[compiled autograd] log aot id for CompiledFunctionBackward #133115

xmfan · 2024-08-09T18:59:55Z

Stack from ghstack (oldest at bottom):

Partially addresses #132939. Adds the AOT ID after the CompiledFunctionBackward annotation in verbose compiled autograd logging

default (no change):
https://interncache-all.fbcdn.net/manifold/tlparse_reports/tree/logs/.tmp8WCSLf/dedicated_log_torch_trace_xw3ktsi_.log/index.html

TORCH_LOGS="compiled_autograd_verbose":
https://interncache-all.fbcdn.net/manifold/tlparse_reports/tree/logs/.tmp8WCSLf/dedicated_log_torch_trace_gsc9q_43.log/index.html

# File: /data/users/xmfan/a/pytorch/torch/_dynamo/compiled_autograd.py:361 in set_node_origin, code: CompiledFunctionBackward1 (NodeCall 2)
clone: "f32[4]" = torch.ops.aten.clone.default(expand, memory_format = torch.contiguous_format);  expand = None
cos: "f32[4]" = torch.ops.aten.cos.default(getitem_1);  getitem_1 = None
mul: "f32[4]" = torch.ops.aten.mul.Tensor(clone, cos);  clone = cos = None

# File: /data/users/xmfan/a/pytorch/torch/_dynamo/compiled_autograd.py:361 in set_node_origin, code: CompiledFunctionBackward0 (NodeCall 3)
cos_1: "f32[4]" = torch.ops.aten.cos.default(getitem_2)
mul_1: "f32[4]" = torch.ops.aten.mul.Tensor(mul, cos_1);  mul = cos_1 = None

cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang

[ghstack-poisoned]

pytorch-bot · 2024-08-09T18:59:59Z

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As of commit 7c48ddd with merge base e7b870c ():

FLAKY - The following job failed but was likely due to flakiness present on trunk:

inductor / linux-jammy-cpu-py3.12-gcc11-inductor-halide / test (inductor-halide, 1, 1, linux.12xlarge) (gh) (similar failure)
inductor/test_halide.py::CpuHalideTests::test_scalar_output_cpu

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ghstack-source-id: ff8048d Pull Request resolved: #133115

Partially addresses #132939 default (no change): https://interncache-all.fbcdn.net/manifold/tlparse_reports/tree/logs/.tmp8WCSLf/dedicated_log_torch_trace_xw3ktsi_.log/index.html TORCH_LOGS="compiled_autograd_verbose": https://interncache-all.fbcdn.net/manifold/tlparse_reports/tree/logs/.tmp8WCSLf/dedicated_log_torch_trace_gsc9q_43.log/index.html ```python # File: /data/users/xmfan/a/pytorch/torch/_dynamo/compiled_autograd.py:361 in set_node_origin, code: CompiledFunctionBackward1 (NodeCall 2) clone: "f32[4]" = torch.ops.aten.clone.default(expand, memory_format = torch.contiguous_format); expand = None cos: "f32[4]" = torch.ops.aten.cos.default(getitem_1); getitem_1 = None mul: "f32[4]" = torch.ops.aten.mul.Tensor(clone, cos); clone = cos = None # File: /data/users/xmfan/a/pytorch/torch/_dynamo/compiled_autograd.py:361 in set_node_origin, code: CompiledFunctionBackward0 (NodeCall 3) cos_1: "f32[4]" = torch.ops.aten.cos.default(getitem_2) mul_1: "f32[4]" = torch.ops.aten.mul.Tensor(mul, cos_1); mul = cos_1 = None ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames [ghstack-poisoned]

ghstack-source-id: 367d8f6 Pull Request resolved: #133115

yf225 · 2024-08-13T01:19:40Z

test/inductor/test_compiled_autograd.py

+            yield model[2].bias.grad
+
+        logs, ctx = logs_to_string(
+            torch._dynamo.compiled_autograd.__name__, "compiled_autograd_verbose"


Should we always enable it instead of only under compiled_autograd_verbose? I believe this feature would be very useful for debugging internal jobs in the future, and if this aot id logging is on by default then we don't need to ask user to go back and run the job again with verbose enabled. (Or alternatively we can also ask user to always turn on compiled_autograd_verbose)

yeah this one is not on the hot path so we can do that

also just thinking:

(1) do compiled autograd logs gets turned on for most jobs internally?

