[aotd] Support saved tensors hooks in aot_autograd #150032
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IvanKobzarev
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Mar 26, 2025
ghstack-source-id: 3075c63ae1c076bc08c6ef10cae8da3bf91af976 Pull Request resolved: #150032
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/150032
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cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]
#148222 WIP Known to be missing: - [ ] Dynamo guards to cause recompilation if saved_tensors_hooks changed - [ ] Saved tensors hooks that pack into subclasses ``` INFO: TRACED GRAPH ===== Forward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== saved_tensors_pack_hook add 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== saved_tensors_unpack_hook add 3 ===== <eval_with_key>.22 from /data/users/ivankobzarev/a/pytorch/torch/fx/experimental/proxy_tensor.py:1225 in wrapped class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== Forward graph 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add, dtype = torch.float8_e4m3fn) # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]); add = None return (view, _to_copy, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph 3 ===== <eval_with_key>.21 class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", add_packed_2: "f8e4m3fn[s0, s1][s1, 1]cuda:0", tangents_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add_packed_2, dtype = torch.float32); add_packed_2 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) add_7: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(tangents_1, _to_copy); tangents_1 = _to_copy = None return (None, None, add_7) ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]
#148222 WIP Known to be missing: - [ ] Dynamo guards to cause recompilation if saved_tensors_hooks changed - [ ] Saved tensors hooks that pack into subclasses ``` INFO: TRACED GRAPH ===== Forward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== saved_tensors_pack_hook add 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== saved_tensors_unpack_hook add 3 ===== <eval_with_key>.22 from /data/users/ivankobzarev/a/pytorch/torch/fx/experimental/proxy_tensor.py:1225 in wrapped class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== Forward graph 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add, dtype = torch.float8_e4m3fn) # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]); add = None return (view, _to_copy, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph 3 ===== <eval_with_key>.21 class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", add_packed_2: "f8e4m3fn[s0, s1][s1, 1]cuda:0", tangents_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add_packed_2, dtype = torch.float32); add_packed_2 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) add_7: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(tangents_1, _to_copy); tangents_1 = _to_copy = None return (None, None, add_7) ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]
#148222 WIP Known to be missing: - [ ] Dynamo guards to cause recompilation if saved_tensors_hooks changed - [ ] Saved tensors hooks that pack into subclasses ``` INFO: TRACED GRAPH ===== Forward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== saved_tensors_pack_hook add 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== saved_tensors_unpack_hook add 3 ===== <eval_with_key>.22 from /data/users/ivankobzarev/a/pytorch/torch/fx/experimental/proxy_tensor.py:1225 in wrapped class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== Forward graph 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add, dtype = torch.float8_e4m3fn) # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]); add = None return (view, _to_copy, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph 3 ===== <eval_with_key>.21 class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", add_packed_2: "f8e4m3fn[s0, s1][s1, 1]cuda:0", tangents_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add_packed_2, dtype = torch.float32); add_packed_2 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) add_7: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(tangents_1, _to_copy); tangents_1 = _to_copy = None return (None, None, add_7) ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]
#148222 WIP Known to be missing: - [ ] Dynamo guards to cause recompilation if saved_tensors_hooks changed - [ ] Saved tensors hooks that pack into subclasses ``` INFO: TRACED GRAPH ===== Forward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== saved_tensors_pack_hook add 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== saved_tensors_unpack_hook add 3 ===== <eval_with_key>.22 from /data/users/ivankobzarev/a/pytorch/torch/fx/experimental/proxy_tensor.py:1225 in wrapped class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== Forward graph 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add, dtype = torch.float8_e4m3fn) # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]); add = None return (view, _to_copy, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph 3 ===== <eval_with_key>.21 class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", add_packed_2: "f8e4m3fn[s0, s1][s1, 1]cuda:0", tangents_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add_packed_2, dtype = torch.float32); add_packed_2 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) add_7: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(tangents_1, _to_copy); tangents_1 = _to_copy = None return (None, None, add_7) ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]
#148222 WIP Known to be missing: - [ ] Dynamo guards to cause recompilation if saved_tensors_hooks changed - [ ] Saved tensors hooks that pack into subclasses ``` INFO: TRACED GRAPH ===== Forward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== saved_tensors_pack_hook add 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== saved_tensors_unpack_hook add 3 ===== <eval_with_key>.22 from /data/users/ivankobzarev/a/pytorch/torch/fx/experimental/proxy_tensor.py:1225 in wrapped class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== Forward graph 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add, dtype = torch.float8_e4m3fn) # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]); add = None return (view, _to_copy, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph 3 ===== <eval_with_key>.21 class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", add_packed_2: "f8e4m3fn[s0, s1][s1, 1]cuda:0", tangents_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add_packed_2, dtype = torch.float32); add_packed_2 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) add_7: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(tangents_1, _to_copy); tangents_1 = _to_copy = None return (None, None, add_7) ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]
