Note
Go to the end to download the full example code.
Steel method forward to guess the dynamic shapes (with Tiny-LLM)¶
Inputs are always dynamic with LLMs that is why dynamic shapes
needs to be specified when a LLM is exported with:func:torch.export.export.
Most of the examples on HuggingFace use method
transformers.GenerationMixin.generate() but we only want to
export the model and its method forward.
That example shows to guess the inputs of this method even though the model
is executed through meth generate.
We focus on the model arnir0/Tiny-LLM. To avoid downloading any weights, we write a function creating a random model based on the same architecture.
Steel the forward method¶
The first step is to guess the dummy inputs. Let’s use the true model for that. We use the dummy example from the model page.
import copy
import pprint
import torch
import transformers
from onnx_diagnostic import doc
from onnx_diagnostic.helpers import string_type
from onnx_diagnostic.helpers.torch_test_helper import steal_forward
from onnx_diagnostic.torch_models.llms import get_tiny_llm
MODEL_NAME = "arnir0/Tiny-LLM"
tokenizer = transformers.AutoTokenizer.from_pretrained(MODEL_NAME)
model = transformers.AutoModelForCausalLM.from_pretrained(MODEL_NAME)
We rewrite the forward method to print the cache dimension.
def _forward_(*args, _f=None, **kwargs):
assert _f is not None
if not hasattr(torch.compiler, "is_exporting") or not torch.compiler.is_exporting():
# torch.compiler.is_exporting requires torch>=2.7
print("<-", string_type((args, kwargs), with_shape=True, with_min_max=True))
res = _f(*args, **kwargs)
if not hasattr(torch.compiler, "is_exporting") or not torch.compiler.is_exporting():
print("->", string_type(res, with_shape=True, with_min_max=True))
return res
keep_model_forward = model.forward
model.forward = lambda *args, _f=keep_model_forward, **kwargs: _forward_(
*args, _f=_f, **kwargs
)
Let’s run the model.
prompt = "Continue: it rains..."
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(
inputs, max_length=50, temperature=1, top_k=50, top_p=0.95, do_sample=True
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("-- prompt", prompt)
print("-- answer", generated_text)
<- ((),dict(cache_position:T7s8[0,7:A3.5],past_key_values:DynamicCache(key_cache=#0[], value_cache=#0[]),input_ids:T7s1x8[1,29901:A6305.375],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x8x32000[-15.516718864440918,15.75765609741211:A-3.381915190983544],past_key_values:DynamicCache(key_cache=#1[T1s1x1x8x96[-5.490959167480469,6.226877689361572:A-0.11321351693110653]], value_cache=#1[T1s1x1x8x96[-0.6787744760513306,0.49568021297454834:A0.007227749521139988]]))
<- ((),dict(cache_position:T7s1[8,8:A8.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x8x96[-5.490959167480469,6.226877689361572:A-0.11321351693110653]], value_cache=#1[T1s1x1x8x96[-0.6787744760513306,0.49568021297454834:A0.007227749521139988]]),input_ids:T7s1x1[29871,29871:A29871.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-12.200005531311035,13.318134307861328:A-3.0123733444297685],past_key_values:DynamicCache(key_cache=#1[T1s1x1x9x96[-5.490959167480469,6.226877689361572:A-0.11562127664324685]], value_cache=#1[T1s1x1x9x96[-0.6787744760513306,0.49568021297454834:A0.002961578160045098]]))
<- ((),dict(cache_position:T7s1[9,9:A9.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x9x96[-5.490959167480469,6.226877689361572:A-0.11562127664324685]], value_cache=#1[T1s1x1x9x96[-0.6787744760513306,0.49568021297454834:A0.002961578160045098]]),input_ids:T7s1x1[29906,29906:A29906.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-20.586483001708984,4.537554740905762:A-10.816070999450982],past_key_values:DynamicCache(key_cache=#1[T1s1x1x10x96[-6.134088039398193,6.226877689361572:A-0.11618857977773586]], value_cache=#1[T1s1x1x10x96[-0.6787744760513306,0.49568021297454834:A0.002102570093954152]]))
<- ((),dict(cache_position:T7s1[10,10:A10.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x10x96[-6.134088039398193,6.226877689361572:A-0.11618857977773586]], value_cache=#1[T1s1x1x10x96[-0.6787744760513306,0.49568021297454834:A0.002102570093954152]]),input_ids:T7s1x1[29900,29900:A29900.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-20.911588668823242,4.599899768829346:A-11.469897256943398],past_key_values:DynamicCache(key_cache=#1[T1s1x1x11x96[-6.889054775238037,6.226877689361572:A-0.10172391123838183]], value_cache=#1[T1s1x1x11x96[-0.6787744760513306,0.49568021297454834:A-0.0008638351257352704]]))
<- ((),dict(cache_position:T7s1[11,11:A11.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x11x96[-6.889054775238037,6.226877689361572:A-0.10172391123838183]], value_cache=#1[T1s1x1x11x96[-0.6787744760513306,0.49568021297454834:A-0.0008638351257352704]]),input_ids:T7s1x1[29896,29896:A29896.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-16.23381996154785,11.618133544921875:A-8.618926791688427],past_key_values:DynamicCache(key_cache=#1[T1s1x1x12x96[-6.889054775238037,7.80775785446167:A-0.09017611285394701]], value_cache=#1[T1s1x1x12x96[-0.6787744760513306,0.49568021297454834:A-0.000995892527006011]]))
<- ((),dict(cache_position:T7s1[12,12:A12.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x12x96[-6.889054775238037,7.80775785446167:A-0.09017611285394701]], value_cache=#1[T1s1x1x12x96[-0.6787744760513306,0.49568021297454834:A-0.000995892527006011]]),input_ids:T7s1x1[29945,29945:A29945.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-20.31218719482422,3.0988643169403076:A-12.801840564569458],past_key_values:DynamicCache(key_cache=#1[T1s1x1x13x96[-6.889054775238037,7.80775785446167:A-0.07460625980522122]], value_cache=#1[T1s1x1x13x96[-0.6787744760513306,0.49568021297454834:A-0.0018652743326642034]]))
<- ((),dict(cache_position:T7s1[13,13:A13.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x13x96[-6.889054775238037,7.80775785446167:A-0.07460625980522122]], value_cache=#1[T1s1x1x13x96[-0.6787744760513306,0.49568021297454834:A-0.0018652743326642034]]),input_ids:T7s1x1[13,13:A13.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-8.535780906677246,9.496033668518066:A-3.0107516475531737],past_key_values:DynamicCache(key_cache=#1[T1s1x1x14x96[-6.889054775238037,7.80775785446167:A-0.08203760455069728]], value_cache=#1[T1s1x1x14x96[-0.6787744760513306,0.7704185843467712:A0.00028720200176784213]]))
<- ((),dict(cache_position:T7s1[14,14:A14.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x14x96[-6.889054775238037,7.80775785446167:A-0.08203760455069728]], value_cache=#1[T1s1x1x14x96[-0.6787744760513306,0.7704185843467712:A0.00028720200176784213]]),input_ids:T7s1x1[797,797:A797.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-16.181751251220703,6.713463306427002:A-7.234935389662161],past_key_values:DynamicCache(key_cache=#1[T1s1x1x15x96[-6.889054775238037,7.80775785446167:A-0.08197259105986127]], value_cache=#1[T1s1x1x15x96[-0.6787744760513306,0.7704185843467712:A-0.00017697135474211085]]))
<- ((),dict(cache_position:T7s1[15,15:A15.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x15x96[-6.889054775238037,7.80775785446167:A-0.08197259105986127]], value_cache=#1[T1s1x1x15x96[-0.6787744760513306,0.7704185843467712:A-0.00017697135474211085]]),input_ids:T7s1x1[263,263:A263.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-18.37200927734375,4.366279602050781:A-7.967071872619447],past_key_values:DynamicCache(key_cache=#1[T1s1x1x16x96[-6.889054775238037,7.80775785446167:A-0.08520186098113906]], value_cache=#1[T1s1x1x16x96[-0.6787744760513306,0.7704185843467712:A0.0008742369020543114]]))
<- ((),dict(cache_position:T7s1[16,16:A16.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x16x96[-6.889054775238037,7.80775785446167:A-0.08520186098113906]], value_cache=#1[T1s1x1x16x96[-0.6787744760513306,0.7704185843467712:A0.0008742369020543114]]),input_ids:T7s1x1[716,716:A716.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-19.772930145263672,3.6206936836242676:A-8.921781750202179],past_key_values:DynamicCache(key_cache=#1[T1s1x1x17x96[-6.889054775238037,7.80775785446167:A-0.0886952623449735]], value_cache=#1[T1s1x1x17x96[-0.6787744760513306,0.7704185843467712:A0.0007837815899923347]]))
<- ((),dict(cache_position:T7s1[17,17:A17.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x17x96[-6.889054775238037,7.80775785446167:A-0.0886952623449735]], value_cache=#1[T1s1x1x17x96[-0.6787744760513306,0.7704185843467712:A0.0007837815899923347]]),input_ids:T7s1x1[15593,15593:A15593.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-15.695737838745117,10.960634231567383:A-7.4099382205242295],past_key_values:DynamicCache(key_cache=#1[T1s1x1x18x96[-6.889054775238037,7.80775785446167:A-0.08336106764175012]], value_cache=#1[T1s1x1x18x96[-0.6787744760513306,0.7704185843467712:A0.0006955612630092757]]))
<- ((),dict(cache_position:T7s1[18,18:A18.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x18x96[-6.889054775238037,7.80775785446167:A-0.08336106764175012]], value_cache=#1[T1s1x1x18x96[-0.6787744760513306,0.7704185843467712:A0.0006955612630092757]]),input_ids:T7s1x1[515,515:A515.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-17.34794807434082,3.7818150520324707:A-10.320165206015576],past_key_values:DynamicCache(key_cache=#1[T1s1x1x19x96[-6.889054775238037,8.313292503356934:A-0.08279910333639189]], value_cache=#1[T1s1x1x19x96[-0.6787744760513306,0.7704185843467712:A0.00040719555755187463]]))
<- ((),dict(cache_position:T7s1[19,19:A19.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x19x96[-6.889054775238037,8.313292503356934:A-0.08279910333639189]], value_cache=#1[T1s1x1x19x96[-0.6787744760513306,0.7704185843467712:A0.00040719555755187463]]),input_ids:T7s1x1[278,278:A278.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-17.660898208618164,2.7139883041381836:A-9.061210543265567],past_key_values:DynamicCache(key_cache=#1[T1s1x1x20x96[-6.889054775238037,8.313292503356934:A-0.0834990618175046]], value_cache=#1[T1s1x1x20x96[-0.6787744760513306,0.7704185843467712:A0.00106996538469654]]))
<- ((),dict(cache_position:T7s1[20,20:A20.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x20x96[-6.889054775238037,8.313292503356934:A-0.0834990618175046]], value_cache=#1[T1s1x1x20x96[-0.6787744760513306,0.7704185843467712:A0.00106996538469654]]),input_ids:T7s1x1[10059,10059:A10059.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-19.330181121826172,5.414156436920166:A-9.971013845281675],past_key_values:DynamicCache(key_cache=#1[T1s1x1x21x96[-6.889054775238037,8.313292503356934:A-0.0808628409421732]], value_cache=#1[T1s1x1x21x96[-0.6787744760513306,0.7704185843467712:A0.0006638906451053839]]))
<- ((),dict(cache_position:T7s1[21,21:A21.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x21x96[-6.889054775238037,8.313292503356934:A-0.0808628409421732]], value_cache=#1[T1s1x1x21x96[-0.6787744760513306,0.7704185843467712:A0.0006638906451053839]]),input_ids:T7s1x1[390,390:A390.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-9.6025390625,14.172816276550293:A0.04168865215312689],past_key_values:DynamicCache(key_cache=#1[T1s1x1x22x96[-6.889054775238037,8.313292503356934:A-0.08319223000178295]], value_cache=#1[T1s1x1x22x96[-0.6787744760513306,0.7704185843467712:A0.0011100013767080588]]))
<- ((),dict(cache_position:T7s1[22,22:A22.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x22x96[-6.889054775238037,8.313292503356934:A-0.08319223000178295]], value_cache=#1[T1s1x1x22x96[-0.6787744760513306,0.7704185843467712:A0.0011100013767080588]]),input_ids:T7s1x1[13873,13873:A13873.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-18.0345458984375,4.507065773010254:A-9.40355803508684],past_key_values:DynamicCache(key_cache=#1[T1s1x1x23x96[-6.889054775238037,8.313292503356934:A-0.07736036775394746]], value_cache=#1[T1s1x1x23x96[-0.6787744760513306,0.7704185843467712:A0.00100597195562617]]))
<- ((),dict(cache_position:T7s1[23,23:A23.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x23x96[-6.889054775238037,8.313292503356934:A-0.07736036775394746]], value_cache=#1[T1s1x1x23x96[-0.6787744760513306,0.7704185843467712:A0.00100597195562617]]),input_ids:T7s1x1[322,322:A322.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-15.167494773864746,7.321971416473389:A-6.406562220249791],past_key_values:DynamicCache(key_cache=#1[T1s1x1x24x96[-6.889054775238037,8.313292503356934:A-0.07975757822850937]], value_cache=#1[T1s1x1x24x96[-0.6787744760513306,0.7704185843467712:A0.0005617271902072692]]))
<- ((),dict(cache_position:T7s1[24,24:A24.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x24x96[-6.889054775238037,8.313292503356934:A-0.07975757822850937]], value_cache=#1[T1s1x1x24x96[-0.6787744760513306,0.7704185843467712:A0.0005617271902072692]]),input_ids:T7s1x1[278,278:A278.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-14.755518913269043,5.158234596252441:A-6.608570245609153],past_key_values:DynamicCache(key_cache=#1[T1s1x1x25x96[-6.889054775238037,8.313292503356934:A-0.0836349297289174]], value_cache=#1[T1s1x1x25x96[-0.6787744760513306,0.7704185843467712:A0.0010857617866167858]]))
