Lines Matching full:ops
48 sin = torch.ops.aten.sin.default(x)
50 …strict_mode = torch.ops.higher_order.strict_mode(strict_graph_0, (sin, b_submodule_buffer1)); str…
52 add = torch.ops.aten.add.Tensor(x, 3); x = None
60 add = torch.ops.aten.add.Tensor(arg0_1, 2)
61 add_1 = torch.ops.aten.add.Tensor(add, 4); add = None
62 add_2 = torch.ops.aten.add.Tensor(arg1_1, 6); arg1_1 = None
63 sum_1 = torch.ops.aten.sum.default(arg0_1); arg0_1 = None
64 sum_2 = torch.ops.aten.sum.default(add_1); add_1 = None
65 add_3 = torch.ops.aten.add.Tensor(sum_1, sum_2); sum_1 = sum_2 = None
66 sum_3 = torch.ops.aten.sum.default(add_2); add_2 = None
67 add_4 = torch.ops.aten.add.Tensor(add_3, sum_3); add_3 = sum_3 = None
216 view = torch.ops.aten.view.default(x, [1, 3]); x = None
217 permute = torch.ops.aten.permute.default(p_linear_weight, [1, 0]); p_linear_weight = None
218 …addmm = torch.ops.aten.addmm.default(p_linear_bias, view, permute); p_linear_bias = permute = None
219 view_1 = torch.ops.aten.view.default(addmm, [3]); addmm = None
220 _softmax = torch.ops.aten._softmax.default(view_1, 0, False); view_1 = None
221 alias = torch.ops.aten.alias.default(_softmax)
222 alias_1 = torch.ops.aten.alias.default(alias); alias = None
223 clone = torch.ops.aten.clone.default(c_lifted_tensor_0); c_lifted_tensor_0 = None
224 alias_2 = torch.ops.aten.alias.default(clone); clone = None
225 alias_3 = torch.ops.aten.alias.default(alias_2); alias_2 = None
226 alias_4 = torch.ops.aten.alias.default(alias_3); alias_3 = None
227 _log_softmax = torch.ops.aten._log_softmax.default(_softmax, 0, False); _softmax = None
228 alias_5 = torch.ops.aten.alias.default(_log_softmax)
229 alias_6 = torch.ops.aten.alias.default(alias_5); alias_5 = None
230 mul = torch.ops.aten.mul.Tensor(_log_softmax, alias_4); _log_softmax = None
231 sum_1 = torch.ops.aten.sum.dim_IntList(mul, []); mul = None
232 neg = torch.ops.aten.neg.default(sum_1); sum_1 = None
233 div = torch.ops.aten.div.Scalar(neg, 1); neg = None
234 …full_like = torch.ops.aten.full_like.default(div, 1, pin_memory = False, memory_format = torch.pre…
235 div_1 = torch.ops.aten.div.Scalar(full_like, 1); full_like = None
236 neg_1 = torch.ops.aten.neg.default(div_1); div_1 = None
237 expand = torch.ops.aten.expand.default(neg_1, [3]); neg_1 = None
238 mul_1 = torch.ops.aten.mul.Tensor(expand, alias_4); expand = alias_4 = None
239 alias_7 = torch.ops.aten.alias.default(alias_6); alias_6 = None
240 alias_8 = torch.ops.aten.alias.default(alias_7); alias_7 = None
241 exp = torch.ops.aten.exp.default(alias_8); alias_8 = None
242 sum_2 = torch.ops.aten.sum.dim_IntList(mul_1, [0], True)
243 mul_2 = torch.ops.aten.mul.Tensor(exp, sum_2); exp = sum_2 = None
244 sub = torch.ops.aten.sub.Tensor(mul_1, mul_2); mul_1 = mul_2 = None
245 alias_9 = torch.ops.aten.alias.default(alias_1); alias_1 = None
246 alias_10 = torch.ops.aten.alias.default(alias_9); alias_9 = None
247 mul_3 = torch.ops.aten.mul.Tensor(sub, alias_10); sub = None
248 sum_3 = torch.ops.aten.sum.dim_IntList(mul_3, [0], True)
249 mul_4 = torch.ops.aten.mul.Tensor(alias_10, sum_3); alias_10 = sum_3 = None
250 sub_1 = torch.ops.aten.sub.Tensor(mul_3, mul_4); mul_3 = mul_4 = None
251 view_2 = torch.ops.aten.view.default(sub_1, [1, 3]); sub_1 = None
252 permute_1 = torch.ops.aten.permute.default(view_2, [1, 0])