#148222 WIP Known to be missing: - [ ] Dynamo guards to cause recompilation if saved_tensors_hooks changed - [ ] Saved tensors hooks that pack into subclasses ``` INFO: TRACED GRAPH ===== Forward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== saved_tensors_pack_hook add 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== saved_tensors_unpack_hook add 3 ===== <eval_with_key>.22 from /data/users/ivankobzarev/a/pytorch/torch/fx/experimental/proxy_tensor.py:1225 in wrapped class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== Forward graph 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add, dtype = torch.float8_e4m3fn) # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]); add = None return (view, _to_copy, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph 3 ===== <eval_with_key>.21 class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", add_packed_2: "f8e4m3fn[s0, s1][s1, 1]cuda:0", tangents_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add_packed_2, dtype = torch.float32); add_packed_2 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) add_7: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(tangents_1, _to_copy); tangents_1 = _to_copy = None return (None, None, add_7) ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]
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#148222 WIP Known to be missing: - [ ] Dynamo guards to cause recompilation if saved_tensors_hooks changed - [ ] Saved tensors hooks that pack into subclasses ``` INFO: TRACED GRAPH ===== Forward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== saved_tensors_pack_hook add 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== saved_tensors_unpack_hook add 3 ===== <eval_with_key>.22 from /data/users/ivankobzarev/a/pytorch/torch/fx/experimental/proxy_tensor.py:1225 in wrapped class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== Forward graph 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add, dtype = torch.float8_e4m3fn) # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]); add = None return (view, _to_copy, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph 3 ===== <eval_with_key>.21 class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", add_packed_2: "f8e4m3fn[s0, s1][s1, 1]cuda:0", tangents_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add_packed_2, dtype = torch.float32); add_packed_2 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) add_7: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(tangents_1, _to_copy); tangents_1 = _to_copy = None return (None, None, add_7) ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames Differential Revision: [D72187044](https://our.internmc.facebook.com/intern/diff/D72187044) [ghstack-poisoned]
#148222 Goal: At the moment autograd saved tensors hooks are run in eager after compiled forward. They are executed at the same time for all saved tensors. Hooks can be used to reduce amout of memory used for saved tensors, doing quantization or offloading to cpu. This is suboptimal for optimization of peak memory. Better solution will be to put the hooks in the graph, as close as possible to the last usage of the tensor. To get user specified autograd saved tensors hooks in the graph. Logic: UX: If user specifies with torch.autograd.graph.saved_tensors_hooks(pack_gm, unpack_gm). Where pack_gm and unpack_gm are torch.fx.GraphModule. Then AotAutograd will retrace those graph modules, doing decompositions and functionalization in aot_autograd, inlining the result graphs in forward epilogue and backward prologue. User may want to use control logic in the hooks, for example applying quantization only for specific dtypes and sizes. This is also possible, user can put it into torch.fx.wrap function and use symbolic trace to make a GraphModule. In that case AotAutograd cahing will work only in case when user explicitly set to the torch.fx.wrap call_function node "user_cache_hash" metadata. If this metadata set - then aot_autograd cache can use saved cache artifact. If metadata is not set - then cache is bypassed. Dynamo: Dynamo traces pack and unpack hooks and installs them as subgraph and explicitly adds to the output_graph. (As those subgraphs are not used and will not be copied in the result by default). The complexity here is that at this moment we do not have example of inputs for the hooks. We trace pack_hook with some Tensor from the inputs. The result subgraphs are added to the hashing of AotAutograd Cache. In AotAutograd we retrace the graph with the true saved tensors coming from partitioner. Backwards Compatibility: As current hooks are executed in eager mode and not all of them will be traceable - we only try to put in the graph hooks, explicitly marked by user with annotation (@_inlineable_saved_tensors_hooks). For other hooks or if compiled autograd is enabled - keep the same logic. Recompilations: Hooks are guarded with lambda guard matching function id to cause recompilation if user reruns compiled function. Aot_autograd: After partitioner prepared forward and backward module - we trace prepared at Dynamo graphs for pack and unpack hooks and inline them in epilogue of forward and prologue of backward. Forward outputs and backward inputs are changed, transparently for user. We do not try to put it close the last usage etc., relying on inductor to do this optimization. ``` INFO: TRACED GRAPH ===== Forward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== saved_tensors_pack_hook add 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== saved_tensors_unpack_hook add 3 ===== <eval_with_key>.22 from /data/users/ivankobzarev/a/pytorch/torch/fx/experimental/proxy_tensor.py:1225 in wrapped class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== Forward graph 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add, dtype = torch.float8_e4m3fn) # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]); add = None return (view, _to_copy, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph 3 ===== <eval_with_key>.21 class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", add_packed_2: "f8e4m3fn[s0, s1][s1, 1]cuda:0", tangents_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add_packed_2, dtype = torch.float32); add_packed_2 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) add_7: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(tangents_1, _to_copy); tangents_1 = _to_copy = None return (None, None, add_7) ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy chenyang78 kadeng muchulee8 amjames chauhang aakhundov Differential Revision: [D72187044](https://our.internmc.facebook.com/intern/diff/D72187044) [ghstack-poisoned]