<- ((),dict(cache_position:T7s1[25,25:A25.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x25x96[-6.889054775238037,8.313292503356934:A-0.0836349297289174]], value_cache=#1[T1s1x1x25x96[-0.6787744760513306,0.7704185843467712:A0.0010857617866167858]]),input_ids:T7s1x1[1791,1791:A1791.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-11.993223190307617,14.889642715454102:A-3.3122239010294434],past_key_values:DynamicCache(key_cache=#1[T1s1x1x26x96[-6.889054775238037,8.313292503356934:A-0.08047150500253618]], value_cache=#1[T1s1x1x26x96[-0.6787744760513306,0.7704185843467712:A0.0007142034878889275]]))
<- ((),dict(cache_position:T7s1[26,26:A26.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x26x96[-6.889054775238037,8.313292503356934:A-0.08047150500253618]], value_cache=#1[T1s1x1x26x96[-0.6787744760513306,0.7704185843467712:A0.0007142034878889275]]),input_ids:T7s1x1[310,310:A310.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-17.586320877075195,9.162973403930664:A-8.866850411470281],past_key_values:DynamicCache(key_cache=#1[T1s1x1x27x96[-6.889054775238037,8.313292503356934:A-0.07684408922665889]], value_cache=#1[T1s1x1x27x96[-0.6787744760513306,0.7704185843467712:A0.0009532463969876124]]))
<- ((),dict(cache_position:T7s1[27,27:A27.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x27x96[-6.889054775238037,8.313292503356934:A-0.07684408922665889]], value_cache=#1[T1s1x1x27x96[-0.6787744760513306,0.7704185843467712:A0.0009532463969876124]]),input_ids:T7s1x1[278,278:A278.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-17.297277450561523,5.848733901977539:A-7.398837297641206],past_key_values:DynamicCache(key_cache=#1[T1s1x1x28x96[-6.889054775238037,8.313292503356934:A-0.07498764249937397]], value_cache=#1[T1s1x1x28x96[-0.6787744760513306,0.7704185843467712:A0.0014071516006825256]]))
<- ((),dict(cache_position:T7s1[28,28:A28.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x28x96[-6.889054775238037,8.313292503356934:A-0.07498764249937397]], value_cache=#1[T1s1x1x28x96[-0.6787744760513306,0.7704185843467712:A0.0014071516006825256]]),input_ids:T7s1x1[1510,1510:A1510.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-14.583517074584961,9.910909652709961:A-5.632623090738431],past_key_values:DynamicCache(key_cache=#1[T1s1x1x29x96[-6.889054775238037,8.313292503356934:A-0.06901649970355699]], value_cache=#1[T1s1x1x29x96[-0.6787744760513306,0.7704185843467712:A0.0006051383341390197]]))
<- ((),dict(cache_position:T7s1[29,29:A29.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x29x96[-6.889054775238037,8.313292503356934:A-0.06901649970355699]], value_cache=#1[T1s1x1x29x96[-0.6787744760513306,0.7704185843467712:A0.0006051383341390197]]),input_ids:T7s1x1[408,408:A408.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-14.443014144897461,8.125170707702637:A-5.422454376161099],past_key_values:DynamicCache(key_cache=#1[T1s1x1x30x96[-6.889054775238037,8.313292503356934:A-0.06529899162233051]], value_cache=#1[T1s1x1x30x96[-0.6787744760513306,0.7704185843467712:A0.0010467832619775032]]))
<- ((),dict(cache_position:T7s1[30,30:A30.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x30x96[-6.889054775238037,8.313292503356934:A-0.06529899162233051]], value_cache=#1[T1s1x1x30x96[-0.6787744760513306,0.7704185843467712:A0.0010467832619775032]]),input_ids:T7s1x1[278,278:A278.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-16.140138626098633,6.0859198570251465:A-6.6104045244310985],past_key_values:DynamicCache(key_cache=#1[T1s1x1x31x96[-6.889054775238037,8.313292503356934:A-0.06128143776571647]], value_cache=#1[T1s1x1x31x96[-0.6787744760513306,0.7704185843467712:A0.0014537448374119443]]))
<- ((),dict(cache_position:T7s1[31,31:A31.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x31x96[-6.889054775238037,8.313292503356934:A-0.06128143776571647]], value_cache=#1[T1s1x1x31x96[-0.6787744760513306,0.7704185843467712:A0.0014537448374119443]]),input_ids:T7s1x1[716,716:A716.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-20.811765670776367,3.611508846282959:A-8.24887487068586],past_key_values:DynamicCache(key_cache=#1[T1s1x1x32x96[-6.889054775238037,8.313292503356934:A-0.06097532617828468]], value_cache=#1[T1s1x1x32x96[-0.6787744760513306,0.7704185843467712:A0.0013875808298990933]]))
<- ((),dict(cache_position:T7s1[32,32:A32.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x32x96[-6.889054775238037,8.313292503356934:A-0.06097532617828468]], value_cache=#1[T1s1x1x32x96[-0.6787744760513306,0.7704185843467712:A0.0013875808298990933]]),input_ids:T7s1x1[6673,6673:A6673.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-16.10395050048828,6.696835517883301:A-8.499282572563738],past_key_values:DynamicCache(key_cache=#1[T1s1x1x33x96[-6.889054775238037,8.313292503356934:A-0.05654821477663871]], value_cache=#1[T1s1x1x33x96[-0.6787744760513306,0.7704185843467712:A0.0010210350696336654]]))
<- ((),dict(cache_position:T7s1[33,33:A33.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x33x96[-6.889054775238037,8.313292503356934:A-0.05654821477663871]], value_cache=#1[T1s1x1x33x96[-0.6787744760513306,0.7704185843467712:A0.0010210350696336654]]),input_ids:T7s1x1[310,310:A310.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-17.874752044677734,6.981978416442871:A-10.318285797805526],past_key_values:DynamicCache(key_cache=#1[T1s1x1x34x96[-6.889054775238037,8.313292503356934:A-0.05575674153816876]], value_cache=#1[T1s1x1x34x96[-0.6787744760513306,0.7704185843467712:A0.0012018388038665994]]))
<- ((),dict(cache_position:T7s1[34,34:A34.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x34x96[-6.889054775238037,8.313292503356934:A-0.05575674153816876]], value_cache=#1[T1s1x1x34x96[-0.6787744760513306,0.7704185843467712:A0.0012018388038665994]]),input_ids:T7s1x1[278,278:A278.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-17.786760330200195,4.774350166320801:A-8.626841848013225],past_key_values:DynamicCache(key_cache=#1[T1s1x1x35x96[-6.889054775238037,8.313292503356934:A-0.05483112523836012]], value_cache=#1[T1s1x1x35x96[-0.6787744760513306,0.7704185843467712:A0.0015578603266259874]]))
<- ((),dict(cache_position:T7s1[35,35:A35.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x35x96[-6.889054775238037,8.313292503356934:A-0.05483112523836012]], value_cache=#1[T1s1x1x35x96[-0.6787744760513306,0.7704185843467712:A0.0015578603266259874]]),input_ids:T7s1x1[3303,3303:A3303.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-19.781566619873047,8.825448989868164:A-9.946102178138215],past_key_values:DynamicCache(key_cache=#1[T1s1x1x36x96[-6.889054775238037,8.313292503356934:A-0.05320249750484099]], value_cache=#1[T1s1x1x36x96[-0.6787744760513306,0.7704185843467712:A0.00132377600806632]]))
<- ((),dict(cache_position:T7s1[36,36:A36.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x36x96[-6.889054775238037,8.313292503356934:A-0.05320249750484099]], value_cache=#1[T1s1x1x36x96[-0.6787744760513306,0.7704185843467712:A0.00132377600806632]]),input_ids:T7s1x1[3900,3900:A3900.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-20.112751007080078,6.211709976196289:A-10.294955048627687],past_key_values:DynamicCache(key_cache=#1[T1s1x1x37x96[-6.889054775238037,8.313292503356934:A-0.04830179690274694]], value_cache=#1[T1s1x1x37x96[-0.6787744760513306,0.7704185843467712:A0.001900057164130795]]))
<- ((),dict(cache_position:T7s1[37,37:A37.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x37x96[-6.889054775238037,8.313292503356934:A-0.04830179690274694]], value_cache=#1[T1s1x1x37x96[-0.6787744760513306,0.7704185843467712:A0.001900057164130795]]),input_ids:T7s1x1[29892,29892:A29892.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-16.048019409179688,8.138830184936523:A-7.2565812354758386],past_key_values:DynamicCache(key_cache=#1[T1s1x1x38x96[-6.889054775238037,8.313292503356934:A-0.04943213857114371]], value_cache=#1[T1s1x1x38x96[-0.6787744760513306,0.7704185843467712:A0.0022145933762299378]]))
<- ((),dict(cache_position:T7s1[38,38:A38.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x38x96[-6.889054775238037,8.313292503356934:A-0.04943213857114371]], value_cache=#1[T1s1x1x38x96[-0.6787744760513306,0.7704185843467712:A0.0022145933762299378]]),input_ids:T7s1x1[988,988:A988.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-18.39621353149414,6.714192867279053:A-8.594809377158061],past_key_values:DynamicCache(key_cache=#1[T1s1x1x39x96[-6.889054775238037,8.313292503356934:A-0.044657828196584454]], value_cache=#1[T1s1x1x39x96[-0.6787744760513306,0.7704185843467712:A0.0027604635280175332]]))
<- ((),dict(cache_position:T7s1[39,39:A39.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x39x96[-6.889054775238037,8.313292503356934:A-0.044657828196584454]], value_cache=#1[T1s1x1x39x96[-0.6787744760513306,0.7704185843467712:A0.0027604635280175332]]),input_ids:T7s1x1[540,540:A540.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-16.49686050415039,10.166898727416992:A-4.7641984540976114],past_key_values:DynamicCache(key_cache=#1[T1s1x1x40x96[-6.889054775238037,8.313292503356934:A-0.043682278613505335]], value_cache=#1[T1s1x1x40x96[-0.6787744760513306,0.7704185843467712:A0.0025552720869162233]]))
<- ((),dict(cache_position:T7s1[40,40:A40.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x40x96[-6.889054775238037,8.313292503356934:A-0.043682278613505335]], value_cache=#1[T1s1x1x40x96[-0.6787744760513306,0.7704185843467712:A0.0025552720869162233]]),input_ids:T7s1x1[338,338:A338.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-15.731707572937012,7.470067977905273:A-6.960792868179968],past_key_values:DynamicCache(key_cache=#1[T1s1x1x41x96[-6.889054775238037,8.313292503356934:A-0.04205607225567957]], value_cache=#1[T1s1x1x41x96[-0.6787744760513306,0.7704185843467712:A0.00257631501830755]]))
<- ((),dict(cache_position:T7s1[41,41:A41.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x41x96[-6.889054775238037,8.313292503356934:A-0.04205607225567957]], value_cache=#1[T1s1x1x41x96[-0.6787744760513306,0.7704185843467712:A0.00257631501830755]]),input_ids:T7s1x1[373,373:A373.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-18.593360900878906,7.08710241317749:A-9.289329325654544],past_key_values:DynamicCache(key_cache=#1[T1s1x1x42x96[-6.889054775238037,8.313292503356934:A-0.03847835137954491]], value_cache=#1[T1s1x1x42x96[-0.6787744760513306,0.7704185843467712:A0.0020502728413436077]]))
<- ((),dict(cache_position:T7s1[42,42:A42.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x42x96[-6.889054775238037,8.313292503356934:A-0.03847835137954491]], value_cache=#1[T1s1x1x42x96[-0.6787744760513306,0.7704185843467712:A0.0020502728413436077]]),input_ids:T7s1x1[278,278:A278.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-17.142427444458008,4.84787654876709:A-7.669538961884566],past_key_values:DynamicCache(key_cache=#1[T1s1x1x43x96[-6.889054775238037,8.313292503356934:A-0.03644608427772579]], value_cache=#1[T1s1x1x43x96[-0.6787744760513306,0.7704185843467712:A0.002320326777601784]]))
<- ((),dict(cache_position:T7s1[43,43:A43.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x43x96[-6.889054775238037,8.313292503356934:A-0.03644608427772579]], value_cache=#1[T1s1x1x43x96[-0.6787744760513306,0.7704185843467712:A0.002320326777601784]]),input_ids:T7s1x1[14451,14451:A14451.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-19.24009132385254,9.010613441467285:A-9.13139057582058],past_key_values:DynamicCache(key_cache=#1[T1s1x1x44x96[-6.889054775238037,8.313292503356934:A-0.033572797414225614]], value_cache=#1[T1s1x1x44x96[-0.6787744760513306,0.7704185843467712:A0.002116824136874818]]))
<- ((),dict(cache_position:T7s1[44,44:A44.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x44x96[-6.889054775238037,8.313292503356934:A-0.033572797414225614]], value_cache=#1[T1s1x1x44x96[-0.6787744760513306,0.7704185843467712:A0.002116824136874818]]),input_ids:T7s1x1[304,304:A304.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-18.65241050720215,6.762457370758057:A-8.737269920860882],past_key_values:DynamicCache(key_cache=#1[T1s1x1x45x96[-6.889054775238037,8.313292503356934:A-0.031952815897265854]], value_cache=#1[T1s1x1x45x96[-0.6787744760513306,0.7704185843467712:A0.0024474782390320293]]))
<- ((),dict(cache_position:T7s1[45,45:A45.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x45x96[-6.889054775238037,8.313292503356934:A-0.031952815897265854]], value_cache=#1[T1s1x1x45x96[-0.6787744760513306,0.7704185843467712:A0.0024474782390320293]]),input_ids:T7s1x1[278,278:A278.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-16.6575870513916,5.5629472732543945:A-7.0177001177384515],past_key_values:DynamicCache(key_cache=#1[T1s1x1x46x96[-6.889054775238037,8.313292503356934:A-0.029740042597568128]], value_cache=#1[T1s1x1x46x96[-0.6787744760513306,0.7704185843467712:A0.002691285062106228]]))
