a user friendly way to use g2c in module and an example of g2c #8632

ZiyueHuang · 2017-11-13T09:02:30Z

Description

add the interface stated in #8539

also backward compatible

As a feature requested in #8168
cc @eric-haibin-lin

Checklist

Essentials

Passed code style checking (make lint)
Changes are complete (i.e. I finished coding on this PR)
All changes have test coverage
For user-facing API changes, API doc string has been updated. For new C++ functions in header files, their functionalities and arguments are well-documented.
To my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change

Changes

unittests exist

Comments

If this change is a backward incompatible change, why must this change be made.
Interesting edge cases to note here

eric-haibin-lin · 2017-11-13T18:12:16Z

tests/python/unittest/test_module.py

@@ -78,15 +78,15 @@ def test_module_ctx_group():
        b = mx.symbol.Variable('b')
        c = a + b
    shape = (2, 5)
-    mod1 = mx.mod.Module(c, context=[mx.cpu(0)], data_names=['a', 'b'], label_names=None,
-                         group2ctxs=[{'dev1':mx.cpu(1),'dev2':mx.cpu(2)}])
+    mod1 = mx.mod.Module(c, context=[mx.cpu(0), mx.cpu(1)], data_names=['a', 'b'], label_names=None,


Please add test cases for all types of supported inputs

eric-haibin-lin · 2017-11-14T21:50:27Z

example/sparse/matrix_fact_parallel_model.py

+
+def matrix_fact_model_parallel_net(factor_size, num_hidden, max_user, max_item):
+    # set ctx_group attribute to 'dev1' for the symbols created in this scope,
+    # the symbols will be binded to the context that 'dev1' map to in group2ctxs


binded -> bound

eric-haibin-lin · 2017-11-14T21:52:31Z

example/sparse/matrix_fact_parallel_model.py

+        item_weight = mx.symbol.Variable('item_weight', stype='row_sparse')
+        item = mx.symbol.contrib.SparseEmbedding(data=item, weight=item_weight,
+                                                 input_dim=max_item, output_dim=factor_size)
+        # non-linear transformation of user features


Why not move Line 35 - Line 40 to dev2?

src/executor/graph_executor.cc:396: Check failed: device[nid] == devid (0 vs. 1) device of same output not equal to each other

I think we should spend some effort to investigate the error message before merging this