(2) is there anything else we can put into tlparse from compiled autograd that we aren't right now?

for 1, tlparse seems to have non-verbose logs by default

2, there's probably a bunch of cross-artifacts analysis we can do in tlparse. but i feel like adding logs to the compiled autograd artifacts is sufficient for the problems we're seeing rn

@bdhirsh another issue we saw is that when going from CA Dynamo graph to AOTAutograd joint graph, the source of each FX node in AOTAutograd joint graph is lost (i.e. we don't know which Dynamo graph FX node it comes from anymore). Curious is there a way to preserve this information?

hmm do you have an example, or a repro you can file?

I tried running the repro below, and when I the logs from TORCH_LOGS="aot,+compiled_autograd", it seems like the stacktraces are lost when compiled autograd first gets a graph, before it sends it to AOTDispatcher. log output: P1528486054

code:

import torch @torch.compile def f(x): return torch.matmul(x, x).sin() x = torch.randn(4, 4, requires_grad=True) with torch._dynamo.utils.maybe_enable_compiled_autograd(True): out = f(x) out.sum().backward()

need to use compiled_autograd_verbose instead of +compiled_autograd (it's a separate artifact since artifacts don't support verbosity levels)

e.g. P1528511564

Filed here #133567, I'll take a look

This seems to fix it #133574

Partially addresses #132939. Adds the AOT ID after the CompiledFunctionBackward annotation in verbose compiled autograd logging default (no change): https://interncache-all.fbcdn.net/manifold/tlparse_reports/tree/logs/.tmp8WCSLf/dedicated_log_torch_trace_xw3ktsi_.log/index.html TORCH_LOGS="compiled_autograd_verbose": https://interncache-all.fbcdn.net/manifold/tlparse_reports/tree/logs/.tmp8WCSLf/dedicated_log_torch_trace_gsc9q_43.log/index.html ```python # File: /data/users/xmfan/a/pytorch/torch/_dynamo/compiled_autograd.py:361 in set_node_origin, code: CompiledFunctionBackward1 (NodeCall 2) clone: "f32[4]" = torch.ops.aten.clone.default(expand, memory_format = torch.contiguous_format); expand = None cos: "f32[4]" = torch.ops.aten.cos.default(getitem_1); getitem_1 = None mul: "f32[4]" = torch.ops.aten.mul.Tensor(clone, cos); clone = cos = None # File: /data/users/xmfan/a/pytorch/torch/_dynamo/compiled_autograd.py:361 in set_node_origin, code: CompiledFunctionBackward0 (NodeCall 3) cos_1: "f32[4]" = torch.ops.aten.cos.default(getitem_2) mul_1: "f32[4]" = torch.ops.aten.mul.Tensor(mul, cos_1); mul = cos_1 = None ``` cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx ipiszy yf225 chenyang78 kadeng muchulee8 ColinPeppler amjames desertfire chauhang [ghstack-poisoned]