<- ((),dict(cache_position:T7s1[46,46:A46.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x46x96[-6.889054775238037,8.313292503356934:A-0.029740042597568128]], value_cache=#1[T1s1x1x46x96[-0.6787744760513306,0.7704185843467712:A0.002691285062106228]]),input_ids:T7s1x1[970,970:A970.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-17.061763763427734,10.011451721191406:A-6.619339906038483],past_key_values:DynamicCache(key_cache=#1[T1s1x1x47x96[-6.889054775238037,8.313292503356934:A-0.029412733558816167]], value_cache=#1[T1s1x1x47x96[-0.6787744760513306,0.7704185843467712:A0.001974078777864453]]))
<- ((),dict(cache_position:T7s1[47,47:A47.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x47x96[-6.889054775238037,8.313292503356934:A-0.029412733558816167]], value_cache=#1[T1s1x1x47x96[-0.6787744760513306,0.7704185843467712:A0.001974078777864453]]),input_ids:T7s1x1[29889,29889:A29889.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-16.40967559814453,8.257579803466797:A-8.369471787367017],past_key_values:DynamicCache(key_cache=#1[T1s1x1x48x96[-6.889054775238037,8.313292503356934:A-0.030028530776765667]], value_cache=#1[T1s1x1x48x96[-0.6787744760513306,0.7704185843467712:A0.002173969200735352]]))
<- ((),dict(cache_position:T7s1[48,48:A48.0],past_key_values:DynamicCache(key_cache=#1[T1s1x1x48x96[-6.889054775238037,8.313292503356934:A-0.030028530776765667]], value_cache=#1[T1s1x1x48x96[-0.6787744760513306,0.7704185843467712:A0.002173969200735352]]),input_ids:T7s1x1[940,940:A940.0],inputs_embeds:None,use_cache:bool=True,return_dict:bool=True))
-> CausalLMOutputWithPast(logits:T1s1x1x32000[-15.901311874389648,12.435640335083008:A-3.747984215274686],past_key_values:DynamicCache(key_cache=#1[T1s1x1x49x96[-6.889054775238037,8.313292503356934:A-0.029988411581415967]], value_cache=#1[T1s1x1x49x96[-0.6787744760513306,0.7704185843467712:A0.0021623855351103295]]))
-- prompt Continue: it rains...
-- answer Continue: it rains... 2015
In a new interview from the Chicago Rangers and the rest of the show as the new president of the United States, where he is on the rise to the public. He says
Let’s restore the forward as it was.
model.forward = keep_model_forward
Another syntax with onnx_diagnostic.helpers.torch_test_helper.steal_forward().
with steal_forward(model):
model.generate(inputs, max_length=50, temperature=1, top_k=50, top_p=0.95, do_sample=True)
---- stolen forward for class LlamaForCausalLM -- iteration 0
<- args=() --- kwargs=dict(cache_position:T7s8,past_key_values:DynamicCache(key_cache=#0[], value_cache=#0[]),input_ids:T7s1x8,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x8x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x8x96], value_cache=#1[T1s1x1x8x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 1
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x8x96], value_cache=#1[T1s1x1x8x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x9x96], value_cache=#1[T1s1x1x9x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 2
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x9x96], value_cache=#1[T1s1x1x9x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x10x96], value_cache=#1[T1s1x1x10x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 3
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x10x96], value_cache=#1[T1s1x1x10x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x11x96], value_cache=#1[T1s1x1x11x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 4
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x11x96], value_cache=#1[T1s1x1x11x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x12x96], value_cache=#1[T1s1x1x12x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 5
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x12x96], value_cache=#1[T1s1x1x12x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x13x96], value_cache=#1[T1s1x1x13x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 6
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x13x96], value_cache=#1[T1s1x1x13x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x14x96], value_cache=#1[T1s1x1x14x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 7
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x14x96], value_cache=#1[T1s1x1x14x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x15x96], value_cache=#1[T1s1x1x15x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 8
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x15x96], value_cache=#1[T1s1x1x15x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x16x96], value_cache=#1[T1s1x1x16x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 9
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x16x96], value_cache=#1[T1s1x1x16x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x17x96], value_cache=#1[T1s1x1x17x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 10
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x17x96], value_cache=#1[T1s1x1x17x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x18x96], value_cache=#1[T1s1x1x18x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 11
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x18x96], value_cache=#1[T1s1x1x18x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x19x96], value_cache=#1[T1s1x1x19x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 12
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x19x96], value_cache=#1[T1s1x1x19x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x20x96], value_cache=#1[T1s1x1x20x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 13
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x20x96], value_cache=#1[T1s1x1x20x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x21x96], value_cache=#1[T1s1x1x21x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 14
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x21x96], value_cache=#1[T1s1x1x21x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x22x96], value_cache=#1[T1s1x1x22x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 15
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x22x96], value_cache=#1[T1s1x1x22x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x23x96], value_cache=#1[T1s1x1x23x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 16
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x23x96], value_cache=#1[T1s1x1x23x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x24x96], value_cache=#1[T1s1x1x24x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 17
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x24x96], value_cache=#1[T1s1x1x24x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x25x96], value_cache=#1[T1s1x1x25x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 18
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x25x96], value_cache=#1[T1s1x1x25x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x26x96], value_cache=#1[T1s1x1x26x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 19
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x26x96], value_cache=#1[T1s1x1x26x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x27x96], value_cache=#1[T1s1x1x27x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 20
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x27x96], value_cache=#1[T1s1x1x27x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x28x96], value_cache=#1[T1s1x1x28x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 21
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x28x96], value_cache=#1[T1s1x1x28x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x29x96], value_cache=#1[T1s1x1x29x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 22
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x29x96], value_cache=#1[T1s1x1x29x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x30x96], value_cache=#1[T1s1x1x30x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 23
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x30x96], value_cache=#1[T1s1x1x30x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x31x96], value_cache=#1[T1s1x1x31x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 24
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x31x96], value_cache=#1[T1s1x1x31x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x32x96], value_cache=#1[T1s1x1x32x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 25
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x32x96], value_cache=#1[T1s1x1x32x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x33x96], value_cache=#1[T1s1x1x33x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 26
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x33x96], value_cache=#1[T1s1x1x33x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x34x96], value_cache=#1[T1s1x1x34x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 27
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x34x96], value_cache=#1[T1s1x1x34x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x35x96], value_cache=#1[T1s1x1x35x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 28
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x35x96], value_cache=#1[T1s1x1x35x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x36x96], value_cache=#1[T1s1x1x36x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 29
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x36x96], value_cache=#1[T1s1x1x36x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x37x96], value_cache=#1[T1s1x1x37x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 30
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x37x96], value_cache=#1[T1s1x1x37x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x38x96], value_cache=#1[T1s1x1x38x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 31
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x38x96], value_cache=#1[T1s1x1x38x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x39x96], value_cache=#1[T1s1x1x39x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 32
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x39x96], value_cache=#1[T1s1x1x39x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x40x96], value_cache=#1[T1s1x1x40x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 33
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x40x96], value_cache=#1[T1s1x1x40x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x41x96], value_cache=#1[T1s1x1x41x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 34
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x41x96], value_cache=#1[T1s1x1x41x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x42x96], value_cache=#1[T1s1x1x42x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 35
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x42x96], value_cache=#1[T1s1x1x42x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x43x96], value_cache=#1[T1s1x1x43x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 36
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x43x96], value_cache=#1[T1s1x1x43x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x44x96], value_cache=#1[T1s1x1x44x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 37
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x44x96], value_cache=#1[T1s1x1x44x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x45x96], value_cache=#1[T1s1x1x45x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 38
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x45x96], value_cache=#1[T1s1x1x45x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x46x96], value_cache=#1[T1s1x1x46x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 39
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x46x96], value_cache=#1[T1s1x1x46x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x47x96], value_cache=#1[T1s1x1x47x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 40
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x47x96], value_cache=#1[T1s1x1x47x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x48x96], value_cache=#1[T1s1x1x48x96]))
.
---- stolen forward for class LlamaForCausalLM -- iteration 41
<- args=() --- kwargs=dict(cache_position:T7s1,past_key_values:DynamicCache(key_cache=#1[T1s1x1x48x96], value_cache=#1[T1s1x1x48x96]),input_ids:T7s1x1,inputs_embeds:None,use_cache:bool,return_dict:bool)
--
-> CausalLMOutputWithPast(logits:T1s1x1x32000,past_key_values:DynamicCache(key_cache=#1[T1s1x1x49x96], value_cache=#1[T1s1x1x49x96]))
.
Untrained model¶
This part can skipped if you are only interested in exporting the original model. It is useful to create a unit test to ensure a specific architecture can be exported despite the many changes brought to torch or transformers.
Let’s create an untrained model using the config file provided
config.json
to create an untrained model:
onnx_diagnostic.torch_models.llms.get_tiny_llm().
