Dealing with API changes in TF 2.0 #306
Comments
goldingn
changed the title
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According to this page TF 2.0 release is planned for Q2 2019, which has passed, and a beta is available. So I'm assuming the release will be happening soon. |
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Added deprecation warnings to the irreplaceable optimisers in 27b1ed3 |
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Suppressed TensorFlow warning about contrib being deprecated in 87b29cb |
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It would be worthwhile installing the v2.0 beta and attempting to test the current version of the package to look for the biggest pain points. |
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There's an experimental branch for running greta on TF 2.0.0. It's currently failing, apparently because of some eager-mode thing. |
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any updates? |
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This will be a top priority for the new greta dev, who will be starting at the end of March. I haven't had time to look at this this year. It's all doable, the biggest hurdling is switching everything internally from graph mode to eager mode 🙄 |
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thanks, looking forward to it! Yup, having eager execution instead of reserving all the GPU memory will be much more friendly for multi-tasking on the GPU, as will support for python 3.8 (now the default flavor on the current ubuntu LTS) |
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Good to know. I hadn't realised there were performance improvements with eager mode! |
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@goldingn exciting news :-) You've seen the new python-less torch for R, right? e.g. this intro series https://blogs.rstudio.com/ai/posts/2020-10-09-torch-optim/ Let us know how we can help :-) cc @dfalbel |
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We have! Cutting that python installation step out is the main motivation. Though the new reticulate python dependency system (#357) looks like it will be helpful for TF too. Looking forward to having some time and some help to lessen greta's installation pain. |
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Sweet, I hadn't seen the python-less torch either. Very nice! The new reticulate R packaging stuff is also rather slick. Antecedently, The (python) libraries we rely on for RL (stable-baselines) recently switched from tensorflow 1.x to pytorch though and it's been a huge improvement on the GPU side. not only do we get the eager execution / shared GPU, but it's also a lot less finicky about being tied to precise version of some cuda lib. No idea how well the torch suite matches up with what you need on the greta end though. |
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One possible path forwards for this:
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It seems like it might be possible to switch off eager mode (maybe!) in 2.6 or 2.7 - rstudio/tensorflow#498 - however there are still issues with installing all of the python requisites on an M1 mac. |
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OK so we can happily create library(greta)
#>
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#>
#> binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#>
#> %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,
#> eigen, forwardsolve, gamma, identity, rowMeans, rowSums, sweep,
#> tapply
# initialise greta
normal(0,1)
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✓ Initialising python and checking dependencies ... done!
#>
#> ℹ Disabling TF eager execution with: `tf$compat$v1$disable_eager_execution()`
#> greta array (variable following a normal distribution)
#>
#> [,1]
#> [1,] ?
# simple model
m <- model(normal(0, 1))
#> Loaded Tensorflow version 2.6.0
#> Error in py_call_impl(callable, dots$args, dots$keywords): ValueError: Expected integer, got dtype <class 'float'>.Created on 2022-01-31 by the reprex package (v2.0.1) Also there's an annoying "Tensorflow 2.6.0 Loaded" command that appears...I swear that was supposed to happen earlier in the install process |
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Tried to add more of the "disabling" features of V1 into version 2, but this did not seem to stop this overall error: library(greta)
#>
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#>
#> binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#>
#> %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,
#> eigen, forwardsolve, gamma, identity, rowMeans, rowSums, sweep,
#> tapply
# initialise greta
normal(0,1)
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✓ Initialising python and checking dependencies ... done!
#>
#> ℹ Disabling TF eager execution with: `tf$compat$v1$disable_eager_execution()`
#> ℹ Disabling TF V2 behaviour: `tf$compat$v1$disable_v2_behavior()`
#> ℹ Disabling TF V2 tensorshape: `tf$compat$v1$disable_v2_tensorshape()`
#> ℹ Disabling TF V2 control flow: `tf$compat$v1$disable_control_flow_v2()`
#> ℹ Disabling TF V2 resource variables:
#> `tf$compat$v1$disable_resource_variables()`
#> ℹ Disabling TF V2 tensor equality: `tf$compat$v1$disable_tensor_equality()`
#> greta array (variable following a normal distribution)
#>
#> [,1]
#> [1,] ?