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@IvanKobzarev has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
#148222 Goal: At the moment autograd saved tensors hooks are run in eager after compiled forward. They are executed at the same time for all saved tensors. Hooks can be used to reduce amout of memory used for saved tensors, doing quantization or offloading to cpu. This is suboptimal for optimization of peak memory. Better solution will be to put the hooks in the graph, as close as possible to the last usage of the tensor. To get user specified autograd saved tensors hooks in the graph. Logic: UX: If user specifies with torch.autograd.graph.saved_tensors_hooks(pack_gm, unpack_gm). Where pack_gm and unpack_gm are torch.fx.GraphModule. Then AotAutograd will retrace those graph modules, doing decompositions and functionalization in aot_autograd, inlining the result graphs in forward epilogue and backward prologue. User may want to use control logic in the hooks, for example applying quantization only for specific dtypes and sizes. This is also possible, user can put it into torch.fx.wrap function and use symbolic trace to make a GraphModule. In that case AotAutograd cahing will work only in case when user explicitly set to the torch.fx.wrap call_function node "user_cache_hash" metadata. If this metadata set - then aot_autograd cache can use saved cache artifact. If metadata is not set - then cache is bypassed. Dynamo: Dynamo traces pack and unpack hooks and installs them as subgraph and explicitly adds to the output_graph. (As those subgraphs are not used and will not be copied in the result by default). The complexity here is that at this moment we do not have example of inputs for the hooks. We trace pack_hook with some Tensor from the inputs. The result subgraphs are added to the hashing of AotAutograd Cache. In AotAutograd we retrace the graph with the true saved tensors coming from partitioner. Backwards Compatibility: As current hooks are executed in eager mode and not all of them will be traceable - we only try to put in the graph hooks, explicitly marked by user with annotation (@_inlineable_saved_tensors_hooks). For other hooks or if compiled autograd is enabled - keep the same logic. Recompilations: Hooks are guarded with lambda guard matching function id to cause recompilation if user reruns compiled function. Aot_autograd: After partitioner prepared forward and backward module - we trace prepared at Dynamo graphs for pack and unpack hooks and inline them in epilogue of forward and prologue of backward. Forward outputs and backward inputs are changed, transparently for user. We do not try to put it close the last usage etc., relying on inductor to do this optimization. ``` INFO: TRACED GRAPH ===== Forward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== saved_tensors_pack_hook add 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== saved_tensors_unpack_hook add 3 ===== <eval_with_key>.22 from /data/users/ivankobzarev/a/pytorch/torch/fx/experimental/proxy_tensor.py:1225 in wrapped class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== Forward graph 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add, dtype = torch.float8_e4m3fn) # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]); add = None return (view, _to_copy, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph 3 ===== <eval_with_key>.21 class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", add_packed_2: "f8e4m3fn[s0, s1][s1, 1]cuda:0", tangents_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add_packed_2, dtype = torch.float32); add_packed_2 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) add_7: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(tangents_1, _to_copy); tangents_1 = _to_copy = None return (None, None, add_7) ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy chenyang78 kadeng muchulee8 amjames chauhang aakhundov Differential Revision: [D72187044](https://our.internmc.facebook.com/intern/diff/D72187044) [ghstack-poisoned]
|
@IvanKobzarev has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
#148222 Goal: At the moment autograd saved tensors hooks are run in eager after compiled forward. They are executed at the same time for all saved tensors. Hooks can be used to reduce amout of memory used for saved tensors, doing quantization or offloading to cpu. This is suboptimal for optimization of peak memory. Better solution will be to put the hooks in the graph, as close as possible to the last usage of the tensor. To get user specified autograd saved tensors hooks in the graph. Logic: UX: If user specifies with torch.autograd.graph.saved_tensors_hooks(pack_gm, unpack_gm). Where pack_gm and unpack_gm are torch.fx.GraphModule. Then AotAutograd will retrace those graph modules, doing decompositions and functionalization in aot_autograd, inlining the result graphs in forward epilogue and backward prologue. User may want to use control logic in the hooks, for example applying quantization only for specific dtypes and sizes. This is also possible, user can put it into torch.fx.wrap function and use symbolic trace to make a GraphModule. In that case AotAutograd cahing will work only in case when user explicitly set to the torch.fx.wrap call_function node "user_cache_hash" metadata. If this metadata set - then aot_autograd cache can use saved cache artifact. If metadata is not set - then cache is bypassed. Dynamo: Dynamo traces pack and unpack hooks and installs them as subgraph and explicitly adds to the output_graph. (As those subgraphs are not used and will not be copied in the result by default). The complexity here is that at this moment we do not have example of inputs for the hooks. We trace pack_hook with some Tensor from the inputs. The result subgraphs are added to the hashing of AotAutograd Cache. In AotAutograd we retrace the graph with the true saved tensors coming from partitioner. Backwards Compatibility: As current hooks are executed in eager mode and not all of them will be traceable - we only try to put in the graph hooks, explicitly marked by user with annotation (@_inlineable_saved_tensors_hooks). For other hooks or if compiled autograd is enabled - keep the same logic. Recompilations: Hooks are guarded with lambda guard matching function id to cause recompilation if user reruns compiled function. Aot_autograd: After partitioner prepared forward and backward module - we trace prepared at