Then let’s use it.
experiment = get_tiny_llm()
untrained_model, inputs, dynamic_shapes = (
experiment["model"],
experiment["inputs"],
experiment["dynamic_shapes"],
)
Before we run it, we make a copy of the inputs as the cache get modified by the execution. Then it is no longer valid associated with the previous input_ids and mask.
print("input type before", string_type(inputs, with_shape=True))
expected_output = untrained_model(**inputs)
print("input type after-", string_type(inputs, with_shape=True))
input type before dict(input_ids:T7s2x3,attention_mask:T7s2x33,position_ids:T7s2x3,past_key_values:DynamicCache(key_cache=#1[T1s2x1x30x96], value_cache=#1[T1s2x1x30x96]))
input type after- dict(input_ids:T7s2x3,attention_mask:T7s2x33,position_ids:T7s2x3,past_key_values:DynamicCache(key_cache=#1[T1s2x1x33x96], value_cache=#1[T1s2x1x33x96]))
The outputs
print("result type", string_type(expected_output, with_shape=True))
result type CausalLMOutputWithPast(logits:T1s2x3x32000,past_key_values:DynamicCache(key_cache=#1[T1s2x1x33x96], value_cache=#1[T1s2x1x33x96]))
It works.
ExportedProgram¶
try:
ep = torch.export.export(
untrained_model, (), kwargs=cloned_inputs, dynamic_shapes=dynamic_shapes, strict=False
)
print("It worked:")
print(ep)
except Exception as e:
# To work, it needs at least PRs:
# * https://github.com/huggingface/transformers/pull/36311
# * https://github.com/huggingface/transformers/pull/36652
print("It failed:", e)
It worked:
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, p_model_embed_tokens_weight: "f32[32000, 192]", p_model_layers_0_self_attn_q_proj_weight: "f32[192, 192]", p_model_layers_0_self_attn_k_proj_weight: "f32[96, 192]", p_model_layers_0_self_attn_v_proj_weight: "f32[96, 192]", p_model_layers_0_self_attn_o_proj_weight: "f32[192, 192]", p_model_layers_0_mlp_gate_proj_weight: "f32[1024, 192]", p_model_layers_0_mlp_up_proj_weight: "f32[1024, 192]", p_model_layers_0_mlp_down_proj_weight: "f32[192, 1024]", p_model_layers_0_input_layernorm_weight: "f32[192]", p_model_layers_0_post_attention_layernorm_weight: "f32[192]", p_model_norm_weight: "f32[192]", p_lm_head_weight: "f32[32000, 192]", b_model_rotary_emb_inv_freq: "f32[48]", input_ids: "i64[s41, s2]", attention_mask: "i64[s41, s2 + s67]", position_ids: "i64[s41, s2]", past_key_values_key_cache_0: "f32[s41, 1, s67, 96]", past_key_values_value_cache_0: "f32[s41, 1, s67, 96]"):
#
sym_size_int_22: "Sym(s41)" = torch.ops.aten.sym_size.int(input_ids, 0)
sym_size_int_23: "Sym(s2)" = torch.ops.aten.sym_size.int(input_ids, 1)
sym_size_int_24: "Sym(s67)" = torch.ops.aten.sym_size.int(past_key_values_key_cache_0, 2)
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/sparse.py:190 in forward, code: return F.embedding(
embedding: "f32[s41, s2, 192]" = torch.ops.aten.embedding.default(p_model_embed_tokens_weight, input_ids); p_model_embed_tokens_weight = input_ids = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:542 in forward, code: past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
add: "Sym(s2 + s67)" = sym_size_int_24 + sym_size_int_23
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:541 in forward, code: cache_position = torch.arange(
arange: "i64[s2]" = torch.ops.aten.arange.start(sym_size_int_24, add, device = device(type='cpu'), pin_memory = False); sym_size_int_24 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:548 in forward, code: causal_mask = self._update_causal_mask(
full: "f32[s2, s2 + s67]" = torch.ops.aten.full.default([sym_size_int_23, add], -3.4028234663852886e+38, dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
triu: "f32[s2, s2 + s67]" = torch.ops.aten.triu.default(full, 1); full = None
arange_1: "i64[s2 + s67]" = torch.ops.aten.arange.default(add, device = device(type='cpu'), pin_memory = False)
reshape: "i64[s2, 1]" = torch.ops.aten.reshape.default(arange, [-1, 1]); arange = None
gt: "b8[s2, s2 + s67]" = torch.ops.aten.gt.Tensor(arange_1, reshape); arange_1 = reshape = None
mul_: "f32[s2, s2 + s67]" = torch.ops.aten.mul_.Tensor(triu, gt); triu = gt = None
unsqueeze: "f32[1, s2, s2 + s67]" = torch.ops.aten.unsqueeze.default(mul_, 0); mul_ = None
unsqueeze_1: "f32[1, 1, s2, s2 + s67]" = torch.ops.aten.unsqueeze.default(unsqueeze, 1); unsqueeze = None
slice_1: "f32[1, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(unsqueeze_1, 2, 0, 9223372036854775807); unsqueeze_1 = None
slice_2: "f32[1, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_1, 3, 0, 9223372036854775807); slice_1 = None
expand: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.expand.default(slice_2, [sym_size_int_22, 1, -1, -1]); slice_2 = None
clone: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.clone.default(expand); expand = None
slice_3: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(clone)
slice_4: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_3, 1); slice_3 = None
slice_5: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_4, 2); slice_4 = None
slice_6: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_5, 3, None, add); slice_5 = None
slice_7: "i64[s41, s2 + s67]" = torch.ops.aten.slice.Tensor(attention_mask, 0, 0, 9223372036854775807); attention_mask = None
unsqueeze_2: "i64[s41, 1, s2 + s67]" = torch.ops.aten.unsqueeze.default(slice_7, 1); slice_7 = None
unsqueeze_3: "i64[s41, 1, 1, s2 + s67]" = torch.ops.aten.unsqueeze.default(unsqueeze_2, 2); unsqueeze_2 = None
slice_8: "i64[s41, 1, 1, s2 + s67]" = torch.ops.aten.slice.Tensor(unsqueeze_3, 3, 0, 9223372036854775807); unsqueeze_3 = None
_assert_tensor_metadata_default = torch.ops.aten._assert_tensor_metadata.default(slice_8, dtype = torch.int64, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default = None
to: "i64[s41, 1, 1, s2 + s67]" = torch.ops.aten.to.dtype_layout(slice_8, dtype = torch.int64, layout = torch.strided, device = device(type='cpu')); slice_8 = None
add_2: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.add.Tensor(slice_6, to); slice_6 = to = None
eq_4: "b8[s41, 1, s2, s2 + s67]" = torch.ops.aten.eq.Scalar(add_2, 0); add_2 = None
slice_9: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(clone)
slice_10: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_9, 1); slice_9 = None
slice_11: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_10, 2); slice_10 = None
slice_12: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_11, 3, None, add); slice_11 = None
masked_fill: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.masked_fill.Scalar(slice_12, eq_4, -3.4028234663852886e+38); slice_12 = eq_4 = None
slice_13: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(clone, 0, 0, 9223372036854775807)
slice_14: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_13, 1, 0, 9223372036854775807); slice_13 = None
slice_15: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_14, 2, 0, 9223372036854775807); slice_14 = None
copy_: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.copy_.default(slice_15, masked_fill); slice_15 = masked_fill = copy_ = None
# No stacktrace found for following nodes
submod_3 = self.submod_1
wrap_with_set_grad_enabled = torch.ops.higher_order.wrap_with_set_grad_enabled(False, submod_3, b_model_rotary_emb_inv_freq, sym_size_int_22, position_ids); submod_3 = b_model_rotary_emb_inv_freq = position_ids = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:125 in forward, code: return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
to_6: "f32[s41, s2, 96]" = wrap_with_set_grad_enabled[0]
to_7: "f32[s41, s2, 96]" = wrap_with_set_grad_enabled[1]; wrap_with_set_grad_enabled = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:82 in forward, code: hidden_states = hidden_states.to(torch.float32)
_assert_tensor_metadata_default_8 = torch.ops.aten._assert_tensor_metadata.default(embedding, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_8 = None
to_8: "f32[s41, s2, 192]" = torch.ops.aten.to.dtype(embedding, torch.float32); embedding = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:83 in forward, code: variance = hidden_states.pow(2).mean(-1, keepdim=True)
pow_1: "f32[s41, s2, 192]" = torch.ops.aten.pow.Tensor_Scalar(to_8, 2)
mean: "f32[s41, s2, 1]" = torch.ops.aten.mean.dim(pow_1, [-1], True); pow_1 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:84 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
add_3: "f32[s41, s2, 1]" = torch.ops.aten.add.Tensor(mean, 1e-05); mean = None
rsqrt: "f32[s41, s2, 1]" = torch.ops.aten.rsqrt.default(add_3); add_3 = None
mul_2: "f32[s41, s2, 192]" = torch.ops.aten.mul.Tensor(to_8, rsqrt); rsqrt = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:85 in forward, code: return self.weight * hidden_states.to(input_dtype)
_assert_tensor_metadata_default_9 = torch.ops.aten._assert_tensor_metadata.default(mul_2, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_9 = None
to_9: "f32[s41, s2, 192]" = torch.ops.aten.to.dtype(mul_2, torch.float32); mul_2 = None
mul_3: "f32[s41, s2, 192]" = torch.ops.aten.mul.Tensor(p_model_layers_0_input_layernorm_weight, to_9); p_model_layers_0_input_layernorm_weight = to_9 = None
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear: "f32[s41, s2, 192]" = torch.ops.aten.linear.default(mul_3, p_model_layers_0_self_attn_q_proj_weight); p_model_layers_0_self_attn_q_proj_weight = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:254 in forward, code: query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
view: "f32[s41, s2, 2, 96]" = torch.ops.aten.view.default(linear, [sym_size_int_22, sym_size_int_23, -1, 96]); linear = None
transpose_1: "f32[s41, 2, s2, 96]" = torch.ops.aten.transpose.int(view, 1, 2); view = None
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear_1: "f32[s41, s2, 96]" = torch.ops.aten.linear.default(mul_3, p_model_layers_0_self_attn_k_proj_weight); p_model_layers_0_self_attn_k_proj_weight = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:255 in forward, code: key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
view_1: "f32[s41, s2, 1, 96]" = torch.ops.aten.view.default(linear_1, [sym_size_int_22, sym_size_int_23, -1, 96]); linear_1 = None
transpose_2: "f32[s41, 1, s2, 96]" = torch.ops.aten.transpose.int(view_1, 1, 2); view_1 = None
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear_2: "f32[s41, s2, 96]" = torch.ops.aten.linear.default(mul_3, p_model_layers_0_self_attn_v_proj_weight); mul_3 = p_model_layers_0_self_attn_v_proj_weight = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:256 in forward, code: value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
view_2: "f32[s41, s2, 1, 96]" = torch.ops.aten.view.default(linear_2, [sym_size_int_22, sym_size_int_23, -1, 96]); linear_2 = None
transpose_3: "f32[s41, 1, s2, 96]" = torch.ops.aten.transpose.int(view_2, 1, 2); view_2 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:259 in forward, code: query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
unsqueeze_7: "f32[s41, 1, s2, 96]" = torch.ops.aten.unsqueeze.default(to_6, 1); to_6 = None
unsqueeze_8: "f32[s41, 1, s2, 96]" = torch.ops.aten.unsqueeze.default(to_7, 1); to_7 = None
mul_4: "f32[s41, 2, s2, 96]" = torch.ops.aten.mul.Tensor(transpose_1, unsqueeze_7)
slice_19: "f32[s41, 2, s2, 48]" = torch.ops.aten.slice.Tensor(transpose_1, 3, 0, 48)
