new_zeros（）pytorch版本的转换方式 new_zeros（）pytorch版本的转换⽅式 如下所⽰：logprobs.new_zeros(logprobs.size())pytorch 0.4版本中⽤到的 新建⼀个与logprobs类型相同的Variable 转换为pytorch0.2等版本 logprobs.new(logprobs.size()).zero_()以上这篇new_zeros() pytorch版本的转换⽅式...
save(newStateDict, "edsr-baseline-fp32.pth.tar") 对比原始code的同学应该会发现:EDSR中的add_mean与sub_mean不见了。是的,笔者将add_mean与sub_mean移到了网络外面,不对其进行量化,具体为什么这样做,见后面的介绍。 除了上述操作外,我们还需要提供前述EDSR实现的量化版本模型,这个没太多需要介绍的,直接看...
PyTorch 为了实现量化,首先就得需要具备能够表示量化数据的 Tensor,这就是从 PyTorch 1.1 之后引入的 Quantized Tensor。Quantized Tensor 可以存储 int8/uint8/int32 类型的数据,并携带有 scale、zero_point 这些参数。把一个标准的 float Tensor 转换为量化 Tensor 的步骤如下: ...
这个 Python 函数会从 ATen 调用 toDLPack 函数，如下所示：DLManagedTensor* toDLPack(const Tensor& src) {ATenDLMTensor * atDLMTensor(new ATenDLMTensor); atDLMTensor->handle = src; atDLMTensor->tensor.manager_ctx = atDLMTensor; atDLMTensor->tensor.deleter = &deleter; atDLMTensor->tens...
# Operation | New/Shared memory | Still in computation graph |tensor.clone() # | New | Yes |tensor.detach() # | Shared | No |tensor.detach.clone()() # | New | No | br 张量拼接 '''注意torch.cat和torch.stack的区别在于torch.cat沿着给定的维度拼接,而...
global temp_buffertemp_buffer = h_new.detach().clone() if not self.h.requires_grad else h_new.clone() return self.y else:# h [batch_size, seq_len, d_model, state_size]h = torch.zeros(x.size(0), self.seq_len, self.d_model...
PyTorch is not a Python binding into a monolithic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would useNumPy/SciPy/scikit-learnetc. You can write your new neural network layers in Python itself, using your favorite libraries and use pack...
# y: [batch, n_output, EMBEDDING_LENGTH] prev_s = x.new_zeros(batch, 1, self.decoder_dim) prev_y = x.new_zeros(batch, 1, EMBEDDING_LENGTH) y = x.new_empty(batch, n_output, EMBEDDING_LENGTH) tmp_states = None for i_output in range(n_output): # repeat_s: [batch, n_squen...
bnb_4bit_quant_type='nf4', ) ) model.config.use_cache = False new_model = "lion-gem...
optimizer.step() # apply gradients output_sum = output # 保存模型 torch.save(rnn, 'rnn1.pt') 2 保存完之后利用这个训练完的模型对数据进行处理： 代码： new_m = torch.load('rnn1.pt') predict = new_m(test...