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目前是反向对齐出现的问题，建议用二分的方法查找diff，定位出PaddlePaddle与PyTorch梯度无法对齐的API或者操作，然后进一步验证并反馈。
相关文档可以参考这个： https://github.com/PaddlePaddle/models/blob/release/2.2/docs/lwfx/ArticleReproduction_CV.md#3.8

目前是反向对齐出现的问题，建议用二分的方法查找diff，定位出PaddlePaddle与PyTorch梯度无法对齐的API或者操作，然后进一步验证并反馈。 相关文档可以参考这个： https://github.com/PaddlePaddle/models/blob/release/2.2/docs/lwfx/ArticleReproduction_CV.md#3.8

已经定位到更细粒度的差别，为SynthesisLayer中的梯度出现差别。

代码地址： https://github.com/miemie2013/stylegan2-ada-pytorch_m2

首先运行test2_03_SynthesisLayer_grad.py，保存pytorch版SynthesisLayer的权重"pytorch_synthesisLayer.pth"，跑一次前向，保存输入、输出、输出对输入的梯度、部分层输出对输入的梯度为文件synthesisLayer_grad.npz；
接着，跑test2_03_SynthesisLayer_grad_2paddle.py，建立paddle版SynthesisLayer网络，将"pytorch_synthesisLayer.pth"的权重移植到paddle，保存为文件"pytorch_synthesisLayer.pdparams"；
最后，跑test2_03_SynthesisLayer_grad_paddle.py，建立paddle版SynthesisLayer网络，加载"pytorch_synthesisLayer.pdparams"的权重，读取synthesisLayer_grad.npz中保存的输入作为输入，跑一次前向。即此时paddle版SynthesisLayer与pytorch版SynthesisLayer有相同的权重，相同的输入，我们期望paddle版SynthesisLayer与pytorch版SynthesisLayer有相同的输出、相同的梯度。

出现差别的梯度如下：

# paddle_networks2.py文件

class SynthesisLayer(nn.Layer):
    ...
    def forward(self, x, w, dic2, pre_name, noise_mode='random', fused_modconv=True, gain=1):
        assert noise_mode in ['random', 'const', 'none']
        in_resolution = self.resolution // self.up
        styles = self.affine(w)

        dstyles_dw = paddle.grad(outputs=[styles.sum()], inputs=[w], create_graph=True)[0]
        dstyles_dw_paddle = dstyles_dw.numpy()
        dstyles_dw_pytorch = dic2[pre_name + '.dstyles_dw']
        ddd = np.sum((dstyles_dw_pytorch - dstyles_dw_paddle) ** 2)
        print('ddd=%.6f' % ddd)

        noise = None
        if self.use_noise and noise_mode == 'random':
            noise = paddle.randn([x.shape[0], 1, self.resolution, self.resolution]) * self.noise_strength
        if self.use_noise and noise_mode == 'const':
            noise = self.noise_const * self.noise_strength

        flip_weight = (self.up == 1) # slightly faster
        img2 = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up,
                                padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv)

        dimg2_dx = paddle.grad(outputs=[img2.sum()], inputs=[x], create_graph=True)[0]
        dimg2_dx_paddle = dimg2_dx.numpy()
        dimg2_dx_pytorch = dic2[pre_name + '.dimg2_dx']
        ddd = np.sum((dimg2_dx_pytorch - dimg2_dx_paddle) ** 2)
        print('ddd=%.6f' % ddd)

        dimg2_dw = paddle.grad(outputs=[img2.sum()], inputs=[w], create_graph=True)[0]
        dimg2_dw_paddle = dimg2_dw.numpy()
        dimg2_dw_pytorch = dic2[pre_name + '.dimg2_dw']
        ddd = np.sum((dimg2_dw_pytorch - dimg2_dw_paddle) ** 2)
        print('dimg2_dw_diff=%.6f' % ddd)

        dimg2_dstyles = paddle.grad(outputs=[img2.sum()], inputs=[styles], create_graph=True)[0]
        dimg2_dstyles_paddle = dimg2_dstyles.numpy()
        dimg2_dstyles_pytorch = dic2[pre_name + '.dimg2_dstyles']
        ddd = np.sum((dimg2_dstyles_pytorch - dimg2_dstyles_paddle) ** 2)
        print('ddd=%.6f' % ddd)

