mirror of
https://github.com/nod-ai/AMD-SHARK-Studio.git
synced 2026-04-03 03:00:17 -04:00
7023d556b5
* Add debug log of torch_model_blacklist.txt * Add make_fx for torch model * Update torch_model_blacklists.txt * Add some Xfails
182 lines
8.0 KiB
Python
182 lines
8.0 KiB
Python
from shark.shark_inference import SharkInference
|
|
|
|
import torch
|
|
import numpy as np
|
|
import sys
|
|
from torch.fx.experimental.proxy_tensor import make_fx
|
|
from torch._decomp import get_decompositions
|
|
|
|
torch.manual_seed(0)
|
|
|
|
vision_models = [
|
|
"alexnet",
|
|
"resnet101",
|
|
"resnet18",
|
|
"resnet50",
|
|
"squeezenet1_0",
|
|
"wide_resnet50_2",
|
|
"mobilenet_v3_small",
|
|
]
|
|
|
|
|
|
def get_torch_model(modelname):
|
|
if modelname in vision_models:
|
|
return get_vision_model(modelname)
|
|
else:
|
|
return get_hf_model(modelname)
|
|
|
|
|
|
##################### Hugging Face LM Models ###################################
|
|
|
|
|
|
class HuggingFaceLanguage(torch.nn.Module):
|
|
def __init__(self, hf_model_name):
|
|
super().__init__()
|
|
from transformers import AutoModelForSequenceClassification
|
|
import transformers as trf
|
|
|
|
transformers_path = trf.__path__[0]
|
|
hf_model_path = f"{transformers_path}/models/{hf_model_name}"
|
|
self.model = AutoModelForSequenceClassification.from_pretrained(
|
|
hf_model_name, # The pretrained model.
|
|
num_labels=2, # The number of output labels--2 for binary classification.
|
|
output_attentions=False, # Whether the model returns attentions weights.
|
|
output_hidden_states=False, # Whether the model returns all hidden-states.
|
|
torchscript=True,
|
|
)
|
|
|
|
def forward(self, tokens):
|
|
return self.model.forward(tokens)[0]
|
|
|
|
|
|
def get_hf_model(name):
|
|
from transformers import (
|
|
BertTokenizer,
|
|
TFBertModel,
|
|
)
|
|
|
|
model = HuggingFaceLanguage(name)
|
|
# TODO: Currently the test input is set to (1,128)
|
|
test_input = torch.randint(2, (1, 128))
|
|
actual_out = model(test_input)
|
|
|
|
# fx_g = make_fx(
|
|
# model(test_input),
|
|
# decomposition_table=get_decompositions(
|
|
# [
|
|
# torch.ops.aten.embedding_dense_backward,
|
|
# torch.ops.aten.native_layer_norm_backward,
|
|
# torch.ops.aten.slice_backward,
|
|
# torch.ops.aten.select_backward,
|
|
# torch.ops.aten.norm.ScalarOpt_dim,
|
|
# torch.ops.aten.native_group_norm,
|
|
# torch.ops.aten.upsample_bilinear2d.vec,
|
|
# torch.ops.aten.split.Tensor,
|
|
# torch.ops.aten.split_with_sizes,
|
|
# ]
|
|
# ),
|
|
# )(test_input)
|
|
# File "/home/chi/src/ubuntu20/shark/SHARK/shark.venv/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py", line 225, in wrapped
|
|
# t = dispatch_trace(wrap_key(f, args), concrete_args=tuple(phs),
|
|
# File "/home/chi/src/ubuntu20/shark/SHARK/shark.venv/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py", line 167, in dispatch_trace
|
|
# graph = tracer.trace(root, concrete_args)
|
|
# File "/home/chi/src/ubuntu20/shark/SHARK/shark.venv/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py", line 559, in trace
|
|
# fn, args = self.create_args_for_root(fn, isinstance(root, torch.nn.Module), concrete_args)
|
|
# File "/home/chi/src/ubuntu20/shark/SHARK/shark.venv/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py", line 388, in create_args_for_root
