model Phi3Model FAIL -- step=EXPORT, reason='Cannot associate shape [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHin...'
....embed_tokens Embedding OK -- ExportedProgram
....layers[0] Phi3DecoderLayer FAIL -- step=EXPORT, reason='Cannot associate shape [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHin...'
......self_attn Phi3Attention FAIL -- step=EXPORT, reason='Cannot associate shape [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHin...'
........o_proj Linear OK -- ExportedProgram
........qkv_proj Linear OK -- ExportedProgram
......mlp Phi3MLP OK -- ExportedProgram
........gate_up_proj Linear <OK-2i-0>
........down_proj Linear <OK-2i-0>
........activation_fn SiLUActivation <OK-2i-0>
......input_layernorm Phi3RMSNorm OK -- ExportedProgram
......post_attention_layernorm Phi3RMSNorm OK -- ExportedProgram
......resid_attn_dropout Dropout OK -- ExportedProgram
......resid_mlp_dropout Dropout OK -- ExportedProgram
....layers[1] Phi3DecoderLayer FAIL -- step=EXPORT, reason='Cannot associate shape [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHin...'
......self_attn Phi3Attention FAIL -- step=EXPORT, reason='Cannot associate shape [[{0: DimHint(DYNAMIC), 2: DimHint(DYNAMIC)}, {0: DimHint(DYNAMIC), 2: DimHin...'
........o_proj Linear OK -- ExportedProgram
........qkv_proj Linear OK -- ExportedProgram
......mlp Phi3MLP OK -- ExportedProgram
........gate_up_proj Linear <OK-2i-0>
........down_proj Linear <OK-2i-0>
........activation_fn SiLUActivation <OK-2i-0>
......input_layernorm Phi3RMSNorm OK -- ExportedProgram
......post_attention_layernorm Phi3RMSNorm OK -- ExportedProgram
......resid_attn_dropout Dropout OK -- ExportedProgram
......resid_mlp_dropout Dropout OK -- ExportedProgram
....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 5.488 seconds)
Related examples
Export Phi-3.5-mini-instruct with report_exportability
Export Phi-3.5-mini-instruct with report_exportability
