#!/usr/bin/env python

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# to you under the Apache License, Version 2.0 (the
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#
#   http://www.apache.org/licenses/LICENSE-2.0
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"""Test script for torch module"""
import os
import torch
import time
import numpy as np
import tvm
import tvm.testing
import tempfile
from tvm.contrib.torch import PyTorchTVMModule, compile


class Model(torch.nn.Module):
    def forward(self, x, y):
        return torch.matmul(x, y.softmax(1))


model = Model()
model.cuda().half()
x = torch.rand([1280, 2464, 4]).cuda().half()
y = torch.rand([1280, 4, 1]).cuda().half()
for i in range(20):
    t = time.time()
    o = model(x, y)
    torch.cuda.synchronize()
    print(1000 * (time.time() - t))
print(o.shape)


model_jit = torch.jit.script(model)
print(model_jit.graph)
input_shapes = [("x", list(x.shape)), ("y", list(y.shape))]
dtype = "float16"
export_dir = tempfile.mkdtemp("pytorch_compiled")
print("tmp export_dir:", export_dir)


mod = PyTorchTVMModule()
print("Converting...")
mod.from_pytorch(model_jit, input_shapes, dtype)

log_file = os.path.join(export_dir, "tuning.log")
if not os.path.exists(log_file):
    print("Tuning...")
    mod.tune_tvm(log_file=log_file, n_trial=20)

print("Building...")
tvm_mod = mod.build_tvm(export_dir)
pytorch_mod = mod.build_pytorch_module(num_inputs=2, num_outputs=1)


## Or you can load from a prebuilt tvm module
# mod = PyTorchTVMModule()
# tvm_mod = mod.load_tvm(export_dir)
# pytorch_mod = mod.build_pytorch_module(num_inputs=2, num_outputs=1, input_infos=input_shapes)


print("Run TVM...")
tvm_x = tvm.nd.array(x.cpu().numpy().astype(dtype), device=tvm.gpu(0))
tvm_y = tvm.nd.array(y.cpu().numpy().astype(dtype), device=tvm.gpu(0))
for i in range(20):
    t = time.time()
    tvm_mod.run(x=tvm_x, y=tvm_y)
    print(1000 * (time.time() - t))
tvm_output = tvm_mod.get_output(0)
print(tvm_output.shape)


print("Run PyTorch...")
for i in range(20):
    t = time.time()
    outputs = pytorch_mod.forward([x, y])
    torch.cuda.synchronize()
    print(1000 * (time.time() - t))
print(outputs[0].shape)


class EnsembleModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.layer = torch.jit.script(pytorch_mod)

    def forward(self, x, y, z) -> torch.Tensor:
        if x > 1:
            out = self.layer(y, z)[0]
        else:
            out = torch.ones([1280, 2464, 1])
        return out


print("Exporting...")
scripted = torch.jit.script(EnsembleModel())
print(scripted.graph)
scripted_path = os.path.join(export_dir, "model_tvm.pt")
scripted.save(scripted_path)


# print(o == outputs[0])
# print(o - outputs[0])