slice_20: "f32[s41, 2, s2, 48]" = torch.ops.aten.slice.Tensor(transpose_1, 3, 48, 9223372036854775807); transpose_1 = None
neg: "f32[s41, 2, s2, 48]" = torch.ops.aten.neg.default(slice_20); slice_20 = None
cat_1: "f32[s41, 2, s2, 96]" = torch.ops.aten.cat.default([neg, slice_19], -1); neg = slice_19 = None
mul_5: "f32[s41, 2, s2, 96]" = torch.ops.aten.mul.Tensor(cat_1, unsqueeze_8); cat_1 = None
add_4: "f32[s41, 2, s2, 96]" = torch.ops.aten.add.Tensor(mul_4, mul_5); mul_4 = mul_5 = None
mul_6: "f32[s41, 1, s2, 96]" = torch.ops.aten.mul.Tensor(transpose_2, unsqueeze_7); unsqueeze_7 = None
slice_21: "f32[s41, 1, s2, 48]" = torch.ops.aten.slice.Tensor(transpose_2, 3, 0, 48)
slice_22: "f32[s41, 1, s2, 48]" = torch.ops.aten.slice.Tensor(transpose_2, 3, 48, 9223372036854775807); transpose_2 = None
neg_1: "f32[s41, 1, s2, 48]" = torch.ops.aten.neg.default(slice_22); slice_22 = None
cat_2: "f32[s41, 1, s2, 96]" = torch.ops.aten.cat.default([neg_1, slice_21], -1); neg_1 = slice_21 = None
mul_7: "f32[s41, 1, s2, 96]" = torch.ops.aten.mul.Tensor(cat_2, unsqueeze_8); cat_2 = unsqueeze_8 = None
add_5: "f32[s41, 1, s2, 96]" = torch.ops.aten.add.Tensor(mul_6, mul_7); mul_6 = mul_7 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:264 in forward, code: key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
cat_3: "f32[s41, 1, s2 + s67, 96]" = torch.ops.aten.cat.default([past_key_values_key_cache_0, add_5], -2); past_key_values_key_cache_0 = add_5 = None
cat_4: "f32[s41, 1, s2 + s67, 96]" = torch.ops.aten.cat.default([past_key_values_value_cache_0, transpose_3], -2); past_key_values_value_cache_0 = transpose_3 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:277 in forward, code: attn_output, attn_weights = attention_interface(
slice_23: "f32[s41, 1, s2 + s67, 96]" = torch.ops.aten.slice.Tensor(cat_3, 0, 0, 9223372036854775807)
slice_24: "f32[s41, 1, s2 + s67, 96]" = torch.ops.aten.slice.Tensor(slice_23, 1, 0, 9223372036854775807); slice_23 = None
unsqueeze_9: "f32[s41, 1, 1, s2 + s67, 96]" = torch.ops.aten.unsqueeze.default(slice_24, 2); slice_24 = None
slice_25: "f32[s41, 1, 1, s2 + s67, 96]" = torch.ops.aten.slice.Tensor(unsqueeze_9, 3, 0, 9223372036854775807); unsqueeze_9 = None
slice_26: "f32[s41, 1, 1, s2 + s67, 96]" = torch.ops.aten.slice.Tensor(slice_25, 4, 0, 9223372036854775807); slice_25 = None
expand_2: "f32[s41, 1, 2, s2 + s67, 96]" = torch.ops.aten.expand.default(slice_26, [sym_size_int_22, 1, 2, add, 96]); slice_26 = None
reshape_1: "f32[s41, 2, s2 + s67, 96]" = torch.ops.aten.reshape.default(expand_2, [sym_size_int_22, 2, add, 96]); expand_2 = None
slice_27: "f32[s41, 1, s2 + s67, 96]" = torch.ops.aten.slice.Tensor(cat_4, 0, 0, 9223372036854775807)
slice_28: "f32[s41, 1, s2 + s67, 96]" = torch.ops.aten.slice.Tensor(slice_27, 1, 0, 9223372036854775807); slice_27 = None
unsqueeze_10: "f32[s41, 1, 1, s2 + s67, 96]" = torch.ops.aten.unsqueeze.default(slice_28, 2); slice_28 = None
slice_29: "f32[s41, 1, 1, s2 + s67, 96]" = torch.ops.aten.slice.Tensor(unsqueeze_10, 3, 0, 9223372036854775807); unsqueeze_10 = None
slice_30: "f32[s41, 1, 1, s2 + s67, 96]" = torch.ops.aten.slice.Tensor(slice_29, 4, 0, 9223372036854775807); slice_29 = None
expand_3: "f32[s41, 1, 2, s2 + s67, 96]" = torch.ops.aten.expand.default(slice_30, [sym_size_int_22, 1, 2, add, 96]); slice_30 = None
reshape_2: "f32[s41, 2, s2 + s67, 96]" = torch.ops.aten.reshape.default(expand_3, [sym_size_int_22, 2, add, 96]); expand_3 = None
slice_31: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(clone); clone = None
slice_32: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_31, 1); slice_31 = None
slice_33: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_32, 2); slice_32 = None
slice_34: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_33, 3, None, add); slice_33 = add = None
contiguous: "f32[s41, 2, s2, 96]" = torch.ops.aten.contiguous.default(add_4); add_4 = None
contiguous_1: "f32[s41, 2, s2 + s67, 96]" = torch.ops.aten.contiguous.default(reshape_1); reshape_1 = None
contiguous_2: "f32[s41, 2, s2 + s67, 96]" = torch.ops.aten.contiguous.default(reshape_2); reshape_2 = None
scaled_dot_product_attention: "f32[s41, 2, s2, 96]" = torch.ops.aten.scaled_dot_product_attention.default(contiguous, contiguous_1, contiguous_2, slice_34, scale = 0.10206207261596575); contiguous = contiguous_1 = contiguous_2 = slice_34 = None
transpose_4: "f32[s41, s2, 2, 96]" = torch.ops.aten.transpose.int(scaled_dot_product_attention, 1, 2); scaled_dot_product_attention = None
contiguous_3: "f32[s41, s2, 2, 96]" = torch.ops.aten.contiguous.default(transpose_4); transpose_4 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:288 in forward, code: attn_output = attn_output.reshape(*input_shape, -1).contiguous()
reshape_3: "f32[s41, s2, 192]" = torch.ops.aten.reshape.default(contiguous_3, [sym_size_int_22, sym_size_int_23, -1]); contiguous_3 = sym_size_int_22 = sym_size_int_23 = None
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear_3: "f32[s41, s2, 192]" = torch.ops.aten.linear.default(reshape_3, p_model_layers_0_self_attn_o_proj_weight); reshape_3 = p_model_layers_0_self_attn_o_proj_weight = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:331 in forward, code: hidden_states = residual + hidden_states
add_7: "f32[s41, s2, 192]" = torch.ops.aten.add.Tensor(to_8, linear_3); to_8 = linear_3 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:82 in forward, code: hidden_states = hidden_states.to(torch.float32)
_assert_tensor_metadata_default_10 = torch.ops.aten._assert_tensor_metadata.default(add_7, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_10 = None
to_10: "f32[s41, s2, 192]" = torch.ops.aten.to.dtype(add_7, torch.float32); add_7 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:83 in forward, code: variance = hidden_states.pow(2).mean(-1, keepdim=True)
pow_2: "f32[s41, s2, 192]" = torch.ops.aten.pow.Tensor_Scalar(to_10, 2)
mean_1: "f32[s41, s2, 1]" = torch.ops.aten.mean.dim(pow_2, [-1], True); pow_2 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:84 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
add_8: "f32[s41, s2, 1]" = torch.ops.aten.add.Tensor(mean_1, 1e-05); mean_1 = None
rsqrt_1: "f32[s41, s2, 1]" = torch.ops.aten.rsqrt.default(add_8); add_8 = None
mul_8: "f32[s41, s2, 192]" = torch.ops.aten.mul.Tensor(to_10, rsqrt_1); rsqrt_1 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:85 in forward, code: return self.weight * hidden_states.to(input_dtype)
_assert_tensor_metadata_default_11 = torch.ops.aten._assert_tensor_metadata.default(mul_8, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_11 = None
to_11: "f32[s41, s2, 192]" = torch.ops.aten.to.dtype(mul_8, torch.float32); mul_8 = None
mul_9: "f32[s41, s2, 192]" = torch.ops.aten.mul.Tensor(p_model_layers_0_post_attention_layernorm_weight, to_11); p_model_layers_0_post_attention_layernorm_weight = to_11 = None
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear_4: "f32[s41, s2, 1024]" = torch.ops.aten.linear.default(mul_9, p_model_layers_0_mlp_gate_proj_weight); p_model_layers_0_mlp_gate_proj_weight = None
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/activation.py:434 in forward, code: return F.silu(input, inplace=self.inplace)
silu: "f32[s41, s2, 1024]" = torch.ops.aten.silu.default(linear_4); linear_4 = None
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear_5: "f32[s41, s2, 1024]" = torch.ops.aten.linear.default(mul_9, p_model_layers_0_mlp_up_proj_weight); mul_9 = p_model_layers_0_mlp_up_proj_weight = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:174 in forward, code: down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
mul_10: "f32[s41, s2, 1024]" = torch.ops.aten.mul.Tensor(silu, linear_5); silu = linear_5 = None
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear_6: "f32[s41, s2, 192]" = torch.ops.aten.linear.default(mul_10, p_model_layers_0_mlp_down_proj_weight); mul_10 = p_model_layers_0_mlp_down_proj_weight = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:337 in forward, code: hidden_states = residual + hidden_states
add_9: "f32[s41, s2, 192]" = torch.ops.aten.add.Tensor(to_10, linear_6); to_10 = linear_6 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:82 in forward, code: hidden_states = hidden_states.to(torch.float32)
_assert_tensor_metadata_default_12 = torch.ops.aten._assert_tensor_metadata.default(add_9, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_12 = None
to_12: "f32[s41, s2, 192]" = torch.ops.aten.to.dtype(add_9, torch.float32); add_9 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:83 in forward, code: variance = hidden_states.pow(2).mean(-1, keepdim=True)
pow_3: "f32[s41, s2, 192]" = torch.ops.aten.pow.Tensor_Scalar(to_12, 2)
mean_2: "f32[s41, s2, 1]" = torch.ops.aten.mean.dim(pow_3, [-1], True); pow_3 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:84 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
add_10: "f32[s41, s2, 1]" = torch.ops.aten.add.Tensor(mean_2, 1e-05); mean_2 = None
rsqrt_2: "f32[s41, s2, 1]" = torch.ops.aten.rsqrt.default(add_10); add_10 = None
mul_11: "f32[s41, s2, 192]" = torch.ops.aten.mul.Tensor(to_12, rsqrt_2); to_12 = rsqrt_2 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:85 in forward, code: return self.weight * hidden_states.to(input_dtype)
_assert_tensor_metadata_default_13 = torch.ops.aten._assert_tensor_metadata.default(mul_11, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_13 = None
to_13: "f32[s41, s2, 192]" = torch.ops.aten.to.dtype(mul_11, torch.float32); mul_11 = None
mul_12: "f32[s41, s2, 192]" = torch.ops.aten.mul.Tensor(p_model_norm_weight, to_13); p_model_norm_weight = to_13 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:842 in forward, code: logits = self.lm_head(hidden_states[:, slice_indices, :])
slice_35: "f32[s41, s2, 192]" = torch.ops.aten.slice.Tensor(mul_12); mul_12 = None
slice_36: "f32[s41, s2, 192]" = torch.ops.aten.slice.Tensor(slice_35, 1, 0); slice_35 = None
slice_37: "f32[s41, s2, 192]" = torch.ops.aten.slice.Tensor(slice_36, 2); slice_36 = None
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear_7: "f32[s41, s2, 32000]" = torch.ops.aten.linear.default(slice_37, p_lm_head_weight); slice_37 = p_lm_head_weight = None
return (linear_7, cat_3, cat_4)
class submod_1(torch.nn.Module):
def forward(self, b_model_rotary_emb_inv_freq: "f32[48]", sym_size_int_22: "Sym(s41)", position_ids: "i64[s41, s2]"):
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:115 in forward, code: inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
unsqueeze_4: "f32[1, 48]" = torch.ops.aten.unsqueeze.default(b_model_rotary_emb_inv_freq, 0); b_model_rotary_emb_inv_freq = None
slice_16: "f32[1, 48]" = torch.ops.aten.slice.Tensor(unsqueeze_4, 1, 0, 9223372036854775807); unsqueeze_4 = None
unsqueeze_5: "f32[1, 48, 1]" = torch.ops.aten.unsqueeze.default(slice_16, 2); slice_16 = None
_assert_tensor_metadata_default_1 = torch.ops.aten._assert_tensor_metadata.default(unsqueeze_5, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_1 = None
to_1: "f32[1, 48, 1]" = torch.ops.aten.to.dtype(unsqueeze_5, torch.float32); unsqueeze_5 = None