        act_gain = self.act_gain * gain
        act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
        img3 = bias_act(img2, paddle.cast(self.bias, dtype=img2.dtype), act=self.activation, gain=act_gain, clamp=act_clamp)
        return img3

dstyles_dw_pytorch与dstyles_dw_paddle是相同的；
dimg2_dx_pytorch与dimg2_dx_paddle是相同的；
dimg2_dw_pytorch与dimg2_dw_paddle是不同的！！！
dimg2_dstyles_pytorch与dimg2_dstyles_paddle是相同的；

核心前向代码是这两句：

styles = self.affine(w)
		...
        img2 = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up,
                                padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv)

即img2对styles的导数和pytorch相同，styles对w的导数和pytorch相同，但是，img2对w的导数和pytorch不同！！！这回我真不知道怎么搞了，望得到官方帮助。

目前是反向对齐出现的问题，建议用二分的方法查找diff，定位出PaddlePaddle与PyTorch梯度无法对齐的API或者操作，然后进一步验证并反馈。 相关文档可以参考这个： https://github.com/PaddlePaddle/models/blob/release/2.2/docs/lwfx/ArticleReproduction_CV.md#3.8

已经定位到更细粒度的差别，为SynthesisLayer中的梯度出现差别。

代码地址： https://github.com/miemie2013/stylegan2-ada-pytorch_m2

首先运行test2_03_SynthesisLayer_grad.py，保存pytorch版SynthesisLayer的权重"pytorch_synthesisLayer.pth"，跑一次前向，保存输入、输出、输出对输入的梯度、部分层输出对输入的梯度为文件synthesisLayer_grad.npz； 接着，跑test2_03_SynthesisLayer_grad_2paddle.py，建立paddle版SynthesisLayer网络，将"pytorch_synthesisLayer.pth"的权重移植到paddle，保存为文件"pytorch_synthesisLayer.pdparams"； 最后，跑test2_03_SynthesisLayer_grad_paddle.py，建立paddle版SynthesisLayer网络，加载"pytorch_synthesisLayer.pdparams"的权重，读取synthesisLayer_grad.npz中保存的输入作为输入，跑一次前向。即此时paddle版SynthesisLayer与pytorch版SynthesisLayer有相同的权重，相同的输入，我们期望paddle版SynthesisLayer与pytorch版SynthesisLayer有相同的输出、相同的梯度。

出现差别的梯度如下：

# paddle_networks2.py文件

class SynthesisLayer(nn.Layer):
    ...
    def forward(self, x, w, dic2, pre_name, noise_mode='random', fused_modconv=True, gain=1):
        assert noise_mode in ['random', 'const', 'none']
        in_resolution = self.resolution // self.up
        styles = self.affine(w)

        dstyles_dw = paddle.grad(outputs=[styles.sum()], inputs=[w], create_graph=True)[0]
        dstyles_dw_paddle = dstyles_dw.numpy()
        dstyles_dw_pytorch = dic2[pre_name + '.dstyles_dw']
        ddd = np.sum((dstyles_dw_pytorch - dstyles_dw_paddle) ** 2)
        print('ddd=%.6f' % ddd)

        noise = None
        if self.use_noise and noise_mode == 'random':
            noise = paddle.randn([x.shape[0], 1, self.resolution, self.resolution]) * self.noise_strength
        if self.use_noise and noise_mode == 'const':
            noise = self.noise_const * self.noise_strength

        flip_weight = (self.up == 1) # slightly faster
        img2 = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up,
                                padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv)

        dimg2_dx = paddle.grad(outputs=[img2.sum()], inputs=[x], create_graph=True)[0]
        dimg2_dx_paddle = dimg2_dx.numpy()
        dimg2_dx_pytorch = dic2[pre_name + '.dimg2_dx']
        ddd = np.sum((dimg2_dx_pytorch - dimg2_dx_paddle) ** 2)
        print('ddd=%.6f' % ddd)