# simple model
m <- model(normal(0, 1))
#> Loaded Tensorflow version 2.6.0
#> Error in py_call_impl(callable, dots$args, dots$keywords): ValueError: Expected integer, got dtype <class 'float'>.Created on 2022-01-31 by the reprex package (v2.0.1) At least it's a consistent bug? Note that these changes are being implemented/tested in this branch: #482 |
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From memory, the instinct is to debug at the |
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OK turns out that a bunch of the errors were actually because this PR wasn't in sync with master, so we were dealing with the issues from #463 Now...we have a new set of spicier issues. library(greta)
#>
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#>
#> binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#>
#> %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,
#> eigen, forwardsolve, gamma, identity, rowMeans, rowSums, sweep,
#> tapply
# initialise greta
normal(0,1)
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✓ Initialising python and checking dependencies ... done!
#>
#> ℹ Disabling TF eager execution with: `tf$compat$v1$disable_eager_execution()`
#> greta array (variable following a normal distribution)
#>
#> [,1]
#> [1,] ?
# simple model
m <- model(normal(0, 1))
#> Loaded Tensorflow version 2.6.0
draws <- mcmc(m)
#> Error in py_call_impl(callable, dots$args, dots$keywords): TypeError: __init__() got an unexpected keyword argument 'seed'Created on 2022-02-07 by the reprex package (v2.0.1) And library(greta)
#>
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#>
#> binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#>
#> %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,
#> eigen, forwardsolve, gamma, identity, rowMeans, rowSums, sweep,
#> tapply
x <- rnorm(10)
y <- as_data(rnorm(10))
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✓ Initialising python and checking dependencies ... done!
#>
#> ℹ Disabling TF eager execution with: `tf$compat$v1$disable_eager_execution()`
alpha <- normal(0,1)
beta <- normal(0,1)
mu <- alpha + beta * x
sigma <- normal(0, 0.1, truncation = c(0, 0.5))
distribution(y) <- normal(mu, sigma)
m <- model(y)
#> Loaded Tensorflow version 2.6.0
#> Error in py_get_attr_impl(x, name, silent): AttributeError: module 'tensorflow_probability.python.bijectors' has no attribute 'AffineScalar'Created on 2022-02-07 by the reprex package (v2.0.1) |
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More curiousities arise - working on #482 When debugging: mcmc(model(normal(0,1))I get: library(greta)
#>
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#>
#> binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#>
#> %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,
#> eigen, forwardsolve, gamma, identity, rowMeans, rowSums, sweep,
#> tapply
# initialise greta
mcmc(model(normal(0,1)))
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✓ Initialising python and checking dependencies ... done!
#>
#> ℹ Disabling TF eager execution with: `tf$compat$v1$disable_eager_execution()`
#> Loaded Tensorflow version 2.6.0
#> Error in py_call_impl(callable, dots$args, dots$keywords): TypeError: __init__() got an unexpected keyword argument 'seed'Created on 2022-02-07 by the reprex package (v2.0.1) using Initially, I thought that this meant we were successfully disabling eager evaluation, with the code: tf$compat$v1$disable_eager_execution()I tested this again by removing that part of the startup/check process when greta initialises python and checks if TF is installed etc. Unfortunately, I got exactly the same error message. So I'm not sure how to approach things from here, and I'm not sure if tf$compat$v1$disable_eager_execution()Is actually disabling eager execution...or it's doing something spooky Or if we need to work out a new way to pass arguments through to tfp. |
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So what I notice is that on M1 mac, I get the same error, regardless of turning on eager mode or not. Here I control with an environment variable whether eager mode is turned on, Below is it being turned off # install with:
# remotes::install_github("greta-dev/greta#482)
Sys.setenv("GRETA_ENABLE_EAGER"=FALSE)
library(greta)
#>
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#>
#> binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#>
#> %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,
#> eigen, forwardsolve, gamma, identity, rowMeans, rowSums, sweep,
#> tapply
# check various python deps are installed correctly
greta_sitrep()
#> ℹ checking if python available
#> ✓ python (version 3.8) available
#>
#> ℹ checking if TensorFlow available
#> ✓ TensorFlow (version 2.6.0) available
#>
#> ℹ checking if TensorFlow Probability available
#> ✓ TensorFlow Probability (version 0.14.1) available
#>
#> ℹ checking if greta conda environment available
#> ✓ greta conda environment available
#>
#> Warning: greta does not currently work with Apple Silicon (M1)
#> We are working on getting this resolved ASAP, see
#> <https://github.com/greta-dev/greta/issues/458> for current progress.