FIXES #132939 Compiled autograd's trace of the AOT backward may result in some additional ops e.g. clone to make contiguous, trace_wrapped HOPs, so the graphs may be slightly offset from each other hf_Whisper example: https://interncache-all.fbcdn.net/manifold/tlparse_reports/tree/logs/.tmpNv89Pu/index.html fsdp2 example: https://interncache-all.fbcdn.net/manifold/tlparse_reports/tree/logs/.tmpPdKssS/rank_0/index.html Unit test example: https://interncache-all.fbcdn.net/manifold/tlparse_reports/tree/logs/.tmpvoQsnl/index.html ```python ===== Compiled autograd graph ===== <eval_with_key>.14 class CompiledAutograd(torch.nn.Module): def forward(self, inputs, sizes, scalars, hooks): # No stacktrace found for following nodes getitem: "f32[]cpu" = inputs[0] aot1_primals_1: "f32[4]cpu" = inputs[1] aot1_primals_2: "f32[4]cpu" = inputs[2] aot0_sin: "f32[4]cpu" = inputs[3] aot0_cos: "f32[4]cpu" = inputs[4] getitem_5: "f32[4]cpu" = inputs[5]; inputs = None # File: /data/users/xmfan/a/pytorch/torch/_dynamo/compiled_autograd.py:444 in set_node_origin, code: SumBackward0 (NodeCall 1) expand: "f32[4]cpu" = torch.ops.aten.expand.default(getitem, [4]); getitem = None # File: /data/users/xmfan/a/pytorch/torch/_dynamo/compiled_autograd.py:444 in set_node_origin, code: CompiledFunctionBackward1 (NodeCall 2) aot1_tangents_1: "f32[4]cpu" = torch.ops.aten.clone.default(expand, memory_format = torch.contiguous_format); expand = None aot1_sin_1: "f32[4]cpu" = torch.ops.aten.sin.default(aot1_primals_2); aot1_primals_2 = None aot1_neg: "f32[4]cpu" = torch.ops.aten.neg.default(aot1_sin_1); aot1_sin_1 = None aot0_tangents_2: "f32[4]cpu" = torch.ops.aten.mul.Tensor(aot1_tangents_1, aot1_neg); aot1_neg = None aot1_cos_1: "f32[4]cpu" = torch.ops.aten.cos.default(aot1_primals_1); aot1_primals_1 = None aot0_tangents_1: "f32[4]cpu" = torch.ops.aten.mul.Tensor(aot1_tangents_1, aot1_cos_1); aot1_tangents_1 = aot1_cos_1 = None # File: /data/users/xmfan/a/pytorch/torch/_dynamo/compiled_autograd.py:444 in set_node_origin, code: CompiledFunctionBackward0 (NodeCall 3) aot0_neg: "f32[4]cpu" = torch.ops.aten.neg.default(aot0_sin); aot0_sin = None aot0_mul: "f32[4]cpu" = torch.ops.aten.mul.Tensor(aot0_tangents_2, aot0_neg); aot0_tangents_2 = aot0_neg = None aot0_mul_1: "f32[4]cpu" = torch.ops.aten.mul.Tensor(aot0_tangents_1, aot0_cos); aot0_tangents_1 = aot0_cos = None aot0_add: "f32[4]cpu" = torch.ops.aten.add.Tensor(aot0_mul, aot0_mul_1); aot0_mul = aot0_mul_1 = None # File: /data/users/xmfan/a/pytorch/torch/_dynamo/compiled_autograd.py:444 in set_node_origin, code: torch::autograd::AccumulateGrad (NodeCall 4) accumulate_grad_ = torch.ops.inductor.accumulate_grad_.default(getitem_5, aot0_add); getitem_5 = aot0_add = accumulate_grad_ = None _exec_final_callbacks_stub = torch__dynamo_external_utils__exec_final_callbacks_stub(); _exec_final_callbacks_stub = None return [] ``` where aot1 is ```python class GraphModule(torch.nn.Module): def forward(self, primals_1: "f32[4][1]cpu", primals_2: "f32[4][1]cpu", tangents_1: "f32[4][1]cpu"): # File: /data/users/xmfan/a/pytorch/test/inductor/test_compiled_autograd.py:2233 in torch_dynamo_resume_in_f_at_2232, code: return tmp1.sin() + tmp2.cos() sin_1: "f32[4][1]cpu" = torch.ops.aten.sin.default(primals_2); primals_2 = None neg: "f32[4][1]cpu" = torch.ops.aten.neg.default(sin_1); sin_1 = None mul: "f32[4][1]cpu" = torch.ops.aten.mul.Tensor(tangents_1, neg); neg = None cos_1: "f32[4][1]cpu" = torch.ops.aten.cos.default(primals_1); primals_1 = None mul_1: "f32[4][1]cpu" = torch.ops.aten.mul.Tensor(tangents_1, cos_1); tangents_1 = cos_1 = None return (mul_1, mul) ``` and aot0 is ```python class GraphModule(torch.nn.Module): def forward(self, sin: "f32[4][1]cpu", cos: "f32[4][1]cpu", tangents_1: "f32[4][1]cpu", tangents_2: "f32[4][1]cpu"): # File: /data/users/xmfan/a/pytorch/test/inductor/test_compiled_autograd.py:2231 in f, code: tmp2 = x.cos() neg: "f32[4][1]cpu" = torch.ops.aten.neg.default(sin); sin = None mul: "f32[4][1]cpu" = torch.ops.aten.mul.Tensor(tangents_2, neg); tangents_2 = neg = None # File: /data/users/xmfan/a/pytorch/test/inductor/test_compiled_autograd.py:2230 in f, code: tmp1 = x.sin() mul_1: "f32[4][1]cpu" = torch.ops.aten.mul.Tensor(tangents_1, cos); tangents_1 = cos = None # File: /data/users/xmfan/a/pytorch/test/inductor/test_compiled_autograd.py:2230 in f, code: tmp1 = x.sin() add: "f32[4][1]cpu" = torch.ops.aten.add.Tensor(mul, mul_1); mul = mul_1 = None return (add,) ``` Pull Request resolved: #133148 Approved by: https://github.com/jansel ghstack dependencies: #133115