253 mm = torch.ops.aten.mm.default(permute_1, view); permute_1 = view = None
254 permute_2 = torch.ops.aten.permute.default(mm, [1, 0]); mm = None
255 sum_4 = torch.ops.aten.sum.dim_IntList(view_2, [0], True); view_2 = None
256 view_3 = torch.ops.aten.view.default(sum_4, [3]); sum_4 = None
257 permute_3 = torch.ops.aten.permute.default(permute_2, [1, 0]); permute_2 = None
265 view = torch.ops.aten.view.default(x, [1, 3]); x = None
266 permute = torch.ops.aten.permute.default(p_linear_weight, [1, 0]); p_linear_weight = None
267 …addmm = torch.ops.aten.addmm.default(p_linear_bias, view, permute); p_linear_bias = permute = None
268 view_1 = torch.ops.aten.view.default(addmm, [3]); addmm = None
269 _softmax = torch.ops.aten._softmax.default(view_1, 0, False); view_1 = None
270 alias = torch.ops.aten.alias.default(_softmax)
271 alias_1 = torch.ops.aten.alias.default(alias); alias = None
272 clone = torch.ops.aten.clone.default(c_lifted_tensor_0); c_lifted_tensor_0 = None
273 alias_2 = torch.ops.aten.alias.default(clone); clone = None
274 alias_3 = torch.ops.aten.alias.default(alias_2); alias_2 = None
275 alias_4 = torch.ops.aten.alias.default(alias_3); alias_3 = None
276 _log_softmax = torch.ops.aten._log_softmax.default(_softmax, 0, False); _softmax = None
277 alias_5 = torch.ops.aten.alias.default(_log_softmax)
278 alias_6 = torch.ops.aten.alias.default(alias_5); alias_5 = None
279 mul = torch.ops.aten.mul.Tensor(_log_softmax, alias_4); _log_softmax = None
280 sum_1 = torch.ops.aten.sum.dim_IntList(mul, []); mul = None
281 neg = torch.ops.aten.neg.default(sum_1); sum_1 = None
282 div = torch.ops.aten.div.Scalar(neg, 1); neg = None
283 …full_like = torch.ops.aten.full_like.default(div, 1, pin_memory = False, memory_format = torch.pre…
284 div_1 = torch.ops.aten.div.Scalar(full_like, 1); full_like = None
285 neg_1 = torch.ops.aten.neg.default(div_1); div_1 = None
286 expand = torch.ops.aten.expand.default(neg_1, [3]); neg_1 = None
287 mul_1 = torch.ops.aten.mul.Tensor(expand, alias_4); expand = alias_4 = None
288 alias_7 = torch.ops.aten.alias.default(alias_6); alias_6 = None
289 alias_8 = torch.ops.aten.alias.default(alias_7); alias_7 = None
290 exp = torch.ops.aten.exp.default(alias_8); alias_8 = None
291 sum_2 = torch.ops.aten.sum.dim_IntList(mul_1, [0], True)
292 mul_2 = torch.ops.aten.mul.Tensor(exp, sum_2); exp = sum_2 = None
293 sub = torch.ops.aten.sub.Tensor(mul_1, mul_2); mul_1 = mul_2 = None
294 alias_9 = torch.ops.aten.alias.default(alias_1); alias_1 = None
295 alias_10 = torch.ops.aten.alias.default(alias_9); alias_9 = None
296 mul_3 = torch.ops.aten.mul.Tensor(sub, alias_10); sub = None
297 sum_3 = torch.ops.aten.sum.dim_IntList(mul_3, [0], True)
298 mul_4 = torch.ops.aten.mul.Tensor(alias_10, sum_3); alias_10 = sum_3 = None
299 sub_1 = torch.ops.aten.sub.Tensor(mul_3, mul_4); mul_3 = mul_4 = None
300 view_2 = torch.ops.aten.view.default(sub_1, [1, 3]); sub_1 = None
301 permute_1 = torch.ops.aten.permute.default(view_2, [1, 0])
302 mm = torch.ops.aten.mm.default(permute_1, view); permute_1 = view = None
303 permute_2 = torch.ops.aten.permute.default(mm, [1, 0]); mm = None
304 sum_4 = torch.ops.aten.sum.dim_IntList(view_2, [0], True); view_2 = None
305 view_3 = torch.ops.aten.view.default(sum_4, [3]); sum_4 = None
306 permute_3 = torch.ops.aten.permute.default(permute_2, [1, 0]); permute_2 = None