expand_1: "f32[s41, 48, 1]" = torch.ops.aten.expand.default(to_1, [sym_size_int_22, -1, 1]); to_1 = sym_size_int_22 = None
_assert_tensor_metadata_default_2 = torch.ops.aten._assert_tensor_metadata.default(expand_1, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_2 = None
to_2: "f32[s41, 48, 1]" = torch.ops.aten.to.dtype_layout(expand_1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu')); expand_1 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:116 in forward, code: position_ids_expanded = position_ids[:, None, :].float()
slice_17: "i64[s41, s2]" = torch.ops.aten.slice.Tensor(position_ids, 0, 0, 9223372036854775807); position_ids = None
unsqueeze_6: "i64[s41, 1, s2]" = torch.ops.aten.unsqueeze.default(slice_17, 1); slice_17 = None
slice_18: "i64[s41, 1, s2]" = torch.ops.aten.slice.Tensor(unsqueeze_6, 2, 0, 9223372036854775807); unsqueeze_6 = None
_assert_tensor_metadata_default_3 = torch.ops.aten._assert_tensor_metadata.default(slice_18, dtype = torch.int64, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_3 = None
to_3: "f32[s41, 1, s2]" = torch.ops.aten.to.dtype(slice_18, torch.float32); slice_18 = None
# No stacktrace found for following nodes
submod_3 = self.submod_1
wrap_with_autocast = torch.ops.higher_order.wrap_with_autocast('cpu', torch.bfloat16, False, False, submod_3, to_2, to_3); submod_3 = to_2 = to_3 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:122 in forward, code: cos = emb.cos() * self.attention_scaling
mul: "f32[s41, s2, 96]" = wrap_with_autocast[0]
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:123 in forward, code: sin = emb.sin() * self.attention_scaling
mul_1: "f32[s41, s2, 96]" = wrap_with_autocast[1]; wrap_with_autocast = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:125 in forward, code: return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
_assert_tensor_metadata_default_6 = torch.ops.aten._assert_tensor_metadata.default(mul, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_6 = None
to_6: "f32[s41, s2, 96]" = torch.ops.aten.to.dtype(mul, torch.float32); mul = None
_assert_tensor_metadata_default_7 = torch.ops.aten._assert_tensor_metadata.default(mul_1, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_7 = None
to_7: "f32[s41, s2, 96]" = torch.ops.aten.to.dtype(mul_1, torch.float32); mul_1 = None
return (to_6, to_7)
class submod_1(torch.nn.Module):
def forward(self, to_2: "f32[s41, 48, 1]", to_3: "f32[s41, 1, s2]"):
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:120 in forward, code: freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
_assert_tensor_metadata_default_4 = torch.ops.aten._assert_tensor_metadata.default(to_2, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_4 = None
to_4: "f32[s41, 48, 1]" = torch.ops.aten.to.dtype(to_2, torch.float32); to_2 = None
_assert_tensor_metadata_default_5 = torch.ops.aten._assert_tensor_metadata.default(to_3, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_5 = None
to_5: "f32[s41, 1, s2]" = torch.ops.aten.to.dtype(to_3, torch.float32); to_3 = None
matmul: "f32[s41, 48, s2]" = torch.ops.aten.matmul.default(to_4, to_5); to_4 = to_5 = None
transpose: "f32[s41, s2, 48]" = torch.ops.aten.transpose.int(matmul, 1, 2); matmul = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:121 in forward, code: emb = torch.cat((freqs, freqs), dim=-1)
cat: "f32[s41, s2, 96]" = torch.ops.aten.cat.default([transpose, transpose], -1); transpose = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:122 in forward, code: cos = emb.cos() * self.attention_scaling
cos: "f32[s41, s2, 96]" = torch.ops.aten.cos.default(cat)
mul: "f32[s41, s2, 96]" = torch.ops.aten.mul.Tensor(cos, 1.0); cos = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:123 in forward, code: sin = emb.sin() * self.attention_scaling
sin: "f32[s41, s2, 96]" = torch.ops.aten.sin.default(cat); cat = None
mul_1: "f32[s41, s2, 96]" = torch.ops.aten.mul.Tensor(sin, 1.0); sin = None
return (mul, mul_1)
Graph signature:
# inputs
p_model_embed_tokens_weight: PARAMETER target='model.embed_tokens.weight'
p_model_layers_0_self_attn_q_proj_weight: PARAMETER target='model.layers.0.self_attn.q_proj.weight'
p_model_layers_0_self_attn_k_proj_weight: PARAMETER target='model.layers.0.self_attn.k_proj.weight'
p_model_layers_0_self_attn_v_proj_weight: PARAMETER target='model.layers.0.self_attn.v_proj.weight'
p_model_layers_0_self_attn_o_proj_weight: PARAMETER target='model.layers.0.self_attn.o_proj.weight'
p_model_layers_0_mlp_gate_proj_weight: PARAMETER target='model.layers.0.mlp.gate_proj.weight'
p_model_layers_0_mlp_up_proj_weight: PARAMETER target='model.layers.0.mlp.up_proj.weight'
p_model_layers_0_mlp_down_proj_weight: PARAMETER target='model.layers.0.mlp.down_proj.weight'
p_model_layers_0_input_layernorm_weight: PARAMETER target='model.layers.0.input_layernorm.weight'
p_model_layers_0_post_attention_layernorm_weight: PARAMETER target='model.layers.0.post_attention_layernorm.weight'
p_model_norm_weight: PARAMETER target='model.norm.weight'
p_lm_head_weight: PARAMETER target='lm_head.weight'
b_model_rotary_emb_inv_freq: BUFFER target='model.rotary_emb.inv_freq' persistent=False
input_ids: USER_INPUT
attention_mask: USER_INPUT
position_ids: USER_INPUT
past_key_values_key_cache_0: USER_INPUT
past_key_values_value_cache_0: USER_INPUT
# outputs
linear_7: USER_OUTPUT
cat_3: USER_OUTPUT
cat_4: USER_OUTPUT
Range constraints: {s41: VR[1, 1024], s2: VR[2, 4096], s2 + s67: VR[4, 8192], s67: VR[1, 4096]}
Back to the original model¶
Let’s use the same dummy inputs but we use the downloaded model.
Dummy inputs and dynamic shapes are created by function
onnx_diagnostic.torch_models.llms.get_tiny_llm().
data = get_tiny_llm()
inputs, dynamic_shapes = data["inputs"], data["dynamic_shapes"]
Let’s print the inputs.
print(string_type(inputs, with_shape=True))
dict(input_ids:T7s2x3,attention_mask:T7s2x33,position_ids:T7s2x3,past_key_values:DynamicCache(key_cache=#1[T1s2x1x30x96], value_cache=#1[T1s2x1x30x96]))
{'attention_mask': {0: Dim('batch', min=1, max=1024),
1: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True)},
'input_ids': {0: Dim('batch', min=1, max=1024),
1: Dim('seq_length', min=1, max=4096)},
'past_key_values': [[{0: Dim('batch', min=1, max=1024),
2: Dim('cache_length', min=1, max=4096)}],
[{0: Dim('batch', min=1, max=1024),
2: Dim('cache_length', min=1, max=4096)}]],
'position_ids': {0: Dim('batch', min=1, max=1024),
1: _DimHint(type=<_DimHintType.DYNAMIC: 3>,
min=None,
max=None,
_factory=True)}}
And Let’s finally export.
try:
ep = torch.export.export(
model, (), kwargs=cloned_inputs, dynamic_shapes=dynamic_shapes, strict=False
)
print("It worked:")
print(ep)
except Exception as e:
# To work, it needs at least PRs:
# * https://github.com/huggingface/transformers/pull/36311
# * https://github.com/huggingface/transformers/pull/36652
print("It failed:", e)
It worked:
ExportedProgram:
class GraphModule(torch.nn.Module):
def forward(self, p_model_embed_tokens_weight: "f32[32000, 192]", p_model_layers_0_self_attn_q_proj_weight: "f32[192, 192]", p_model_layers_0_self_attn_k_proj_weight: "f32[96, 192]", p_model_layers_0_self_attn_v_proj_weight: "f32[96, 192]", p_model_layers_0_self_attn_o_proj_weight: "f32[192, 192]", p_model_layers_0_mlp_gate_proj_weight: "f32[1024, 192]", p_model_layers_0_mlp_up_proj_weight: "f32[1024, 192]", p_model_layers_0_mlp_down_proj_weight: "f32[192, 1024]", p_model_layers_0_input_layernorm_weight: "f32[192]", p_model_layers_0_post_attention_layernorm_weight: "f32[192]", p_model_norm_weight: "f32[192]", p_lm_head_weight: "f32[32000, 192]", b_model_rotary_emb_inv_freq: "f32[48]", input_ids: "i64[s41, s2]", attention_mask: "i64[s41, s2 + s67]", position_ids: "i64[s41, s2]", past_key_values_key_cache_0: "f32[s41, 1, s67, 96]", past_key_values_value_cache_0: "f32[s41, 1, s67, 96]"):
#
sym_size_int_22: "Sym(s41)" = torch.ops.aten.sym_size.int(input_ids, 0)
sym_size_int_23: "Sym(s2)" = torch.ops.aten.sym_size.int(input_ids, 1)
sym_size_int_24: "Sym(s67)" = torch.ops.aten.sym_size.int(past_key_values_key_cache_0, 2)
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/sparse.py:190 in forward, code: return F.embedding(
embedding: "f32[s41, s2, 192]" = torch.ops.aten.embedding.default(p_model_embed_tokens_weight, input_ids); p_model_embed_tokens_weight = input_ids = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:542 in forward, code: past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
add: "Sym(s2 + s67)" = sym_size_int_24 + sym_size_int_23
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:541 in forward, code: cache_position = torch.arange(
arange: "i64[s2]" = torch.ops.aten.arange.start(sym_size_int_24, add, device = device(type='cpu'), pin_memory = False); sym_size_int_24 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:548 in forward, code: causal_mask = self._update_causal_mask(
full: "f32[s2, s2 + s67]" = torch.ops.aten.full.default([sym_size_int_23, add], -3.4028234663852886e+38, dtype = torch.float32, device = device(type='cpu'), pin_memory = False)
triu: "f32[s2, s2 + s67]" = torch.ops.aten.triu.default(full, 1); full = None
arange_1: "i64[s2 + s67]" = torch.ops.aten.arange.default(add, device = device(type='cpu'), pin_memory = False)
reshape: "i64[s2, 1]" = torch.ops.aten.reshape.default(arange, [-1, 1]); arange = None
gt: "b8[s2, s2 + s67]" = torch.ops.aten.gt.Tensor(arange_1, reshape); arange_1 = reshape = None
mul_: "f32[s2, s2 + s67]" = torch.ops.aten.mul_.Tensor(triu, gt); triu = gt = None
unsqueeze: "f32[1, s2, s2 + s67]" = torch.ops.aten.unsqueeze.default(mul_, 0); mul_ = None
unsqueeze_1: "f32[1, 1, s2, s2 + s67]" = torch.ops.aten.unsqueeze.default(unsqueeze, 1); unsqueeze = None
slice_1: "f32[1, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(unsqueeze_1, 2, 0, 9223372036854775807); unsqueeze_1 = None
slice_2: "f32[1, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_1, 3, 0, 9223372036854775807); slice_1 = None
expand: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.expand.default(slice_2, [sym_size_int_22, 1, -1, -1]); slice_2 = None
clone: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.clone.default(expand); expand = None
slice_3: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(clone)
slice_4: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_3, 1); slice_3 = None
slice_5: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_4, 2); slice_4 = None
slice_6: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_5, 3, None, add); slice_5 = None
slice_7: "i64[s41, s2 + s67]" = torch.ops.aten.slice.Tensor(attention_mask, 0, 0, 9223372036854775807); attention_mask = None
unsqueeze_2: "i64[s41, 1, s2 + s67]" = torch.ops.aten.unsqueeze.default(slice_7, 1); slice_7 = None
unsqueeze_3: "i64[s41, 1, 1, s2 + s67]" = torch.ops.aten.unsqueeze.default(unsqueeze_2, 2); unsqueeze_2 = None
slice_8: "i64[s41, 1, 1, s2 + s67]" = torch.ops.aten.slice.Tensor(unsqueeze_3, 3, 0, 9223372036854775807); unsqueeze_3 = None
_assert_tensor_metadata_default = torch.ops.aten._assert_tensor_metadata.default(slice_8, dtype = torch.int64, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default = None
to: "i64[s41, 1, 1, s2 + s67]" = torch.ops.aten.to.dtype_layout(slice_8, dtype = torch.int64, layout = torch.strided, device = device(type='cpu')); slice_8 = None
