我们从Python开源项目中，提取了以下23个代码示例，用于说明如何使用torch.baddbmm()。
def backward(self, grad_output): grad_input1 = torch.zeros(self.input1.size()) if grad_output.is_cuda: self.batchgrid = self.batchgrid.cuda() grad_input1 = grad_input1.cuda() #print('gradout:',grad_output.size()) grad_output_temp = grad_output.contiguous() grad_output_view = grad_output_temp.view(-1, self.height*self.width, 2) grad_output_view.contiguous() grad_output_temp = torch.transpose(grad_output_view, 1, 2) grad_output_temp.contiguous() batchgrid_temp = self.batchgrid.view(-1, self.height*self.width, 3) batchgrid_temp.contiguous() grad_input1 = torch.baddbmm(grad_input1, grad_output_temp, batchgrid_temp) return grad_input1
def SkipConnectLSTMCell(input, hidden, hidden_skip, w_ih, w_hh, b_ih=None, b_hh=None, noise_in=None, noise_hidden=None): input = input.expand(4, *input.size()) if noise_in is None else input.unsqueeze(0) * noise_in hx, cx = hidden hx = torch.cat([hx, hidden_skip], dim=1) hx = hx.expand(4, *hx.size()) if noise_hidden is None else hx.unsqueeze(0) * noise_hidden gates = torch.baddbmm(b_ih.unsqueeze(1), input, w_ih) + torch.baddbmm(b_hh.unsqueeze(1), hx, w_hh) ingate, forgetgate, cellgate, outgate = gates ingate = F.sigmoid(ingate) forgetgate = F.sigmoid(forgetgate) cellgate = F.tanh(cellgate) outgate = F.sigmoid(outgate) cy = (forgetgate * cx) + (ingate * cellgate) hy = outgate * F.tanh(cy) return hy, cy
def SkipConnectGRUCell(input, hidden, hidden_skip, w_ih, w_hh, b_ih=None, b_hh=None, noise_in=None, noise_hidden=None): input = input.expand(3, *input.size()) if noise_in is None else input.unsqueeze(0) * noise_in hx = torch.cat([hidden, hidden_skip], dim=1) hx = hx.expand(3, *hx.size()) if noise_hidden is None else hx.unsqueeze(0) * noise_hidden gi = torch.baddbmm(b_ih.unsqueeze(1), input, w_ih) gh = torch.baddbmm(b_hh.unsqueeze(1), hx, w_hh) i_r, i_i, i_n = gi h_r, h_i, h_n = gh resetgate = F.sigmoid(i_r + h_r) inputgate = F.sigmoid(i_i + h_i) newgate = F.tanh(i_n + resetgate * h_n) hy = newgate + inputgate * (hidden - newgate) return hy
def VarLSTMCell(input, hidden, w_ih, w_hh, b_ih=None, b_hh=None, noise_in=None, noise_hidden=None): input = input.expand(4, *input.size()) if noise_in is None else input.unsqueeze(0) * noise_in hx, cx = hidden hx = hx.expand(4, *hx.size()) if noise_hidden is None else hx.unsqueeze(0) * noise_hidden gates = torch.baddbmm(b_ih.unsqueeze(1), input, w_ih) + torch.baddbmm(b_hh.unsqueeze(1), hx, w_hh) ingate, forgetgate, cellgate, outgate = gates ingate = F.sigmoid(ingate) forgetgate = F.sigmoid(forgetgate) cellgate = F.tanh(cellgate) outgate = F.sigmoid(outgate) cy = (forgetgate * cx) + (ingate * cellgate) hy = outgate * F.tanh(cy) return hy, cy
def forward(self, add_batch, batch1, batch2): self.save_for_backward(batch1, batch2) output = self._get_output(add_batch) return torch.baddbmm(output, self.alpha, add_batch, self.beta, batch1, batch2)
def test_baddbmm(self): num_batches = 10 M, N, O = 12, 8, 5 b1 = torch.randn(num_batches, M, N) b2 = torch.randn(num_batches, N, O) res = torch.bmm(b1, b2) res2 = torch.Tensor().resize_as_(res).zero_() res2.baddbmm_(b1,b2) self.assertEqual(res2, res) res2.baddbmm_(1,b1,b2) self.assertEqual(res2, res*2) res2.baddbmm_(1,.5,b1,b2) self.assertEqual(res2, res*2.5) res3 = torch.baddbmm(1,res2,0,b1,b2) self.assertEqual(res3, res2) res4 = torch.baddbmm(1,res2,.5,b1,b2) self.assertEqual(res4, res*3) res5 = torch.baddbmm(0,res2,1,b1,b2) self.assertEqual(res5, res) res6 = torch.baddbmm(.1,res2,.5,b1,b2) self.assertEqual(res6, res2 * .1 + res * .5)
def forward(ctx, add_batch, batch1, batch2, alpha=1, beta=1, inplace=False): ctx.alpha = alpha ctx.beta = beta ctx.save_for_backward(batch1, batch2) output = _get_output(ctx, add_batch, inplace=inplace) return torch.baddbmm(alpha, add_batch, beta, batch1, batch2, out=output)