@eric-haibin-lin @reminisce I added some codes in graph_executor.cc for debug,

python matrix_factorization_model_parallel.py

[00:53:08] src/executor/graph_executor.cc:365: args context [00:53:08] src/executor/graph_executor.cc:384: nid: 0 ctx.dev_id 0 [00:53:08] src/executor/graph_executor.cc:384: nid: 1 ctx.dev_id 0 [00:53:08] src/executor/graph_executor.cc:384: nid: 3 ctx.dev_id 1 [00:53:08] src/executor/graph_executor.cc:384: nid: 4 ctx.dev_id 1 [00:53:08] src/executor/graph_executor.cc:384: nid: 6 ctx.dev_id 0 [00:53:08] src/executor/graph_executor.cc:384: nid: 7 ctx.dev_id 0 [00:53:08] src/executor/graph_executor.cc:384: nid: 12 ctx.dev_id 1 [00:53:08] src/executor/graph_executor.cc:386: ===================== [00:53:08] src/executor/graph_executor.cc:387: 1 num_forward_outputs [00:53:08] src/executor/graph_executor.cc:388: 5 g.outputs.size() [00:53:08] src/executor/graph_executor.cc:389: 7 arg_grad_ctxes.size() [00:53:08] src/executor/graph_executor.cc:393: arg grads contexts [00:53:08] src/executor/graph_executor.cc:397: nid 19 ctx 0 [00:53:08] src/executor/graph_executor.cc:397: nid 18 ctx 0 [00:53:08] src/executor/graph_executor.cc:397: nid 18 ctx 1 [00:53:08] src/executor/graph_executor.cc:397: nid 20 ctx 1 [00:53:08] src/executor/graph_executor.cc:399: ===================== [00:53:08] src/executor/graph_executor.cc:409: fail nid 18 ctx 1 [00:53:08] src/executor/graph_executor.cc:423: node 0 var user [00:53:08] src/executor/graph_executor.cc:423: node 1 var user_weight [00:53:08] src/executor/graph_executor.cc:425: node 2 _contrib_SparseEmbedding [00:53:08] src/executor/graph_executor.cc:428: input 0 (entry id) [00:53:08] src/executor/graph_executor.cc:428: input 1 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 2 (entry id) [00:53:08] src/executor/graph_executor.cc:423: node 3 var ufcweight [00:53:08] src/executor/graph_executor.cc:423: node 4 var ufcbias [00:53:08] src/executor/graph_executor.cc:425: node 5 FullyConnected [00:53:08] src/executor/graph_executor.cc:428: input 2 (entry id) [00:53:08] src/executor/graph_executor.cc:428: input 3 (entry id) [00:53:08] src/executor/graph_executor.cc:428: input 4 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 5 (entry id) [00:53:08] src/executor/graph_executor.cc:423: node 6 var item [00:53:08] src/executor/graph_executor.cc:423: node 7 var item_weight [00:53:08] src/executor/graph_executor.cc:425: node 8 _contrib_SparseEmbedding [00:53:08] src/executor/graph_executor.cc:428: input 6 (entry id) [00:53:08] src/executor/graph_executor.cc:428: input 7 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 8 (entry id) [00:53:08] src/executor/graph_executor.cc:425: node 9 elemwise_mul [00:53:08] src/executor/graph_executor.cc:428: input 5 (entry id) [00:53:08] src/executor/graph_executor.cc:428: input 8 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 9 (entry id) [00:53:08] src/executor/graph_executor.cc:425: node 10 sum [00:53:08] src/executor/graph_executor.cc:428: input 9 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 10 (entry id) [00:53:08] src/executor/graph_executor.cc:425: node 11 Flatten [00:53:08] src/executor/graph_executor.cc:428: input 10 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 11 (entry id) [00:53:08] src/executor/graph_executor.cc:423: node 12 var score [00:53:08] src/executor/graph_executor.cc:425: node 13 LinearRegressionOutput [00:53:08] src/executor/graph_executor.cc:428: input 11 (entry id) [00:53:08] src/executor/graph_executor.cc:428: input 12 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 13 (entry id) [00:53:08] src/executor/graph_executor.cc:425: node 14 _backward_LinearRegressionOutput [00:53:08] src/executor/graph_executor.cc:428: input 12 (entry id) [00:53:08] src/executor/graph_executor.cc:428: input 13 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 14 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 15 (entry id) [00:53:08] src/executor/graph_executor.cc:425: node 15 _backward_copy [00:53:08] src/executor/graph_executor.cc:428: input 14 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 16 (entry id) [00:53:08] src/executor/graph_executor.cc:425: node 16 _backward_sum [00:53:08] src/executor/graph_executor.cc:428: input 16 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 17 (entry id) [00:53:08] src/executor/graph_executor.cc:425: node 17 _backward_mul [00:53:08] src/executor/graph_executor.cc:428: input 17 (entry id) [00:53:08] src/executor/graph_executor.cc:428: input 5 (entry id) [00:53:08] src/executor/graph_executor.cc:428: input 8 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 18 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 19 (entry id) [00:53:08] src/executor/graph_executor.cc:425: node 18 _backward_FullyConnected [00:53:08] src/executor/graph_executor.cc:428: input 18 (entry id) [00:53:08] src/executor/graph_executor.cc:428: input 2 (entry id) [00:53:08] src/executor/graph_executor.cc:428: input 3 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 20 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 21 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 22 (entry id) [00:53:08] src/executor/graph_executor.cc:425: node 19 _backward_SparseEmbedding [00:53:08] src/executor/graph_executor.cc:428: input 20 (entry id) [00:53:08] src/executor/graph_executor.cc:428: input 0 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 23 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 24 (entry id) [00:53:08] src/executor/graph_executor.cc:425: node 20 _backward_SparseEmbedding [00:53:08] src/executor/graph_executor.cc:428: input 19 (entry id) [00:53:08] src/executor/graph_executor.cc:428: input 6 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 25 (entry id) [00:53:08] src/executor/graph_executor.cc:432: output 26 (entry id) [00:53:08] /home/hanfeng/zyh/zyhmxnet/dmlc-core/include/dmlc/./logging.h:308: [00:53:08] src/executor/graph_executor.cc:436: Check failed: device[nid] == devid (0 vs. 1) fullyconnected0_backward device of same output not equal to each other

So as you can see, the contexts of node 3 var ufcweight and node 4 var ufcbias are at dev1, but the contexts of their grads are at dev1 and dev2 because the outputs below arg grads contexts

[00:53:08] src/executor/graph_executor.cc:393: arg grads contexts [00:53:08] src/executor/graph_executor.cc:397: nid 19 ctx 0 [00:53:08] src/executor/graph_executor.cc:397: nid 18 ctx 0 [00:53:08] src/executor/graph_executor.cc:397: nid 18 ctx 1 [00:53:08] src/executor/graph_executor.cc:397: nid 20 ctx 1

As you can see in the graph structure, node 18 is _backward_FullyConnected so nid 18 ctx 0 and nid 18 ctx 1 are the grads of ufcweight and ufcbias.

eric-haibin-lin · 2017-11-14T21:53:30Z

example/sparse/matrix_factorization_model_parallel.py

+parser.add_argument('--batch-size', type=int, default=1024,
+                    help='number of examples per batch')
+parser.add_argument('--print-every', type=int, default=100,
+                    help='logging frequency')