        dimg2_dw = paddle.grad(outputs=[img2.sum()], inputs=[w], create_graph=True)[0]
        dimg2_dw_paddle = dimg2_dw.numpy()
        dimg2_dw_pytorch = dic2[pre_name + '.dimg2_dw']
        ddd = np.sum((dimg2_dw_pytorch - dimg2_dw_paddle) ** 2)
        print('dimg2_dw_diff=%.6f' % ddd)

        dimg2_dstyles = paddle.grad(outputs=[img2.sum()], inputs=[styles], create_graph=True)[0]
        dimg2_dstyles_paddle = dimg2_dstyles.numpy()
        dimg2_dstyles_pytorch = dic2[pre_name + '.dimg2_dstyles']
        ddd = np.sum((dimg2_dstyles_pytorch - dimg2_dstyles_paddle) ** 2)
        print('ddd=%.6f' % ddd)

        act_gain = self.act_gain * gain
        act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
        img3 = bias_act(img2, paddle.cast(self.bias, dtype=img2.dtype), act=self.activation, gain=act_gain, clamp=act_clamp)
        return img3

dstyles_dw_pytorch与dstyles_dw_paddle是相同的； dimg2_dx_pytorch与dimg2_dx_paddle是相同的； dimg2_dw_pytorch与dimg2_dw_paddle是不同的！！！ dimg2_dstyles_pytorch与dimg2_dstyles_paddle是相同的；

核心前向代码是这两句：

styles = self.affine(w)
		...
        img2 = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up,
                                padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv)

即img2对styles的导数和pytorch相同，styles对w的导数和pytorch相同，但是，img2对w的导数和pytorch不同！！！这回我真不知道怎么搞了，望得到官方帮助。

同学，您好，我在test2_03_SynthesisLayer_grad_paddle.py文件中将
dy_dx = paddle.grad(outputs=[y.sum()], inputs=[x], create_graph=False, retain_graph=True)[0]
dy_dws = paddle.grad(outputs=[y.sum()], inputs=[ws], create_graph=False, retain_graph=True)[0]
dy_dx_paddle = dy_dx.numpy()
dy_dws_paddle = dy_dws.numpy()
改为
y_sum = y.sum()
y_sum.backward(retain_graph=True)
dy_dx_paddle = x.gradient()
dy_dws_paddle = ws.gradient()
这样dy_dws_paddle的梯度是可以对齐的，你可以先使用backward。
感谢您的反馈，paddle.grad的问题后续会排期进行修复。

目前是反向对齐出现的问题，建议用二分的方法查找diff，定位出PaddlePaddle与PyTorch梯度无法对齐的API或者操作，然后进一步验证并反馈。 相关文档可以参考这个： https://github.com/PaddlePaddle/models/blob/release/2.2/docs/lwfx/ArticleReproduction_CV.md#3.8

已经定位到更细粒度的差别，为SynthesisLayer中的梯度出现差别。
代码地址： https://github.com/miemie2013/stylegan2-ada-pytorch_m2
首先运行test2_03_SynthesisLayer_grad.py，保存pytorch版SynthesisLayer的权重"pytorch_synthesisLayer.pth"，跑一次前向，保存输入、输出、输出对输入的梯度、部分层输出对输入的梯度为文件synthesisLayer_grad.npz； 接着，跑test2_03_SynthesisLayer_grad_2paddle.py，建立paddle版SynthesisLayer网络，将"pytorch_synthesisLayer.pth"的权重移植到paddle，保存为文件"pytorch_synthesisLayer.pdparams"； 最后，跑test2_03_SynthesisLayer_grad_paddle.py，建立paddle版SynthesisLayer网络，加载"pytorch_synthesisLayer.pdparams"的权重，读取synthesisLayer_grad.npz中保存的输入作为输入，跑一次前向。即此时paddle版SynthesisLayer与pytorch版SynthesisLayer有相同的权重，相同的输入，我们期望paddle版SynthesisLayer与pytorch版SynthesisLayer有相同的输出、相同的梯度。
出现差别的梯度如下：