def test_functional_blas(self): def compare(fn, *args): unpacked_args = tuple(arg.data if isinstance(arg, Variable) else arg for arg in args) self.assertEqual(fn(*args).data, fn(*unpacked_args)) def test_blas_add(fn, x, y, z): # Checks all signatures compare(fn, x, y, z) compare(fn, 0.5, x, y, z) compare(fn, 0.5, x, 0.25, y, z) def test_blas(fn, x, y): compare(fn, x, y) test_blas(torch.mm, Variable(torch.randn(2, 10)), Variable(torch.randn(10, 4))) test_blas_add(torch.addmm, Variable(torch.randn(2, 4)), Variable(torch.randn(2, 10)), Variable(torch.randn(10, 4))) test_blas(torch.bmm, Variable(torch.randn(4, 2, 10)), Variable(torch.randn(4, 10, 4))) test_blas_add(torch.addbmm, Variable(torch.randn(2, 4)), Variable(torch.randn(4, 2, 10)), Variable(torch.randn(4, 10, 4))) test_blas_add(torch.baddbmm, Variable(torch.randn(4, 2, 4)), Variable(torch.randn(4, 2, 10)), Variable(torch.randn(4, 10, 4))) test_blas(torch.mv, Variable(torch.randn(2, 10)), Variable(torch.randn(10))) test_blas_add(torch.addmv, Variable(torch.randn(2)), Variable(torch.randn(2, 10)), Variable(torch.randn(10))) test_blas(torch.ger, Variable(torch.randn(5)), Variable(torch.randn(6))) test_blas_add(torch.addr, Variable(torch.randn(5, 6)), Variable(torch.randn(5)), Variable(torch.randn(6)))
def test_baddbmm(self): num_batches = 10 M, N, O = 12, 8, 5 b1 = torch.randn(num_batches, M, N) b2 = torch.randn(num_batches, N, O) res = torch.bmm(b1, b2) res2 = torch.Tensor().resize_as_(res).zero_() res2.baddbmm_(b1, b2) self.assertEqual(res2, res) res2.baddbmm_(1, b1, b2) self.assertEqual(res2, res * 2) res2.baddbmm_(1, .5, b1, b2) self.assertEqual(res2, res * 2.5) res3 = torch.baddbmm(1, res2, 0, b1, b2) self.assertEqual(res3, res2) res4 = torch.baddbmm(1, res2, .5, b1, b2) self.assertEqual(res4, res * 3) res5 = torch.baddbmm(0, res2, 1, b1, b2) self.assertEqual(res5, res) res6 = torch.baddbmm(.1, res2, .5, b1, b2) self.assertEqual(res6, res2 * .1 + res * .5)
def forward(self, input, indices=None): """ Shape: - target_batch :math:`(N, E, 1+N_r)`where `N = length, E = embedding size, N_r = noise ratio` """ if indices is None: return super(IndexLinear, self).forward(input) # the pytorch's [] operator BP can't correctly input = input.unsqueeze(1) target_batch = self.weight.index_select(0, indices.view(-1)).view(indices.size(0), indices.size(1), -1).transpose(1,2) bias = self.bias.index_select(0, indices.view(-1)).view(indices.size(0), 1, indices.size(1)) out = torch.baddbmm(1, bias, 1, input, target_batch) return out.squeeze()
def forward(ctx, add_batch, batch1, batch2, alpha=1, beta=1, inplace=False): ctx.alpha = alpha ctx.beta = beta ctx.add_batch_size = add_batch.size() ctx.save_for_backward(batch1, batch2) output = _get_output(ctx, add_batch, inplace=inplace) return torch.baddbmm(alpha, add_batch, beta, batch1, batch2, out=output)
def _test_broadcast_fused_matmul(self, cast): fns = ["baddbmm", "addbmm", "addmm", "addmv", "addr"] for fn in fns: batch_dim = random.randint(1, 8) n_dim = random.randint(1, 8) m_dim = random.randint(1, 8) p_dim = random.randint(1, 8) def dims_full_for_fn(): if fn == "baddbmm": return ([batch_dim, n_dim, p_dim], [batch_dim, n_dim, m_dim], [batch_dim, m_dim, p_dim]) elif fn == "addbmm": return ([n_dim, p_dim], [batch_dim, n_dim, m_dim], [batch_dim, m_dim, p_dim]) elif fn == "addmm": return ([n_dim, p_dim], [n_dim, m_dim], [m_dim, p_dim]) elif fn == "addmv": return ([n_dim], [n_dim, m_dim], [m_dim]) elif fn == "addr": return ([n_dim, m_dim], [n_dim], [m_dim]) else: raise AssertionError("unknown function") (t0_dims_full, t1_dims, t2_dims) = dims_full_for_fn() (t0_dims_small, _, _) = self._select_broadcastable_dims(t0_dims_full) t0_small = cast(torch.randn(*t0_dims_small).float()) t1 = cast(torch.randn(*t1_dims).float()) t2 = cast(torch.randn(*t2_dims).float()) t0_full = cast(t0_small.expand(*t0_dims_full)) fntorch = getattr(torch, fn) r0 = fntorch(t0_small, t1, t2) r1 = fntorch(t0_full, t1, t2) self.assertEqual(r0, r1)
