onnx conv+reshape

%cd ../../..
import set_env
from d2py.utils.file import mkdir
temp_dir = ".temp"
mkdir(temp_dir)

/media/pc/data/lxw/ai/tvm-book/doc/tutorials/frontend

import torch
from torch import nn

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 16, 1, 1, 0, bias=False, groups=1)
    def forward(self, x):
        x = self.conv1(x)
        # 下面两个等同
        # x = x.view(x.size(0), -1)
        x = torch.reshape(x, (x.size(0), -1))
        return x

shape = 1, 3, 32, 32
x = torch.rand(*shape)

torch_model = Model()
# 导出模型
output_name = "conv-reshape"
torch.onnx.export(
    torch_model,               # torch 模型
    x,                         # 模型输入或者对于多个输入，使用元组
    f"{temp_dir}/{output_name}.onnx",               # 模型保存的位置（可以是文件或类似文件的对象）
    export_params=True,        # 将训练后的参数权重存储在模型文件内
    opset_version=17,          # 导出模型的 ONNX 版本
    do_constant_folding=True,  # 是否执行常量折叠以进行优化
    verbose=True,
    input_names = ['data'],    # 模型的输入名称
    output_names = ['output'], # 模型的输出名称
    # export_modules_as_functions=True,
    dynamic_axes={'data' : {0 : 'batch_size'},    # 可变长度的轴
                  'output' : {0 : 'batch_size'}}
)

Exported graph: graph(%data : Float(*, 3, 32, 32, strides=[3072, 1024, 32, 1], requires_grad=0, device=cpu),
      %conv1.weight : Float(16, 3, 1, 1, strides=[3, 1, 1, 1], requires_grad=1, device=cpu)):
  %/conv1/Conv_output_0 : Float(*, 16, 32, 32, strides=[16384, 1024, 32, 1], requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1], onnx_name="/conv1/Conv"](%data, %conv1.weight), scope: __main__.Model::/torch.nn.modules.conv.Conv2d::conv1 # /media/pc/data/tmp/cache/conda/envs/xin/lib/python3.12/site-packages/torch/nn/modules/conv.py:456:0
  %/Shape_output_0 : Long(4, strides=[1], device=cpu) = onnx::Shape[onnx_name="/Shape"](%/conv1/Conv_output_0), scope: __main__.Model:: # /tmp/ipykernel_1214832/3273040045.py:12:0
  %/Constant_output_0 : Long(device=cpu) = onnx::Constant[value={0}, onnx_name="/Constant"](), scope: __main__.Model:: # /tmp/ipykernel_1214832/3273040045.py:12:0
  %/Gather_output_0 : Long(device=cpu) = onnx::Gather[axis=0, onnx_name="/Gather"](%/Shape_output_0, %/Constant_output_0), scope: __main__.Model:: # /tmp/ipykernel_1214832/3273040045.py:12:0
  %onnx::Unsqueeze_7 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={0}]()
  %/Unsqueeze_output_0 : Long(1, strides=[1], device=cpu) = onnx::Unsqueeze[onnx_name="/Unsqueeze"](%/Gather_output_0, %onnx::Unsqueeze_7), scope: __main__.Model::
  %/Constant_1_output_0 : Long(1, strides=[1], requires_grad=0, device=cpu) = onnx::Constant[value={-1}, onnx_name="/Constant_1"](), scope: __main__.Model::
  %/Concat_output_0 : Long(2, strides=[1], device=cpu) = onnx::Concat[axis=0, onnx_name="/Concat"](%/Unsqueeze_output_0, %/Constant_1_output_0), scope: __main__.Model:: # /tmp/ipykernel_1214832/3273040045.py:12:0
  %output : Float(*, *, strides=[16384, 1], requires_grad=1, device=cpu) = onnx::Reshape[allowzero=0, onnx_name="/Reshape"](%/conv1/Conv_output_0, %/Concat_output_0), scope: __main__.Model:: # /tmp/ipykernel_1214832/3273040045.py:12:0
  return (%output)