Pull Request resolved: #133541 Approved by: https://github.com/yf225, https://github.com/bdhirsh ghstack dependencies: #133115, #133148

…ch#133148) FIXES pytorch#132939 Compiled autograd's trace of the AOT backward may result in some additional ops e.g. clone to make contiguous, trace_wrapped HOPs, so the graphs may be slightly offset from each other hf_Whisper example: https://interncache-all.fbcdn.net/manifold/tlparse_reports/tree/logs/.tmpNv89Pu/index.html fsdp2 example: https://interncache-all.fbcdn.net/manifold/tlparse_reports/tree/logs/.tmpPdKssS/rank_0/index.html Unit test example: https://interncache-all.fbcdn.net/manifold/tlparse_reports/tree/logs/.tmpvoQsnl/index.html ```python ===== Compiled autograd graph ===== <eval_with_key>.14 class CompiledAutograd(torch.nn.Module): def forward(self, inputs, sizes, scalars, hooks): # No stacktrace found for following nodes getitem: "f32[]cpu" = inputs[0] aot1_primals_1: "f32[4]cpu" = inputs[1] aot1_primals_2: "f32[4]cpu" = inputs[2] aot0_sin: "f32[4]cpu" = inputs[3] aot0_cos: "f32[4]cpu" = inputs[4] getitem_5: "f32[4]cpu" = inputs[5]; inputs = None # File: /data/users/xmfan/a/pytorch/torch/_dynamo/compiled_autograd.py:444 in set_node_origin, code: SumBackward0 (NodeCall 1) expand: "f32[4]cpu" = torch.ops.aten.expand.default(getitem, [4]); getitem = None # File: /data/users/xmfan/a/pytorch/torch/_dynamo/compiled_autograd.py:444 in set_node_origin, code: CompiledFunctionBackward1 (NodeCall 2) aot1_tangents_1: "f32[4]cpu" = torch.ops.aten.clone.default(expand, memory_format = torch.contiguous_format); expand = None aot1_sin_1: "f32[4]cpu" = torch.ops.aten.sin.default(aot1_primals_2); aot1_primals_2 = None aot1_neg: "f32[4]cpu" = torch.ops.aten.neg.default(aot1_sin_1); aot1_sin_1 = None aot0_tangents_2: "f32[4]cpu" = torch.ops.aten.mul.Tensor(aot1_tangents_1, aot1_neg); aot1_neg = None aot1_cos_1: "f32[4]cpu" = torch.ops.aten.cos.default(aot1_primals_1); aot1_primals_1 = None aot0_tangents_1: "f32[4]cpu" = torch.ops.aten.mul.Tensor(aot1_tangents_1, aot1_cos_1); aot1_tangents_1 = aot1_cos_1 = None # File: /data/users/xmfan/a/pytorch/torch/_dynamo/compiled_autograd.py:444 in set_node_origin, code: CompiledFunctionBackward0 (NodeCall 3) aot0_neg: "f32[4]cpu" = torch.ops.aten.neg.default(aot0_sin); aot0_sin = None aot0_mul: "f32[4]cpu" = torch.ops.aten.mul.Tensor(aot0_tangents_2, aot0_neg); aot0_tangents_2 = aot0_neg = None aot0_mul_1: "f32[4]cpu" = torch.ops.aten.mul.Tensor(aot0_tangents_1, aot0_cos); aot0_tangents_1 = aot0_cos = None aot0_add: "f32[4]cpu" = torch.ops.aten.add.Tensor(aot0_mul, aot0_mul_1); aot0_mul = aot0_mul_1 = None # File: /data/users/xmfan/a/pytorch/torch/_dynamo/compiled_autograd.py:444 in set_node_origin, code: torch::autograd::AccumulateGrad (NodeCall 4) accumulate_grad_ = torch.ops.inductor.accumulate_grad_.default(getitem_5, aot0_add); getitem_5 = aot0_add = accumulate_grad_ = None _exec_final_callbacks_stub = torch__dynamo_external_utils__exec_final_callbacks_stub(); _exec_final_callbacks_stub = None return [] ``` where aot1 is ```python class GraphModule(torch.nn.Module): def forward(self, primals_1: "f32[4][1]cpu", primals_2: "f32[4][1]cpu", tangents_1: "f32[4][1]cpu"): # File: /data/users/xmfan/a/pytorch/test/inductor/test_compiled_autograd.py:2233 in torch_dynamo_resume_in_f_at_2232, code: return tmp1.sin() + tmp2.cos() sin_1: "f32[4][1]cpu" = torch.ops.aten.sin.default(primals_2); primals_2 = None neg: "f32[4][1]cpu" = torch.ops.aten.neg.default(sin_1); sin_1 = None mul: "f32[4][1]cpu" = torch.ops.aten.mul.Tensor(tangents_1, neg); neg = None cos_1: "f32[4][1]cpu" = torch.ops.aten.cos.default(primals_1); primals_1 = None mul_1: "f32[4][1]cpu" = torch.ops.aten.mul.Tensor(tangents_1, cos_1); tangents_1 = cos_1 = None return (mul_1, mul) ``` and aot0 is ```python class GraphModule(torch.nn.Module): def forward(self, sin: "f32[4][1]cpu", cos: "f32[4][1]cpu", tangents_1: "f32[4][1]cpu", tangents_2: "f32[4][1]cpu"): # File: /data/users/xmfan/a/pytorch/test/inductor/test_compiled_autograd.py:2231 in f, code: tmp2 = x.cos() neg: "f32[4][1]cpu" = torch.ops.aten.neg.default(sin); sin = None mul: "f32[4][1]cpu" = torch.ops.aten.mul.Tensor(tangents_2, neg); tangents_2 = neg = None # File: /data/users/xmfan/a/pytorch/test/inductor/test_compiled_autograd.py:2230 in f, code: tmp1 = x.sin() mul_1: "f32[4][1]cpu" = torch.ops.aten.mul.Tensor(tangents_1, cos); tangents_1 = cos = None # File: /data/users/xmfan/a/pytorch/test/inductor/test_compiled_autograd.py:2230 in f, code: tmp1 = x.sin() add: "f32[4][1]cpu" = torch.ops.aten.add.Tensor(mul, mul_1); mul = mul_1 = None return (add,) ``` Pull Request resolved: pytorch#133148 Approved by: https://github.com/jansel ghstack dependencies: pytorch#133115

…ch#133541) Pull Request resolved: pytorch#133541 Approved by: https://github.com/yf225, https://github.com/bdhirsh ghstack dependencies: pytorch#133115, pytorch#133148

[compiled autograd] log aot id for CompiledFunctionBackward

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[compiled autograd] log aot id for CompiledFunctionBackward

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ghstack-source-id: ff8048d Pull Request resolved: #133115

xmfan added a commit that referenced this pull request Aug 9, 2024

[compiled autograd] log aot id for CompiledFunctionBackward

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ghstack-source-id: 367d8f6 Pull Request resolved: #133115

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[compiled autograd] use same graph node names as AOTDispatcher #133148

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[compiled autograd] move non-hot path logs into default logger #133541

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pytorch-bot bot commented Aug 9, 2024 •

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yf225 Aug 13, 2024 •

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xmfan Aug 15, 2024 •

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