Dynamo graphs for pack and unpack hooks and inline them in epilogue of forward and prologue of backward. Forward outputs and backward inputs are changed, transparently for user. We do not try to put it close the last usage etc., relying on inductor to do this optimization. ``` INFO: TRACED GRAPH ===== Forward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== saved_tensors_pack_hook add 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== saved_tensors_unpack_hook add 3 ===== <eval_with_key>.22 from /data/users/ivankobzarev/a/pytorch/torch/fx/experimental/proxy_tensor.py:1225 in wrapped class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== Forward graph 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add, dtype = torch.float8_e4m3fn) # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]); add = None return (view, _to_copy, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph 3 ===== <eval_with_key>.21 class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", add_packed_2: "f8e4m3fn[s0, s1][s1, 1]cuda:0", tangents_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add_packed_2, dtype = torch.float32); add_packed_2 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) add_7: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(tangents_1, _to_copy); tangents_1 = _to_copy = None return (None, None, add_7) ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy chenyang78 kadeng muchulee8 amjames chauhang aakhundov Differential Revision: [D72187044](https://our.internmc.facebook.com/intern/diff/D72187044) [ghstack-poisoned]
#148222 Goal: At the moment autograd saved tensors hooks are run in eager after compiled forward. They are executed at the same time for all saved tensors. Hooks can be used to reduce amout of memory used for saved tensors, doing quantization or offloading to cpu. This is suboptimal for optimization of peak memory. Better solution will be to put the hooks in the graph, as close as possible to the last usage of the tensor. To get user specified autograd saved tensors hooks in the graph. Logic: UX: If user specifies with torch.autograd.graph.saved_tensors_hooks(pack_gm, unpack_gm). Where pack_gm and unpack_gm are torch.fx.GraphModule. Then AotAutograd will retrace those graph modules, doing decompositions and functionalization in aot_autograd, inlining the result graphs in forward epilogue and backward prologue. User may want to use control logic in the hooks, for example applying quantization only for specific dtypes and sizes. This is also possible, user can put it into torch.fx.wrap function and use symbolic trace to make a GraphModule. In that case AotAutograd cahing will work only in case when user explicitly set to the torch.fx.wrap call_function node "user_cache_hash" metadata. If this metadata set - then aot_autograd cache can use saved cache artifact. If metadata is not set - then cache is bypassed. Dynamo: Dynamo traces pack and unpack hooks and installs them as subgraph and explicitly adds to the output_graph. (As those subgraphs are not used and will not be copied in the result by default). The complexity here is that at this moment we do not have example of inputs for the hooks. We trace pack_hook with some Tensor from the inputs. The result subgraphs are added to the hashing of AotAutograd Cache. In AotAutograd we retrace the graph with the true saved tensors coming from partitioner. Backwards Compatibility: As current hooks are executed in eager mode and not all of them will be traceable - we only try to put in the graph hooks, explicitly marked by user with annotation (@_inlineable_saved_tensors_hooks). For other hooks or if compiled autograd is enabled - keep the same logic. Recompilations: Hooks are guarded with lambda guard matching function id to cause recompilation if user reruns compiled function. Aot_autograd: After partitioner prepared forward and backward module - we trace prepared at Dynamo graphs for pack and unpack hooks and inline them in epilogue of forward and prologue of backward. Forward outputs and backward inputs are changed, transparently for user. We do not try to put it close the last usage etc., relying on inductor to do this optimization. ``` INFO: TRACED GRAPH ===== Forward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== saved_tensors_pack_hook add 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== saved_tensors_unpack_hook add 3 ===== <eval_with_key>.22 from /data/users/ivankobzarev/a/pytorch/torch/fx/experimental/proxy_tensor.py:1225 in wrapped class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== Forward graph 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add, dtype = torch.float8_e4m3fn) # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]); add = None return (view, _to_copy, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph 3 ===== <eval_with_key>.21 class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", add_packed_2: "f8e4m3fn[s0, s1][s1, 1]cuda:0", tangents_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add_packed_2, dtype = torch.float32); add_packed_2 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) add_7: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(tangents_1, _to_copy); tangents_1 = _to_copy = None return (None, None, add_7) ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy chenyang78 kadeng muchulee8 amjames chauhang aakhundov Differential Revision: [D72187044](https://our.internmc.facebook.com/intern/diff/D72187044) [ghstack-poisoned]
|
@IvanKobzarev has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
#148222 Goal: At the moment autograd saved tensors hooks are run in eager after compiled forward. They are executed at the same time for all saved tensors. Hooks can be used to reduce amout of memory used for saved tensors, doing quantization or offloading to cpu. This is suboptimal for optimization of peak memory. Better solution will be to put the hooks in the graph, as close as possible to the last usage of the tensor. To get user specified autograd saved tensors hooks in the graph. Logic: UX: If user specifies with torch.autograd.graph.saved_tensors_hooks(pack_gm, unpack_gm). Where pack_gm and unpack_gm are torch.fx.GraphModule. Then AotAutograd will retrace those graph modules, doing decompositions and functionalization in aot_autograd, inlining the result graphs in forward epilogue and backward prologue. User may want to use control logic in the hooks, for example applying quantization only for specific dtypes and sizes. This is also possible, user can put it into torch.fx.wrap function and use symbolic trace to make a GraphModule. In that case AotAutograd cahing will work only in case when