def VarGRUCell(input, hidden, w_ih, w_hh, b_ih=None, b_hh=None, noise_in=None, noise_hidden=None): input = input.expand(3, *input.size()) if noise_in is None else input.unsqueeze(0) * noise_in hx = hidden.expand(3, *hidden.size()) if noise_hidden is None else hidden.unsqueeze(0) * noise_hidden gi = torch.baddbmm(b_ih.unsqueeze(1), input, w_ih) gh = torch.baddbmm(b_hh.unsqueeze(1), hx, w_hh) i_r, i_i, i_n = gi h_r, h_i, h_n = gh resetgate = F.sigmoid(i_r + h_r) inputgate = F.sigmoid(i_i + h_i) newgate = F.tanh(i_n + resetgate * h_n) hy = newgate + inputgate * (hidden - newgate) return hy
def backward(self, grad_output): grad_input1 = self.input1.new(self.input1.size()).zero_() # if grad_output.is_cuda: # self.batchgrid = self.batchgrid.cuda() # grad_input1 = grad_input1.cuda() grad_input1 = torch.baddbmm(grad_input1, torch.transpose(grad_output.view(-1, self.height*self.width, 2), 1,2), self.batchgrid.view(-1, self.height*self.width, 3)) return grad_input1
def backward(self, grad_output): grad_input1 = torch.zeros(self.input1.size()) if grad_output.is_cuda: self.batchgrid = self.batchgrid.cuda() grad_input1 = grad_input1.cuda() #print('gradout:',grad_output.size()) grad_input1 = torch.baddbmm(grad_input1, torch.transpose(grad_output.view(-1, self.height*self.width, 2), 1,2), self.batchgrid.view(-1, self.height*self.width, 3)) #print(grad_input1) return grad_input1*self.lr
def test_functional_blas(self): def compare(fn, *args): unpacked_args = tuple(arg.data if isinstance(arg, Variable) else arg for arg in args) unpacked_result = fn(*unpacked_args) packed_result = fn(*args).data # if non-Variable torch function returns a scalar, compare to scalar if not torch.is_tensor(unpacked_result): assert packed_result.dim() == 1 assert packed_result.nelement() == 1 packed_result = packed_result[0] self.assertEqual(packed_result, unpacked_result) def test_blas_add(fn, x, y, z): # Checks all signatures compare(fn, x, y, z) compare(fn, 0.5, x, y, z) compare(fn, 0.5, x, 0.25, y, z) def test_blas(fn, x, y): compare(fn, x, y) test_blas(torch.mm, Variable(torch.randn(2, 10)), Variable(torch.randn(10, 4))) test_blas_add(torch.addmm, Variable(torch.randn(2, 4)), Variable(torch.randn(2, 10)), Variable(torch.randn(10, 4))) test_blas(torch.bmm, Variable(torch.randn(4, 2, 10)), Variable(torch.randn(4, 10, 4))) test_blas_add(torch.addbmm, Variable(torch.randn(2, 4)), Variable(torch.randn(4, 2, 10)), Variable(torch.randn(4, 10, 4))) test_blas_add(torch.baddbmm, Variable(torch.randn(4, 2, 4)), Variable(torch.randn(4, 2, 10)), Variable(torch.randn(4, 10, 4))) test_blas(torch.mv, Variable(torch.randn(2, 10)), Variable(torch.randn(10))) test_blas_add(torch.addmv, Variable(torch.randn(2)), Variable(torch.randn(2, 10)), Variable(torch.randn(10))) test_blas(torch.ger, Variable(torch.randn(5)), Variable(torch.randn(6))) test_blas_add(torch.addr, Variable(torch.randn(5, 6)), Variable(torch.randn(5)), Variable(torch.randn(6))) test_blas(torch.matmul, Variable(torch.randn(6)), Variable(torch.randn(6))) test_blas(torch.matmul, Variable(torch.randn(10, 4)), Variable(torch.randn(4))) test_blas(torch.matmul, Variable(torch.randn(5)), Variable(torch.randn(5, 6))) test_blas(torch.matmul, Variable(torch.randn(2, 10)), Variable(torch.randn(10, 4))) test_blas(torch.matmul, Variable(torch.randn(5, 2, 10)), Variable(torch.randn(5, 10, 4))) test_blas(torch.matmul, Variable(torch.randn(3, 5, 2, 10)), Variable(torch.randn(3, 5, 10, 4))) test_blas(torch.matmul, Variable(torch.randn(3, 5, 2, 10)), Variable(torch.randn(10))) test_blas(torch.matmul, Variable(torch.randn(10)), Variable(torch.randn(3, 5, 10, 4)))