I made a typo. Should be logging interval instead of logging frequency

eric-haibin-lin · 2017-11-14T21:54:01Z

example/sparse/matrix_factorization_model_parallel.py

+
+    # construct the model
+    net = matrix_fact_model_parallel_net(factor_size, factor_size, max_user, max_movies)
+    a = time.time()


remove this extra line, too?

eric-haibin-lin · 2017-11-14T21:55:30Z

example/sparse/matrix_factorization_model_parallel.py

+    # create kvstore
+    kv = mx.kvstore.create('local') if num_gpus > 1 else None
+
+    # initialize the module


It would be clearer if we first create the variable group2ctxs={'dev1':mx.cpu(), 'dev2':[mx.gpu(i) for i in range(num_gpus)]}) with some documentation to explain what is going on, then pass it to Module constructor.

eric-haibin-lin · 2017-11-14T21:58:27Z

example/sparse/readme.md

+
+## Model Parallel
+
+The example demonstrates the basic usage of `group2ctxs` in `Module`, which allows part of model on cpu and another part of model on gpu.


which allows one part of the model trained on cpu and the other on gpu.

eric-haibin-lin · 2017-11-14T21:59:26Z

tests/python/unittest/test_module.py

+        assert np.all(mod1_input_grads[0].asnumpy() == mod2_input_grads[0].asnumpy())
+        assert np.all(mod1_input_grads[1].asnumpy() == mod2_input_grads[1].asnumpy())
+
+    test_module_ctx_group_impl([mx.cpu(0)], {'dev1': mx.cpu(1), 'dev2': mx.cpu(2)})


nit: usually we name internal functions in test_xxx as check_xxx - check_module_ctx_group

… into mod-g2c

mbaijal · 2017-11-17T18:10:25Z

Hi @ZiyueHuang
I see this PR is WIP. Does that mean it is not meant for 1.0?
It is failing lint checks on Apache Jenkins.

ZiyueHuang · 2017-11-17T18:22:58Z

Hi @mbaijal
Yes. group2context seems problematic and we need debug it.

This reverts commit caabdc5.

…e#8632) * a user friendly way to use g2c in module * also support g2c to be list * update * update test * g2c example * Update matrix_factorization_model_parallel.py * address comments * update * update * remove fc * debug g2c * Revert "debug g2c" This reverts commit caabdc5. * update * move g2c example to another folder * update * readme

ZiyueHuang added 3 commits November 13, 2017 16:59

a user friendly way to use g2c in module

df62733

also support g2c to be list

e174316

update

7bbde95

eric-haibin-lin reviewed Nov 13, 2017

View reviewed changes

eric-haibin-lin self-assigned this Nov 13, 2017

ZiyueHuang added 3 commits November 14, 2017 21:10

update test

8e28dab

g2c example

0a40467

Update matrix_factorization_model_parallel.py

ab6ca5f

ZiyueHuang changed the title ~~a user friendly way to use g2c in module~~ a user friendly way to use g2c in module and an example of g2c Nov 14, 2017

eric-haibin-lin reviewed Nov 14, 2017

View reviewed changes

ZiyueHuang added 7 commits November 15, 2017 23:31

Merge remote-tracking branch 'upstream/master' into mod-g2c

2919995

address comments

da7c6b4

update

7ac3c70

Merge branch 'mod-g2c' of https://github.com/ZiyueHuang/incubator-mxnet…

c31dad8

… into mod-g2c

update

aed4933

remove fc

77734d5

debug g2c

caabdc5

ZiyueHuang changed the title ~~a user friendly way to use g2c in module and an example of g2c~~ [WIP] a user friendly way to use g2c in module and an example of g2c Nov 17, 2017

ZiyueHuang added 3 commits November 21, 2017 16:13

Revert "debug g2c"

9e3fb2e

This reverts commit caabdc5.

Merge remote-tracking branch 'upstream/master' into mod-g2c

d7eb5e4

update

11e7cbd

ZiyueHuang changed the title ~~[WIP] a user friendly way to use g2c in module and an example of g2c~~ a user friendly way to use g2c in module and an example of g2c Nov 21, 2017

ZiyueHuang added 3 commits November 22, 2017 01:04

move g2c example to another folder

7f2074c

update

ad02f4c

readme

0da4313

piiswrong merged commit ec6144f into apache:master Nov 22, 2017

ZiyueHuang deleted the mod-g2c branch January 30, 2018 11:31

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a user friendly way to use g2c in module and an example of g2c #8632

a user friendly way to use g2c in module and an example of g2c #8632

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mbaijal commented Nov 17, 2017

ZiyueHuang commented Nov 17, 2017


		## Model Parallel

		The example demonstrates the basic usage of `group2ctxs` in `Module`, which allows part of model on cpu and another part of model on gpu.

a user friendly way to use g2c in module and an example of g2c #8632

a user friendly way to use g2c in module and an example of g2c #8632

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mbaijal commented Nov 17, 2017

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