|
|
# co = fn_for_analysis.__code__
|
|
# AttributeError: 'Tensor' object has no attribute '__code__'. Did you mean: '__mod__'?
|
|
return model, test_input, actual_out
|
|
|
|
# fx_g = make_fx(
|
|
# model,
|
|
# decomposition_table=get_decompositions(
|
|
# [
|
|
# torch.ops.aten.embedding_dense_backward,
|
|
# torch.ops.aten.native_layer_norm_backward,
|
|
# torch.ops.aten.slice_backward,
|
|
# torch.ops.aten.select_backward,
|
|
# torch.ops.aten.norm.ScalarOpt_dim,
|
|
# torch.ops.aten.native_group_norm,
|
|
# torch.ops.aten.upsample_bilinear2d.vec,
|
|
# torch.ops.aten.split.Tensor,
|
|
# torch.ops.aten.split_with_sizes,
|
|
# ]
|
|
# ),
|
|
# )
|
|
# return fx_g, test_input, actual_out
|
|
|
|
# # Traceback (most recent call last):
|
|
# # File "/home/chi/src/ubuntu20/shark/SHARK/generate_sharktank.py", line 214, in <module>
|
|
# # save_torch_model(args.torch_model_csv)
|
|
# # File "/home/chi/src/ubuntu20/shark/SHARK/generate_sharktank.py", line 74, in save_torch_model
|
|
# # mlir_importer.import_debug(
|
|
# # File "/home/chi/src/ubuntu20/shark/SHARK/shark/shark_importer.py", line 163, in import_debug
|
|
# # imported_mlir = self.import_mlir(
|
|
# # File "/home/chi/src/ubuntu20/shark/SHARK/shark/shark_importer.py", line 109, in import_mlir
|
|
# # return self._torch_mlir(is_dynamic, tracing_required), func_name
|
|
# # File "/home/chi/src/ubuntu20/shark/SHARK/shark/shark_importer.py", line 74, in _torch_mlir
|
|
# # return get_torch_mlir_module(
|
|
# # File "/home/chi/src/ubuntu20/shark/SHARK/shark/torch_mlir_utils.py", line 123, in get_torch_mlir_module
|
|
# # module = torch_mlir.compile(
|
|
# # File "/home/chi/src/ubuntu20/shark/SHARK/shark.venv/lib/python3.10/site-packages/torch_mlir/__init__.py", line 120, in compile
|
|
# # scripted = torch.jit.trace(model, tuple(example_args))
|
|
# # File "/home/chi/src/ubuntu20/shark/SHARK/shark.venv/lib/python3.10/site-packages/torch/jit/_trace.py", line 795, in trace
|
|
# # traced = torch._C._create_function_from_trace(
|
|
# # File "/home/chi/src/ubuntu20/shark/SHARK/shark.venv/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py", line 225, in wrapped
|
|
# # t = dispatch_trace(wrap_key(f, args), concrete_args=tuple(phs),
|
|
# # File "/home/chi/src/ubuntu20/shark/SHARK/shark.venv/lib/python3.10/site-packages/torch/fx/experimental/proxy_tensor.py", line 167, in dispatch_trace
|
|
# # graph = tracer.trace(root, concrete_args)
|
|
# # File "/home/chi/src/ubuntu20/shark/SHARK/shark.venv/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py", line 559, in trace
|
|
# # fn, args = self.create_args_for_root(fn, isinstance(root, torch.nn.Module), concrete_args)
|
|
# # File "/home/chi/src/ubuntu20/shark/SHARK/shark.venv/lib/python3.10/site-packages/torch/fx/_symbolic_trace.py", line 388, in create_args_for_root
|
|
# # co = fn_for_analysis.__code__
|
|
# # File "/home/chi/src/ubuntu20/shark/SHARK/shark.venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1208, in __getattr__
|
|
# # raise AttributeError("'{}' object has no attribute '{}'".format(
|
|
# # AttributeError: 'HuggingFaceLanguage' object has no attribute '__code__'. Did you mean: '__call__'?
|
|
|
|
|
|
################################################################################
|
|
|
|
##################### Torch Vision Models ###################################
|
|
|
|
|
|
class VisionModule(torch.nn.Module):
|
|
def __init__(self, model):
|
|
super().__init__()
|
|
self.model = model
|
|
self.train(False)
|
|
|
|
def forward(self, input):
|
|
return self.model.forward(input)
|
|
|
|
|
|
def get_vision_model(torch_model):
|
|
import torchvision.models as models
|
|
|
|
vision_models_dict = {
|
|
"alexnet": models.alexnet(pretrained=True),
|
|
"resnet18": models.resnet18(pretrained=True),
|
|
"resnet50": models.resnet50(pretrained=True),
|
|
"resnet101": models.resnet101(pretrained=True),
|
|
"squeezenet1_0": models.squeezenet1_0(pretrained=True),
|
|
"wide_resnet50_2": models.wide_resnet50_2(pretrained=True),
|
|
"mobilenet_v3_small": models.mobilenet_v3_small(pretrained=True),
|
|
}
|
|
if isinstance(torch_model, str):
|
|
torch_model = vision_models_dict[torch_model]
|
|
model = VisionModule(torch_model)
|
|
test_input = torch.randn(1, 3, 224, 224)
|
|
actual_out = model(test_input)
|
|
return model, test_input, actual_out
|
|
|
|
|
|
################################################################################
|
|
|
|
# Utility function for comparing two tensors (torch).
|
|
def compare_tensors(torch_tensor, numpy_tensor):
|
|
# setting the absolute and relative tolerance
|
|
rtol = 1e-02
|
|
atol = 1e-03
|
|
# torch_to_numpy = torch_tensor.detach().numpy()
|
|
return np.allclose(torch_tensor, numpy_tensor, rtol, atol)
|