user explicitly set to the torch.fx.wrap call_function node "user_cache_hash" metadata. If this metadata set - then aot_autograd cache can use saved cache artifact. If metadata is not set - then cache is bypassed. Dynamo: Dynamo traces pack and unpack hooks and installs them as subgraph and explicitly adds to the output_graph. (As those subgraphs are not used and will not be copied in the result by default). The complexity here is that at this moment we do not have example of inputs for the hooks. We trace pack_hook with some Tensor from the inputs. The result subgraphs are added to the hashing of AotAutograd Cache. In AotAutograd we retrace the graph with the true saved tensors coming from partitioner. Backwards Compatibility: As current hooks are executed in eager mode and not all of them will be traceable - we only try to put in the graph hooks, explicitly marked by user with annotation (@_inlineable_saved_tensors_hooks). For other hooks or if compiled autograd is enabled - keep the same logic. Recompilations: Hooks are guarded with lambda guard matching function id to cause recompilation if user reruns compiled function. Aot_autograd: After partitioner prepared forward and backward module - we trace prepared at Dynamo graphs for pack and unpack hooks and inline them in epilogue of forward and prologue of backward. Forward outputs and backward inputs are changed, transparently for user. We do not try to put it close the last usage etc., relying on inductor to do this optimization. ``` INFO: TRACED GRAPH ===== Forward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== saved_tensors_pack_hook add 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== saved_tensors_unpack_hook add 3 ===== <eval_with_key>.22 from /data/users/ivankobzarev/a/pytorch/torch/fx/experimental/proxy_tensor.py:1225 in wrapped class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== Forward graph 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add, dtype = torch.float8_e4m3fn) # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]); add = None return (view, _to_copy, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph 3 ===== <eval_with_key>.21 class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", add_packed_2: "f8e4m3fn[s0, s1][s1, 1]cuda:0", tangents_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add_packed_2, dtype = torch.float32); add_packed_2 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) add_7: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(tangents_1, _to_copy); tangents_1 = _to_copy = None return (None, None, add_7) ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy chenyang78 kadeng muchulee8 amjames chauhang aakhundov Differential Revision: [D72187044](https://our.internmc.facebook.com/intern/diff/D72187044) [ghstack-poisoned]
|
@IvanKobzarev has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
#148222 Goal: At the moment autograd saved tensors hooks are run in eager after compiled forward. They are executed at the same time for all saved tensors. Hooks can be used to reduce amout of memory used for saved tensors, doing quantization or offloading to cpu. This is suboptimal for optimization of peak memory. Better solution will be to put the hooks in the graph, as close as possible to the last usage of the tensor. To get user specified autograd saved tensors hooks in the graph. Logic: UX: If user specifies with torch.autograd.graph.saved_tensors_hooks(pack_gm, unpack_gm). Where pack_gm and unpack_gm are torch.fx.GraphModule. Then AotAutograd will retrace those graph modules, doing decompositions and functionalization in aot_autograd, inlining the result graphs in forward epilogue and backward prologue. User may want to use control logic in the hooks, for example applying quantization only for specific dtypes and sizes. This is also possible, user can put it into torch.fx.wrap function and use symbolic trace to make a GraphModule. In that case AotAutograd cahing will work only in case when user explicitly set to the torch.fx.wrap call_function node "user_cache_hash" metadata. If this metadata set - then aot_autograd cache can use saved cache artifact. If metadata is not set - then cache is bypassed. Dynamo: Dynamo traces pack and unpack hooks and installs them as subgraph and explicitly adds to the output_graph. (As those subgraphs are not used and will not be copied in the result by default). The complexity here is that at this moment we do not have example of inputs for the hooks. We trace pack_hook with some Tensor from the inputs. The result subgraphs are added to the hashing of AotAutograd Cache. In AotAutograd we retrace the graph with the true saved tensors coming from partitioner. Backwards Compatibility: As current hooks are executed in eager mode and not all of them will be traceable - we only try to put in the graph hooks, explicitly marked by user with annotation (@_inlineable_saved_tensors_hooks). For other hooks or if compiled autograd is enabled - keep the same logic. Recompilations: Hooks are guarded with lambda guard matching function id to cause recompilation if user reruns compiled function. Aot_autograd: After partitioner prepared forward and backward module - we trace prepared at Dynamo graphs for pack and unpack hooks and inline them in epilogue of forward and prologue of backward. Forward outputs and backward inputs are changed, transparently for user. We do not try to put it close the last usage etc., relying on inductor to do this optimization. ``` INFO: TRACED GRAPH ===== Forward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== saved_tensors_pack_hook add 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== saved_tensors_unpack_hook add 3 ===== <eval_with_key>.22 from /data/users/ivankobzarev/a/pytorch/torch/fx/experimental/proxy_tensor.py:1225 in wrapped class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== Forward graph 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add, dtype = torch.float8_e4m3fn) # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]); add = None return (view, _to_copy, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph 3 ===== <eval_with_key>.21 class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", add_packed_2: "f8e4m3fn[s0, s1][s1, 1]cuda:0", tangents_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add_packed_2, dtype = torch.float32); add_packed_2 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) add_7: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(tangents_1, _to_copy); tangents_1 = _to_copy = None return (None, None, add_7) ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy chenyang78 kadeng muchulee8 amjames chauhang aakhundov Differential Revision: [D72187044](https://our.internmc.facebook.com/intern/diff/D72187044) [ghstack-poisoned]