# paddle_networks2.py文件

class SynthesisLayer(nn.Layer):
    ...
    def forward(self, x, w, dic2, pre_name, noise_mode='random', fused_modconv=True, gain=1):
        assert noise_mode in ['random', 'const', 'none']
        in_resolution = self.resolution // self.up
        styles = self.affine(w)

        dstyles_dw = paddle.grad(outputs=[styles.sum()], inputs=[w], create_graph=True)[0]
        dstyles_dw_paddle = dstyles_dw.numpy()
        dstyles_dw_pytorch = dic2[pre_name + '.dstyles_dw']
        ddd = np.sum((dstyles_dw_pytorch - dstyles_dw_paddle) ** 2)
        print('ddd=%.6f' % ddd)

        noise = None
        if self.use_noise and noise_mode == 'random':
            noise = paddle.randn([x.shape[0], 1, self.resolution, self.resolution]) * self.noise_strength
        if self.use_noise and noise_mode == 'const':
            noise = self.noise_const * self.noise_strength

        flip_weight = (self.up == 1) # slightly faster
        img2 = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up,
                                padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv)

        dimg2_dx = paddle.grad(outputs=[img2.sum()], inputs=[x], create_graph=True)[0]
        dimg2_dx_paddle = dimg2_dx.numpy()
        dimg2_dx_pytorch = dic2[pre_name + '.dimg2_dx']
        ddd = np.sum((dimg2_dx_pytorch - dimg2_dx_paddle) ** 2)
        print('ddd=%.6f' % ddd)

        dimg2_dw = paddle.grad(outputs=[img2.sum()], inputs=[w], create_graph=True)[0]
        dimg2_dw_paddle = dimg2_dw.numpy()
        dimg2_dw_pytorch = dic2[pre_name + '.dimg2_dw']
        ddd = np.sum((dimg2_dw_pytorch - dimg2_dw_paddle) ** 2)
        print('dimg2_dw_diff=%.6f' % ddd)

        dimg2_dstyles = paddle.grad(outputs=[img2.sum()], inputs=[styles], create_graph=True)[0]
        dimg2_dstyles_paddle = dimg2_dstyles.numpy()
        dimg2_dstyles_pytorch = dic2[pre_name + '.dimg2_dstyles']
        ddd = np.sum((dimg2_dstyles_pytorch - dimg2_dstyles_paddle) ** 2)
        print('ddd=%.6f' % ddd)

        act_gain = self.act_gain * gain
        act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
        img3 = bias_act(img2, paddle.cast(self.bias, dtype=img2.dtype), act=self.activation, gain=act_gain, clamp=act_clamp)
        return img3

dstyles_dw_pytorch与dstyles_dw_paddle是相同的； dimg2_dx_pytorch与dimg2_dx_paddle是相同的； dimg2_dw_pytorch与dimg2_dw_paddle是不同的！！！ dimg2_dstyles_pytorch与dimg2_dstyles_paddle是相同的；
核心前向代码是这两句：

styles = self.affine(w)
		...
        img2 = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up,
                                padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv)

即img2对styles的导数和pytorch相同，styles对w的导数和pytorch相同，但是，img2对w的导数和pytorch不同！！！这回我真不知道怎么搞了，望得到官方帮助。

同学，您好，我在test2_03_SynthesisLayer_grad_paddle.py文件中将 dy_dx = paddle.grad(outputs=[y.sum()], inputs=[x], create_graph=False, retain_graph=True)[0] dy_dws = paddle.grad(outputs=[y.sum()], inputs=[ws], create_graph=False, retain_graph=True)[0] dy_dx_paddle = dy_dx.numpy() dy_dws_paddle = dy_dws.numpy() 改为 y_sum = y.sum() y_sum.backward(retain_graph=True) dy_dx_paddle = x.gradient() dy_dws_paddle = ws.gradient() 这样dy_dws_paddle的梯度是可以对齐的，你可以先使用backward。 感谢您的反馈，paddle.grad的问题后续会排期进行修复。

只拿到numpy()数组没有用呀，还是需要梯度张量进行损失的计算，希望官方能尽快修复paddle.grad()的问题。