add_2: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.add.Tensor(slice_6, to); slice_6 = to = None
eq_4: "b8[s41, 1, s2, s2 + s67]" = torch.ops.aten.eq.Scalar(add_2, 0); add_2 = None
slice_9: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(clone)
slice_10: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_9, 1); slice_9 = None
slice_11: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_10, 2); slice_10 = None
slice_12: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_11, 3, None, add); slice_11 = None
masked_fill: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.masked_fill.Scalar(slice_12, eq_4, -3.4028234663852886e+38); slice_12 = eq_4 = None
slice_13: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(clone, 0, 0, 9223372036854775807)
slice_14: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_13, 1, 0, 9223372036854775807); slice_13 = None
slice_15: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_14, 2, 0, 9223372036854775807); slice_14 = None
copy_: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.copy_.default(slice_15, masked_fill); slice_15 = masked_fill = copy_ = None
# No stacktrace found for following nodes
submod_3 = self.submod_1
wrap_with_set_grad_enabled = torch.ops.higher_order.wrap_with_set_grad_enabled(False, submod_3, b_model_rotary_emb_inv_freq, sym_size_int_22, position_ids); submod_3 = b_model_rotary_emb_inv_freq = position_ids = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:125 in forward, code: return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
to_6: "f32[s41, s2, 96]" = wrap_with_set_grad_enabled[0]
to_7: "f32[s41, s2, 96]" = wrap_with_set_grad_enabled[1]; wrap_with_set_grad_enabled = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:82 in forward, code: hidden_states = hidden_states.to(torch.float32)
_assert_tensor_metadata_default_8 = torch.ops.aten._assert_tensor_metadata.default(embedding, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_8 = None
to_8: "f32[s41, s2, 192]" = torch.ops.aten.to.dtype(embedding, torch.float32); embedding = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:83 in forward, code: variance = hidden_states.pow(2).mean(-1, keepdim=True)
pow_1: "f32[s41, s2, 192]" = torch.ops.aten.pow.Tensor_Scalar(to_8, 2)
mean: "f32[s41, s2, 1]" = torch.ops.aten.mean.dim(pow_1, [-1], True); pow_1 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:84 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
add_3: "f32[s41, s2, 1]" = torch.ops.aten.add.Tensor(mean, 1e-05); mean = None
rsqrt: "f32[s41, s2, 1]" = torch.ops.aten.rsqrt.default(add_3); add_3 = None
mul_2: "f32[s41, s2, 192]" = torch.ops.aten.mul.Tensor(to_8, rsqrt); rsqrt = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:85 in forward, code: return self.weight * hidden_states.to(input_dtype)
_assert_tensor_metadata_default_9 = torch.ops.aten._assert_tensor_metadata.default(mul_2, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_9 = None
to_9: "f32[s41, s2, 192]" = torch.ops.aten.to.dtype(mul_2, torch.float32); mul_2 = None
mul_3: "f32[s41, s2, 192]" = torch.ops.aten.mul.Tensor(p_model_layers_0_input_layernorm_weight, to_9); p_model_layers_0_input_layernorm_weight = to_9 = None
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear: "f32[s41, s2, 192]" = torch.ops.aten.linear.default(mul_3, p_model_layers_0_self_attn_q_proj_weight); p_model_layers_0_self_attn_q_proj_weight = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:254 in forward, code: query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
view: "f32[s41, s2, 2, 96]" = torch.ops.aten.view.default(linear, [sym_size_int_22, sym_size_int_23, -1, 96]); linear = None
transpose_1: "f32[s41, 2, s2, 96]" = torch.ops.aten.transpose.int(view, 1, 2); view = None
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear_1: "f32[s41, s2, 96]" = torch.ops.aten.linear.default(mul_3, p_model_layers_0_self_attn_k_proj_weight); p_model_layers_0_self_attn_k_proj_weight = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:255 in forward, code: key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
view_1: "f32[s41, s2, 1, 96]" = torch.ops.aten.view.default(linear_1, [sym_size_int_22, sym_size_int_23, -1, 96]); linear_1 = None
transpose_2: "f32[s41, 1, s2, 96]" = torch.ops.aten.transpose.int(view_1, 1, 2); view_1 = None
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear_2: "f32[s41, s2, 96]" = torch.ops.aten.linear.default(mul_3, p_model_layers_0_self_attn_v_proj_weight); mul_3 = p_model_layers_0_self_attn_v_proj_weight = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:256 in forward, code: value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
view_2: "f32[s41, s2, 1, 96]" = torch.ops.aten.view.default(linear_2, [sym_size_int_22, sym_size_int_23, -1, 96]); linear_2 = None
transpose_3: "f32[s41, 1, s2, 96]" = torch.ops.aten.transpose.int(view_2, 1, 2); view_2 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:259 in forward, code: query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
unsqueeze_7: "f32[s41, 1, s2, 96]" = torch.ops.aten.unsqueeze.default(to_6, 1); to_6 = None
unsqueeze_8: "f32[s41, 1, s2, 96]" = torch.ops.aten.unsqueeze.default(to_7, 1); to_7 = None
mul_4: "f32[s41, 2, s2, 96]" = torch.ops.aten.mul.Tensor(transpose_1, unsqueeze_7)
slice_19: "f32[s41, 2, s2, 48]" = torch.ops.aten.slice.Tensor(transpose_1, 3, 0, 48)
slice_20: "f32[s41, 2, s2, 48]" = torch.ops.aten.slice.Tensor(transpose_1, 3, 48, 9223372036854775807); transpose_1 = None
neg: "f32[s41, 2, s2, 48]" = torch.ops.aten.neg.default(slice_20); slice_20 = None
cat_1: "f32[s41, 2, s2, 96]" = torch.ops.aten.cat.default([neg, slice_19], -1); neg = slice_19 = None
mul_5: "f32[s41, 2, s2, 96]" = torch.ops.aten.mul.Tensor(cat_1, unsqueeze_8); cat_1 = None
add_4: "f32[s41, 2, s2, 96]" = torch.ops.aten.add.Tensor(mul_4, mul_5); mul_4 = mul_5 = None
mul_6: "f32[s41, 1, s2, 96]" = torch.ops.aten.mul.Tensor(transpose_2, unsqueeze_7); unsqueeze_7 = None
slice_21: "f32[s41, 1, s2, 48]" = torch.ops.aten.slice.Tensor(transpose_2, 3, 0, 48)
slice_22: "f32[s41, 1, s2, 48]" = torch.ops.aten.slice.Tensor(transpose_2, 3, 48, 9223372036854775807); transpose_2 = None
neg_1: "f32[s41, 1, s2, 48]" = torch.ops.aten.neg.default(slice_22); slice_22 = None
cat_2: "f32[s41, 1, s2, 96]" = torch.ops.aten.cat.default([neg_1, slice_21], -1); neg_1 = slice_21 = None
mul_7: "f32[s41, 1, s2, 96]" = torch.ops.aten.mul.Tensor(cat_2, unsqueeze_8); cat_2 = unsqueeze_8 = None
add_5: "f32[s41, 1, s2, 96]" = torch.ops.aten.add.Tensor(mul_6, mul_7); mul_6 = mul_7 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:264 in forward, code: key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
cat_3: "f32[s41, 1, s2 + s67, 96]" = torch.ops.aten.cat.default([past_key_values_key_cache_0, add_5], -2); past_key_values_key_cache_0 = add_5 = None
cat_4: "f32[s41, 1, s2 + s67, 96]" = torch.ops.aten.cat.default([past_key_values_value_cache_0, transpose_3], -2); past_key_values_value_cache_0 = transpose_3 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:277 in forward, code: attn_output, attn_weights = attention_interface(
slice_23: "f32[s41, 1, s2 + s67, 96]" = torch.ops.aten.slice.Tensor(cat_3, 0, 0, 9223372036854775807)
slice_24: "f32[s41, 1, s2 + s67, 96]" = torch.ops.aten.slice.Tensor(slice_23, 1, 0, 9223372036854775807); slice_23 = None
unsqueeze_9: "f32[s41, 1, 1, s2 + s67, 96]" = torch.ops.aten.unsqueeze.default(slice_24, 2); slice_24 = None
slice_25: "f32[s41, 1, 1, s2 + s67, 96]" = torch.ops.aten.slice.Tensor(unsqueeze_9, 3, 0, 9223372036854775807); unsqueeze_9 = None
slice_26: "f32[s41, 1, 1, s2 + s67, 96]" = torch.ops.aten.slice.Tensor(slice_25, 4, 0, 9223372036854775807); slice_25 = None
expand_2: "f32[s41, 1, 2, s2 + s67, 96]" = torch.ops.aten.expand.default(slice_26, [sym_size_int_22, 1, 2, add, 96]); slice_26 = None
reshape_1: "f32[s41, 2, s2 + s67, 96]" = torch.ops.aten.reshape.default(expand_2, [sym_size_int_22, 2, add, 96]); expand_2 = None
slice_27: "f32[s41, 1, s2 + s67, 96]" = torch.ops.aten.slice.Tensor(cat_4, 0, 0, 9223372036854775807)
slice_28: "f32[s41, 1, s2 + s67, 96]" = torch.ops.aten.slice.Tensor(slice_27, 1, 0, 9223372036854775807); slice_27 = None
unsqueeze_10: "f32[s41, 1, 1, s2 + s67, 96]" = torch.ops.aten.unsqueeze.default(slice_28, 2); slice_28 = None
slice_29: "f32[s41, 1, 1, s2 + s67, 96]" = torch.ops.aten.slice.Tensor(unsqueeze_10, 3, 0, 9223372036854775807); unsqueeze_10 = None
slice_30: "f32[s41, 1, 1, s2 + s67, 96]" = torch.ops.aten.slice.Tensor(slice_29, 4, 0, 9223372036854775807); slice_29 = None
expand_3: "f32[s41, 1, 2, s2 + s67, 96]" = torch.ops.aten.expand.default(slice_30, [sym_size_int_22, 1, 2, add, 96]); slice_30 = None
reshape_2: "f32[s41, 2, s2 + s67, 96]" = torch.ops.aten.reshape.default(expand_3, [sym_size_int_22, 2, add, 96]); expand_3 = None
slice_31: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(clone); clone = None
slice_32: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_31, 1); slice_31 = None
slice_33: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_32, 2); slice_32 = None
slice_34: "f32[s41, 1, s2, s2 + s67]" = torch.ops.aten.slice.Tensor(slice_33, 3, None, add); slice_33 = add = None
contiguous: "f32[s41, 2, s2, 96]" = torch.ops.aten.contiguous.default(add_4); add_4 = None
contiguous_1: "f32[s41, 2, s2 + s67, 96]" = torch.ops.aten.contiguous.default(reshape_1); reshape_1 = None
contiguous_2: "f32[s41, 2, s2 + s67, 96]" = torch.ops.aten.contiguous.default(reshape_2); reshape_2 = None
scaled_dot_product_attention: "f32[s41, 2, s2, 96]" = torch.ops.aten.scaled_dot_product_attention.default(contiguous, contiguous_1, contiguous_2, slice_34, scale = 0.10206207261596575); contiguous = contiguous_1 = contiguous_2 = slice_34 = None
transpose_4: "f32[s41, s2, 2, 96]" = torch.ops.aten.transpose.int(scaled_dot_product_attention, 1, 2); scaled_dot_product_attention = None
contiguous_3: "f32[s41, s2, 2, 96]" = torch.ops.aten.contiguous.default(transpose_4); transpose_4 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:288 in forward, code: attn_output = attn_output.reshape(*input_shape, -1).contiguous()
reshape_3: "f32[s41, s2, 192]" = torch.ops.aten.reshape.default(contiguous_3, [sym_size_int_22, sym_size_int_23, -1]); contiguous_3 = sym_size_int_22 = sym_size_int_23 = None
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear_3: "f32[s41, s2, 192]" = torch.ops.aten.linear.default(reshape_3, p_model_layers_0_self_attn_o_proj_weight); reshape_3 = p_model_layers_0_self_attn_o_proj_weight = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:331 in forward, code: hidden_states = residual + hidden_states
add_7: "f32[s41, s2, 192]" = torch.ops.aten.add.Tensor(to_8, linear_3); to_8 = linear_3 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:82 in forward, code: hidden_states = hidden_states.to(torch.float32)
_assert_tensor_metadata_default_10 = torch.ops.aten._assert_tensor_metadata.default(add_7, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_10 = None
to_10: "f32[s41, s2, 192]" = torch.ops.aten.to.dtype(add_7, torch.float32); add_7 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:83 in forward, code: variance = hidden_states.pow(2).mean(-1, keepdim=True)