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@IvanKobzarev has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
bdhirsh
reviewed
May 5, 2025
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#148222 Goal: At the moment autograd saved tensors hooks are run in eager after compiled forward. They are executed at the same time for all saved tensors. Hooks can be used to reduce amout of memory used for saved tensors, doing quantization or offloading to cpu. This is suboptimal for optimization of peak memory. Better solution will be to put the hooks in the graph, as close as possible to the last usage of the tensor. To get user specified autograd saved tensors hooks in the graph. Logic: UX: If user specifies with torch.autograd.graph.saved_tensors_hooks(pack_gm, unpack_gm). Where pack_gm and unpack_gm are torch.fx.GraphModule. Then AotAutograd will retrace those graph modules, doing decompositions and functionalization in aot_autograd, inlining the result graphs in forward epilogue and backward prologue. User may want to use control logic in the hooks, for example applying quantization only for specific dtypes and sizes. This is also possible, user can put it into torch.fx.wrap function and use symbolic trace to make a GraphModule. In that case AotAutograd cahing will work only in case when user explicitly set to the torch.fx.wrap call_function node "user_cache_hash" metadata. If this metadata set - then aot_autograd cache can use saved cache artifact. If metadata is not set - then cache is bypassed. Dynamo: Dynamo traces pack and unpack hooks and installs them as subgraph and explicitly adds to the output_graph. (As those subgraphs are not used and will not be copied in the result by default). The complexity here is that at this moment we do not have example of inputs for the hooks. We trace pack_hook with some Tensor from the inputs. The result subgraphs are added to the hashing of AotAutograd Cache. In AotAutograd we retrace the graph with the true saved tensors coming from partitioner. Backwards Compatibility: As current hooks are executed in eager mode and not all of them will be traceable - we only try to put in the graph hooks, explicitly marked by user with annotation (@_inlineable_saved_tensors_hooks). For other hooks or if compiled autograd is enabled - keep the same logic. Recompilations: Hooks are guarded with lambda guard matching function id to cause recompilation if user reruns compiled function. Aot_autograd: After partitioner prepared forward and backward module - we trace prepared at Dynamo graphs for pack and unpack hooks and inline them in epilogue of forward and prologue of backward. Forward outputs and backward inputs are changed, transparently for user. We do not try to put it close the last usage etc., relying on inductor to do this optimization. ``` INFO: TRACED GRAPH ===== Forward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== saved_tensors_pack_hook add 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== saved_tensors_unpack_hook add 3 ===== <eval_with_key>.22 from /data/users/ivankobzarev/a/pytorch/torch/fx/experimental/proxy_tensor.py:1225 in wrapped class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== Forward graph 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add, dtype = torch.float8_e4m3fn) # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]); add = None return (view, _to_copy, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph 3 ===== <eval_with_key>.21 class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", add_packed_2: "f8e4m3fn[s0, s1][s1, 1]cuda:0", tangents_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add_packed_2, dtype = torch.float32); add_packed_2 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) add_7: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(tangents_1, _to_copy); tangents_1 = _to_copy = None return (None, None, add_7) ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy chenyang78 kadeng muchulee8 amjames chauhang aakhundov Differential Revision: [D72187044](https://our.internmc.facebook.com/intern/diff/D72187044) [ghstack-poisoned]
|
@IvanKobzarev has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
#148222 Goal: At the moment autograd saved tensors hooks are run in eager after compiled forward. They are executed at the same time for all saved tensors. Hooks can be used to reduce amout of memory used for saved tensors, doing quantization or offloading to cpu. This is suboptimal for optimization of peak memory. Better solution will be to put the hooks in the graph, as close as possible to the last usage of the tensor. To get user specified autograd saved tensors hooks in the graph. Logic: UX: If user specifies with torch.autograd.graph.saved_tensors_hooks(pack_gm, unpack_gm). Where pack_gm and unpack_gm are torch.fx.GraphModule. Then AotAutograd will retrace those graph modules, doing decompositions and functionalization in aot_autograd, inlining the result graphs in forward epilogue and backward prologue. User may want to use control logic in the hooks, for example applying quantization only for specific dtypes and sizes. This is also possible, user can put it into torch.fx.wrap function and use symbolic trace to make a GraphModule. In that case AotAutograd cahing will work only in case when user explicitly set to the torch.fx.wrap call_function node "user_cache_hash" metadata. If this metadata set - then aot_autograd cache can use saved cache artifact. If metadata is not set - then cache is bypassed. Dynamo: Dynamo traces pack and unpack hooks and installs them as subgraph and explicitly adds to the output_graph. (As those subgraphs are not used and will not be copied in the result by default). The complexity here is that at this moment we do not have example of inputs for the hooks. We trace pack_hook with some Tensor from the inputs. The result subgraphs are added to the hashing of AotAutograd Cache. In AotAutograd we retrace the graph with the true saved tensors coming from partitioner. Backwards Compatibility: As current hooks are executed in eager mode and not all of them will be traceable - we only try to put in the graph hooks, explicitly marked by user with annotation (@_inlineable_saved_tensors_hooks). For other hooks or if compiled autograd is enabled - keep the same logic. Recompilations: Hooks are guarded with lambda guard matching function id to cause recompilation if user reruns compiled function. Aot_autograd: After partitioner prepared forward and backward module - we trace prepared at Dynamo graphs for pack