# from onnx import load_model, save_model
# from onnx.shape_inference import infer_shapes
# onnx_model = load_model(output_onnx_name)
# onnx_model = infer_shapes(onnx_model)
# save_model(onnx_model, "infered_test_net.onnx")

import onnx
import tvm
from tvm import relay
onnx_model = onnx.load(f"{temp_dir}/{output_name}.onnx")
mod, params = relay.frontend.from_onnx(onnx_model, {"data": shape}, freeze_params=True)
# with tvm.transform.PassContext(opt_level=3):
#     mod = relay.quantize.prerequisite_optimize(mod, params)
mod.show()

def @main(%data: Tensor[(1, 3, 32, 32), float32] /* ty=Tensor[(1, 3, 32, 32), float32] span=/conv1/Conv.data:0:0 */) -> Tensor[(1, 16384), float32] {
  %0 = nn.conv2d(%data, meta[relay.Constant][0] /* ty=Tensor[(16, 3, 1, 1), float32] span=/conv1/Conv.conv1.weight:0:0 */, padding=[0, 0, 0, 0], channels=16, kernel_size=[1, 1]) /* ty=Tensor[(1, 16, 32, 32), float32] span=/conv1/Conv:0:0 */;
  reshape(%0, newshape=[1, -1]) /* ty=Tensor[(1, 16384), float32] span=/Reshape:0:0 */
}

with tvm.transform.PassContext(opt_level=3):
    with relay.quantize.qconfig(
        skip_conv_layers=[],
        # calibrate_mode="kl_divergence", 
        weight_scale="max",
        round_for_shift=True,
        # rounding="TONEAREST", # "UPWARD" or "TONEAREST"
        # calibrate_skip_layers=[],
        skip_dense_layer=False,
    ):
        qmod = relay.quantize.quantize(mod, params)
qmod.show()

def @main(%data: Tensor[(1, 3, 32, 32), float32] /* ty=Tensor[(1, 3, 32, 32), float32] span=/conv1/Conv.data:0:0 */) -> Tensor[(1, 16384), float32] {
  %0 = multiply(%data, 16f /* ty=float32 */) /* ty=Tensor[(1, 3, 32, 32), float32] */;
  %1 = round(%0) /* ty=Tensor[(1, 3, 32, 32), float32] */;
  %2 = clip(%1, a_min=-127f, a_max=127f) /* ty=Tensor[(1, 3, 32, 32), float32] */;
  %3 = cast(%2, dtype="int8") /* ty=Tensor[(1, 3, 32, 32), int8] */;
  %4 = nn.conv2d(%3, meta[relay.Constant][0] /* ty=Tensor[(16, 3, 1, 1), int8] */, padding=[0, 0, 0, 0], channels=16, kernel_size=[1, 1], out_dtype="int32") /* ty=Tensor[(1, 16, 32, 32), int32] */;
  %5 = cast(%4, dtype="int64") /* ty=Tensor[(1, 16, 32, 32), int64] */;
  %6 = fixed_point_multiply(%5, multiplier=1230307840, shift=-7) /* ty=Tensor[(1, 16, 32, 32), int64] */;
  %7 = clip(%6, a_min=-127f, a_max=127f) /* ty=Tensor[(1, 16, 32, 32), int64] */;
  %8 = cast(%7, dtype="int32") /* ty=Tensor[(1, 16, 32, 32), int32] */;
  %9 = cast(%8, dtype="int8") /* ty=Tensor[(1, 16, 32, 32), int8] */;
  %10 = annotation.stop_fusion(%9) /* ty=Tensor[(1, 16, 32, 32), int8] */;
  %11 = reshape(%10, newshape=[1, -1]) /* ty=Tensor[(1, 16384), int8] */;
  %12 = cast(%11, dtype="float32") /* ty=Tensor[(1, 16384), float32] */;
  multiply(%12, 0.0625f /* ty=float32 */) /* ty=Tensor[(1, 16384), float32] */
}

# from onnxscript import optimizer

# model = optimizer.optimize(onnx_model, onnx_shape_inference=False)

# onnx.save(
#     model,
#     f"{temp_dir}/{output_name}_opt.onnx",
#     save_as_external_data=True,
#     all_tensors_to_one_file=True,
#     convert_attribute=True
# )