pow_2: "f32[s41, s2, 192]" = torch.ops.aten.pow.Tensor_Scalar(to_10, 2)
mean_1: "f32[s41, s2, 1]" = torch.ops.aten.mean.dim(pow_2, [-1], True); pow_2 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:84 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
add_8: "f32[s41, s2, 1]" = torch.ops.aten.add.Tensor(mean_1, 1e-05); mean_1 = None
rsqrt_1: "f32[s41, s2, 1]" = torch.ops.aten.rsqrt.default(add_8); add_8 = None
mul_8: "f32[s41, s2, 192]" = torch.ops.aten.mul.Tensor(to_10, rsqrt_1); rsqrt_1 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:85 in forward, code: return self.weight * hidden_states.to(input_dtype)
_assert_tensor_metadata_default_11 = torch.ops.aten._assert_tensor_metadata.default(mul_8, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_11 = None
to_11: "f32[s41, s2, 192]" = torch.ops.aten.to.dtype(mul_8, torch.float32); mul_8 = None
mul_9: "f32[s41, s2, 192]" = torch.ops.aten.mul.Tensor(p_model_layers_0_post_attention_layernorm_weight, to_11); p_model_layers_0_post_attention_layernorm_weight = to_11 = None
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear_4: "f32[s41, s2, 1024]" = torch.ops.aten.linear.default(mul_9, p_model_layers_0_mlp_gate_proj_weight); p_model_layers_0_mlp_gate_proj_weight = None
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/activation.py:434 in forward, code: return F.silu(input, inplace=self.inplace)
silu: "f32[s41, s2, 1024]" = torch.ops.aten.silu.default(linear_4); linear_4 = None
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear_5: "f32[s41, s2, 1024]" = torch.ops.aten.linear.default(mul_9, p_model_layers_0_mlp_up_proj_weight); mul_9 = p_model_layers_0_mlp_up_proj_weight = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:174 in forward, code: down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
mul_10: "f32[s41, s2, 1024]" = torch.ops.aten.mul.Tensor(silu, linear_5); silu = linear_5 = None
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear_6: "f32[s41, s2, 192]" = torch.ops.aten.linear.default(mul_10, p_model_layers_0_mlp_down_proj_weight); mul_10 = p_model_layers_0_mlp_down_proj_weight = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:337 in forward, code: hidden_states = residual + hidden_states
add_9: "f32[s41, s2, 192]" = torch.ops.aten.add.Tensor(to_10, linear_6); to_10 = linear_6 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:82 in forward, code: hidden_states = hidden_states.to(torch.float32)
_assert_tensor_metadata_default_12 = torch.ops.aten._assert_tensor_metadata.default(add_9, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_12 = None
to_12: "f32[s41, s2, 192]" = torch.ops.aten.to.dtype(add_9, torch.float32); add_9 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:83 in forward, code: variance = hidden_states.pow(2).mean(-1, keepdim=True)
pow_3: "f32[s41, s2, 192]" = torch.ops.aten.pow.Tensor_Scalar(to_12, 2)
mean_2: "f32[s41, s2, 1]" = torch.ops.aten.mean.dim(pow_3, [-1], True); pow_3 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:84 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
add_10: "f32[s41, s2, 1]" = torch.ops.aten.add.Tensor(mean_2, 1e-05); mean_2 = None
rsqrt_2: "f32[s41, s2, 1]" = torch.ops.aten.rsqrt.default(add_10); add_10 = None
mul_11: "f32[s41, s2, 192]" = torch.ops.aten.mul.Tensor(to_12, rsqrt_2); to_12 = rsqrt_2 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:85 in forward, code: return self.weight * hidden_states.to(input_dtype)
_assert_tensor_metadata_default_13 = torch.ops.aten._assert_tensor_metadata.default(mul_11, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_13 = None
to_13: "f32[s41, s2, 192]" = torch.ops.aten.to.dtype(mul_11, torch.float32); mul_11 = None
mul_12: "f32[s41, s2, 192]" = torch.ops.aten.mul.Tensor(p_model_norm_weight, to_13); p_model_norm_weight = to_13 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:842 in forward, code: logits = self.lm_head(hidden_states[:, slice_indices, :])
slice_35: "f32[s41, s2, 192]" = torch.ops.aten.slice.Tensor(mul_12); mul_12 = None
slice_36: "f32[s41, s2, 192]" = torch.ops.aten.slice.Tensor(slice_35, 1, 0); slice_35 = None
slice_37: "f32[s41, s2, 192]" = torch.ops.aten.slice.Tensor(slice_36, 2); slice_36 = None
# File: /home/xadupre/vv/this312/lib/python3.12/site-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear_7: "f32[s41, s2, 32000]" = torch.ops.aten.linear.default(slice_37, p_lm_head_weight); slice_37 = p_lm_head_weight = None
return (linear_7, cat_3, cat_4)
class submod_1(torch.nn.Module):
def forward(self, b_model_rotary_emb_inv_freq: "f32[48]", sym_size_int_22: "Sym(s41)", position_ids: "i64[s41, s2]"):
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:115 in forward, code: inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
unsqueeze_4: "f32[1, 48]" = torch.ops.aten.unsqueeze.default(b_model_rotary_emb_inv_freq, 0); b_model_rotary_emb_inv_freq = None
slice_16: "f32[1, 48]" = torch.ops.aten.slice.Tensor(unsqueeze_4, 1, 0, 9223372036854775807); unsqueeze_4 = None
unsqueeze_5: "f32[1, 48, 1]" = torch.ops.aten.unsqueeze.default(slice_16, 2); slice_16 = None
_assert_tensor_metadata_default_1 = torch.ops.aten._assert_tensor_metadata.default(unsqueeze_5, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_1 = None
to_1: "f32[1, 48, 1]" = torch.ops.aten.to.dtype(unsqueeze_5, torch.float32); unsqueeze_5 = None
expand_1: "f32[s41, 48, 1]" = torch.ops.aten.expand.default(to_1, [sym_size_int_22, -1, 1]); to_1 = sym_size_int_22 = None
_assert_tensor_metadata_default_2 = torch.ops.aten._assert_tensor_metadata.default(expand_1, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_2 = None
to_2: "f32[s41, 48, 1]" = torch.ops.aten.to.dtype_layout(expand_1, dtype = torch.float32, layout = torch.strided, device = device(type='cpu')); expand_1 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:116 in forward, code: position_ids_expanded = position_ids[:, None, :].float()
slice_17: "i64[s41, s2]" = torch.ops.aten.slice.Tensor(position_ids, 0, 0, 9223372036854775807); position_ids = None
unsqueeze_6: "i64[s41, 1, s2]" = torch.ops.aten.unsqueeze.default(slice_17, 1); slice_17 = None
slice_18: "i64[s41, 1, s2]" = torch.ops.aten.slice.Tensor(unsqueeze_6, 2, 0, 9223372036854775807); unsqueeze_6 = None
_assert_tensor_metadata_default_3 = torch.ops.aten._assert_tensor_metadata.default(slice_18, dtype = torch.int64, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_3 = None
to_3: "f32[s41, 1, s2]" = torch.ops.aten.to.dtype(slice_18, torch.float32); slice_18 = None
# No stacktrace found for following nodes
submod_3 = self.submod_1
wrap_with_autocast = torch.ops.higher_order.wrap_with_autocast('cpu', torch.bfloat16, False, False, submod_3, to_2, to_3); submod_3 = to_2 = to_3 = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:122 in forward, code: cos = emb.cos() * self.attention_scaling
mul: "f32[s41, s2, 96]" = wrap_with_autocast[0]
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:123 in forward, code: sin = emb.sin() * self.attention_scaling
mul_1: "f32[s41, s2, 96]" = wrap_with_autocast[1]; wrap_with_autocast = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:125 in forward, code: return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
_assert_tensor_metadata_default_6 = torch.ops.aten._assert_tensor_metadata.default(mul, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_6 = None
to_6: "f32[s41, s2, 96]" = torch.ops.aten.to.dtype(mul, torch.float32); mul = None
_assert_tensor_metadata_default_7 = torch.ops.aten._assert_tensor_metadata.default(mul_1, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_7 = None
to_7: "f32[s41, s2, 96]" = torch.ops.aten.to.dtype(mul_1, torch.float32); mul_1 = None
return (to_6, to_7)
class submod_1(torch.nn.Module):
def forward(self, to_2: "f32[s41, 48, 1]", to_3: "f32[s41, 1, s2]"):
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:120 in forward, code: freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
_assert_tensor_metadata_default_4 = torch.ops.aten._assert_tensor_metadata.default(to_2, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_4 = None
to_4: "f32[s41, 48, 1]" = torch.ops.aten.to.dtype(to_2, torch.float32); to_2 = None
_assert_tensor_metadata_default_5 = torch.ops.aten._assert_tensor_metadata.default(to_3, dtype = torch.float32, device = device(type='cpu'), layout = torch.strided); _assert_tensor_metadata_default_5 = None
to_5: "f32[s41, 1, s2]" = torch.ops.aten.to.dtype(to_3, torch.float32); to_3 = None
matmul: "f32[s41, 48, s2]" = torch.ops.aten.matmul.default(to_4, to_5); to_4 = to_5 = None
transpose: "f32[s41, s2, 48]" = torch.ops.aten.transpose.int(matmul, 1, 2); matmul = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:121 in forward, code: emb = torch.cat((freqs, freqs), dim=-1)
cat: "f32[s41, s2, 96]" = torch.ops.aten.cat.default([transpose, transpose], -1); transpose = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:122 in forward, code: cos = emb.cos() * self.attention_scaling
cos: "f32[s41, s2, 96]" = torch.ops.aten.cos.default(cat)
mul: "f32[s41, s2, 96]" = torch.ops.aten.mul.Tensor(cos, 1.0); cos = None
# File: /home/xadupre/github/transformers/src/transformers/models/llama/modeling_llama.py:123 in forward, code: sin = emb.sin() * self.attention_scaling
sin: "f32[s41, s2, 96]" = torch.ops.aten.sin.default(cat); cat = None
mul_1: "f32[s41, s2, 96]" = torch.ops.aten.mul.Tensor(sin, 1.0); sin = None
return (mul, mul_1)
Graph signature:
# inputs
p_model_embed_tokens_weight: PARAMETER target='model.embed_tokens.weight'
p_model_layers_0_self_attn_q_proj_weight: PARAMETER target='model.layers.0.self_attn.q_proj.weight'
p_model_layers_0_self_attn_k_proj_weight: PARAMETER target='model.layers.0.self_attn.k_proj.weight'
p_model_layers_0_self_attn_v_proj_weight: PARAMETER target='model.layers.0.self_attn.v_proj.weight'
p_model_layers_0_self_attn_o_proj_weight: PARAMETER target='model.layers.0.self_attn.o_proj.weight'
p_model_layers_0_mlp_gate_proj_weight: PARAMETER target='model.layers.0.mlp.gate_proj.weight'
p_model_layers_0_mlp_up_proj_weight: PARAMETER target='model.layers.0.mlp.up_proj.weight'
p_model_layers_0_mlp_down_proj_weight: PARAMETER target='model.layers.0.mlp.down_proj.weight'
p_model_layers_0_input_layernorm_weight: PARAMETER target='model.layers.0.input_layernorm.weight'
p_model_layers_0_post_attention_layernorm_weight: PARAMETER target='model.layers.0.post_attention_layernorm.weight'
p_model_norm_weight: PARAMETER target='model.norm.weight'
p_lm_head_weight: PARAMETER target='lm_head.weight'
b_model_rotary_emb_inv_freq: BUFFER target='model.rotary_emb.inv_freq' persistent=False
input_ids: USER_INPUT
attention_mask: USER_INPUT
position_ids: USER_INPUT
past_key_values_key_cache_0: USER_INPUT
past_key_values_value_cache_0: USER_INPUT
# outputs
linear_7: USER_OUTPUT
cat_3: USER_OUTPUT
cat_4: USER_OUTPUT
Range constraints: {s41: VR[1, 1024], s2: VR[2, 4096], s2 + s67: VR[4, 8192], s67: VR[1, 4096]}
If you have any error, then look at example Export Tiny-LLM with patches.
doc.plot_legend("Tiny-LLM\nforward inputs\nbehind generate", "torch.export.export", "tomato")
Total running time of the script: (0 minutes 3.171 seconds)
Related examples