and unpack hooks and inline them in epilogue of forward and prologue of backward. Forward outputs and backward inputs are changed, transparently for user. We do not try to put it close the last usage etc., relying on inductor to do this optimization. ``` INFO: TRACED GRAPH ===== Forward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== saved_tensors_pack_hook add 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== saved_tensors_unpack_hook add 3 ===== <eval_with_key>.22 from /data/users/ivankobzarev/a/pytorch/torch/fx/experimental/proxy_tensor.py:1225 in wrapped class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== Forward graph 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add, dtype = torch.float8_e4m3fn) # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]); add = None return (view, _to_copy, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph 3 ===== <eval_with_key>.21 class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", add_packed_2: "f8e4m3fn[s0, s1][s1, 1]cuda:0", tangents_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add_packed_2, dtype = torch.float32); add_packed_2 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) add_7: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(tangents_1, _to_copy); tangents_1 = _to_copy = None return (None, None, add_7) ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy chenyang78 kadeng muchulee8 amjames chauhang aakhundov Differential Revision: [D72187044](https://our.internmc.facebook.com/intern/diff/D72187044) [ghstack-poisoned]
|
@IvanKobzarev has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
#148222 Goal: At the moment autograd saved tensors hooks are run in eager after compiled forward. They are executed at the same time for all saved tensors. Hooks can be used to reduce amout of memory used for saved tensors, doing quantization or offloading to cpu. This is suboptimal for optimization of peak memory. Better solution will be to put the hooks in the graph, as close as possible to the last usage of the tensor. To get user specified autograd saved tensors hooks in the graph. Logic: UX: If user specifies with torch.autograd.graph.saved_tensors_hooks(pack_gm, unpack_gm). Where pack_gm and unpack_gm are torch.fx.GraphModule. Then AotAutograd will retrace those graph modules, doing decompositions and functionalization in aot_autograd, inlining the result graphs in forward epilogue and backward prologue. User may want to use control logic in the hooks, for example applying quantization only for specific dtypes and sizes. This is also possible, user can put it into torch.fx.wrap function and use symbolic trace to make a GraphModule. In that case AotAutograd cahing will work only in case when user explicitly set to the torch.fx.wrap call_function node "user_cache_hash" metadata. If this metadata set - then aot_autograd cache can use saved cache artifact. If metadata is not set - then cache is bypassed. Dynamo: Dynamo traces pack and unpack hooks and installs them as subgraph and explicitly adds to the output_graph. (As those subgraphs are not used and will not be copied in the result by default). The complexity here is that at this moment we do not have example of inputs for the hooks. We trace pack_hook with some Tensor from the inputs. The result subgraphs are added to the hashing of AotAutograd Cache. In AotAutograd we retrace the graph with the true saved tensors coming from partitioner. Backwards Compatibility: As current hooks are executed in eager mode and not all of them will be traceable - we only try to put in the graph hooks, explicitly marked by user with annotation (@_inlineable_saved_tensors_hooks). For other hooks or if compiled autograd is enabled - keep the same logic. Recompilations: Hooks are guarded with lambda guard matching function id to cause recompilation if user reruns compiled function. Aot_autograd: After partitioner prepared forward and backward module - we trace prepared at Dynamo graphs for pack and unpack hooks and inline them in epilogue of forward and prologue of backward. Forward outputs and backward inputs are changed, transparently for user. We do not try to put it close the last usage etc., relying on inductor to do this optimization. ``` INFO: TRACED GRAPH ===== Forward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== saved_tensors_pack_hook add 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== saved_tensors_unpack_hook add 3 ===== <eval_with_key>.22 from /data/users/ivankobzarev/a/pytorch/torch/fx/experimental/proxy_tensor.py:1225 in wrapped class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== Forward graph 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add, dtype = torch.float8_e4m3fn) # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]); add = None return (view, _to_copy, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph 3 ===== <eval_with_key>.21 class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", add_packed_2: "f8e4m3fn[s0, s1][s1, 1]cuda:0", tangents_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add_packed_2, dtype = torch.float32); add_packed_2 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) add_7: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(tangents_1, _to_copy); tangents_1 = _to_copy = None return (None, None, add_7) ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy chenyang78 kadeng muchulee8 amjames chauhang aakhundov Differential Revision: [D72187044](https://our.internmc.facebook.com/intern/diff/D72187044) [ghstack-poisoned]
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#148222 Goal: At the moment autograd saved tensors hooks are run in eager after compiled forward. They are executed at the same time for all saved tensors. Hooks can be used to reduce amout of memory used for saved tensors, doing quantization or offloading to cpu. This is suboptimal for optimization of peak memory. Better solution will be to put the hooks in the graph, as close as possible to the last usage of the tensor. To get user specified autograd saved tensors hooks in the graph. Logic: UX: If user specifies with torch.autograd.graph.saved_tensors_hooks(pack_gm, unpack_gm). Where pack_gm and unpack_gm are torch.fx.GraphModule. Then AotAutograd will retrace those graph modules, doing decompositions and functionalization in aot_autograd, inlining the result graphs in forward epilogue and backward prologue. User may want to use control logic in the hooks, for example applying quantization only for specific dtypes and sizes. This is also possible, user can put it into torch.fx.wrap function and use symbolic trace to make a GraphModule. In that case AotAutograd cahing will work only in case when user explicitly set to the torch.fx.wrap call_function node "user_cache_hash" metadata. If this metadata set - then aot_autograd cache can use saved cache artifact. If metadata is not set - then cache is bypassed. Dynamo: Dynamo traces pack and unpack hooks and installs them as subgraph and explicitly adds to the output_graph. (As those subgraphs are not used and will not be copied in the result by default). The complexity here is that at this moment we do not have example of inputs for the hooks. We trace pack_hook with some Tensor from the inputs. The result subgraphs are added to the hashing of AotAutograd Cache. In AotAutograd we retrace the graph with the true saved tensors coming from partitioner. Backwards Compatibility: As current hooks are executed in eager mode and not all of them will be traceable - we only try to put in the graph hooks, explicitly marked by user with annotation (@_inlineable_saved_tensors_hooks). For other hooks or if compiled autograd is enabled - keep the same logic. Recompilations: Hooks are guarded with lambda guard matching function id to cause recompilation if user reruns compiled function. Aot_autograd: After partitioner prepared forward and backward module - we trace prepared at Dynamo graphs for pack and unpack hooks and inline them in epilogue of forward and prologue of backward. Forward outputs and backward inputs are changed, transparently for user. We do not try to put it close the last usage etc., relying on inductor to do this optimization. ``` INFO: TRACED GRAPH ===== Forward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph pre saved_tensors_hooks inlining 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]) return (view, add, primals_1, primals_2) INFO: TRACED GRAPH ===== saved_tensors_pack_hook add 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== saved_tensors_unpack_hook add 3 ===== <eval_with_key>.22 from /data/users/ivankobzarev/a/pytorch/torch/fx/experimental/proxy_tensor.py:1225 in wrapped class pack_float8(torch.nn.Module): def forward(self, x_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(x_1, dtype = torch.float8_e4m3fn); x_1 = None return (torch.float32, _to_copy) INFO: TRACED GRAPH ===== Forward graph 3 ===== /data/users/ivankobzarev/a/pytorch/torch/fx/_lazy_graph_module.py class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", primals_3: "f32[s0, s1][s1, 1]cuda:0"): # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6660 in simple_fn, code: x = x + 1 add: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(primals_3, 1); primals_3 = None # No stacktrace found for following nodes _to_copy: "f8e4m3fn[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add, dtype = torch.float8_e4m3fn) # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) view: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.view.default(add, [primals_1, primals_2]); add = None return (view, _to_copy, primals_1, primals_2) INFO: TRACED GRAPH ===== Backward graph 3 ===== <eval_with_key>.21 class GraphModule(torch.nn.Module): def forward(self, primals_1: "Sym(s0)", primals_2: "Sym(s1)", add_packed_2: "f8e4m3fn[s0, s1][s1, 1]cuda:0", tangents_1: "f32[s0, s1][s1, 1]cuda:0"): # No stacktrace found for following nodes _to_copy: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten._to_copy.default(add_packed_2, dtype = torch.float32); add_packed_2 = None # File: /data/users/ivankobzarev/a/pytorch/test/functorch/test_aotdispatch.py:6661 in simple_fn, code: x = SAF.apply(x) add_7: "f32[s0, s1][s1, 1]cuda:0" = torch.ops.aten.add.Tensor(tangents_1, _to_copy); tangents_1 = _to_copy = None return (None, None, add_7) ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy chenyang78 kadeng muchulee8 amjames chauhang aakhundov Differential Revision: [D72187044](https://our.internmc.facebook.com/intern/diff/D72187044) [ghstack-poisoned]
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Stack from ghstack (oldest at bottom):
#148222
Goal:
At the moment autograd saved tensors hooks are run in eager after compiled forward.
They are executed at the same time for all saved tensors.
Hooks can be used to reduce amout of memory used for saved tensors, doing quantization or offloading to cpu.
This is suboptimal for optimization of peak memory.
Better solution will be to put the hooks in the graph, as close as possible to the last usage of the tensor.
To get user specified autograd saved tensors hooks in the graph.
Logic:
UX:
If user specifies with torch.autograd.graph.saved_tensors_hooks(pack_gm, unpack_gm).
Where pack_gm and unpack_gm are torch.fx.GraphModule.
Then AotAutograd will retrace those graph modules, doing decompositions and functionalization in aot_autograd, inlining the result graphs in forward epilogue and backward prologue.
User may want to use control logic in the hooks, for example applying quantization only for specific dtypes and sizes.
This is also possible, user can put it into torch.fx.wrap function and use symbolic trace to make a GraphModule.
In that case AotAutograd cahing will work only in case when user explicitly set to the torch.fx.wrap call_function node "user_cache_hash" metadata.
If this metadata set - then aot_autograd cache can use saved cache artifact.
If metadata is not set - then cache is bypassed.
Dynamo:
Dynamo traces pack and unpack hooks and installs them as subgraph and explicitly adds to the output_graph. (As those subgraphs are not used and will not be copied in the result by default).
The complexity here is that at this moment we do not have example of inputs for the hooks.
We trace pack_hook with some Tensor from the inputs.
The result subgraphs are added to the hashing of AotAutograd Cache.
In AotAutograd we retrace the graph with the true saved tensors coming from partitioner.
Backwards Compatibility:
As current hooks are executed in eager mode and not all of them will be traceable - we only try to put in the graph hooks, explicitly marked by user with annotation (@_inlineable_saved_tensors_hooks).
For other hooks or if compiled autograd is enabled - keep the same logic.
Recompilations:
Hooks are guarded with lambda guard matching function id to cause recompilation if user reruns compiled function.
Aot_autograd:
After partitioner prepared forward and backward module - we trace prepared at Dynamo graphs for pack and unpack hooks and inline them in epilogue of forward and prologue of backward. Forward outputs and backward inputs are changed, transparently for user.
We do not try to put it close the last usage etc., relying on inductor to do this optimization.
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov
Differential Revision: D72187044