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✓ Initialising python and checking dependencies ... done!
#>
#> eager flag value is FALSE
#> ℹ Disabling TF eager execution with: `tf$compat$v1$disable_eager_execution()`
#> ℹ Show current state of eager execution with:
#> `tf$compat$v1$executing_eagerly()`
#> Loaded Tensorflow version 2.6.0
# you may need to do
# install_greta_deps()
tensorflow::tf$compat$v1$executing_eagerly()
#> [1] FALSE
m <- model(normal(0,1))
#> Warning: greta does not currently work with Apple Silicon (M1)
#> We are working on getting this resolved ASAP, see
#> <https://github.com/greta-dev/greta/issues/458> for current progress.
#> Warning: greta does not currently work with Apple Silicon (M1)
#> We are working on getting this resolved ASAP, see
#> <https://github.com/greta-dev/greta/issues/458> for current progress.
mcmc(m)
#> Error in py_call_impl(callable, dots$args, dots$keywords): RuntimeError: Evaluation error: ValueError: Tensor("mcmc_sample_chain/trace_scan/while/mcmc_sample_chain_trace_scan_while_chain_of_mcmc_sample_chain_trace_scan_while_reshape_of_mcmc_sample_chain_trace_scan_while_identity/forward_log_det_jacobian/mcmc_sample_chain_trace_scan_while_reshape/forward/Const:0", shape=(2,), dtype=int32) must be from the same graph as Tensor("leapfrog_integrate_one_step/maybe_call_fn_and_grads/value_and_gradients/value_and_gradient/Shape:0", shape=(2,), dtype=int32) (graphs are <tensorflow.python.framework.ops.Graph object at 0x14e9feee0> and FuncGraph(name=mh_one_step_hmc_kernel_one_step_leapfrog_integrate_while_body_440, id=5902822128)).
#> .Created on 2022-03-17 by the reprex package (v2.0.1) Session infosessioninfo::session_info()
#> ─ Session info ↘️ 👌 🌧️ ───────────────────────────────────────────────────
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#>
#> ─ Python configuration ───────────────────────────────────────────────────────
#> python: /Users/nick/Library/r-miniconda-arm64/envs/greta-env/bin/python
#> libpython: /Users/nick/Library/r-miniconda-arm64/envs/greta-env/lib/libpython3.8.dylib
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#> numpy_version: 1.19.5
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#>
#> NOTE: Python version was forced by use_python function
#>
#> ──────────────────────────────────────────────────────────────────────────────And here is it being turned on: # Now do not turn it on
Sys.setenv("GRETA_ENABLE_EAGER"=TRUE)
library(greta)
#>
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#>
#> binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#>
#> %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,
#> eigen, forwardsolve, gamma, identity, rowMeans, rowSums, sweep,
#> tapply
# check various python deps are installed correctly
greta_sitrep()
#> ℹ checking if python available
#> ✓ python (version 3.8) available
#>
#> ℹ checking if TensorFlow available
#> ✓ TensorFlow (version 2.6.0) available
#>
#> ℹ checking if TensorFlow Probability available
#> ✓ TensorFlow Probability (version 0.14.1) available
#>
#> ℹ checking if greta conda environment available
#> ✓ greta conda environment available
#>
#> Warning: greta does not currently work with Apple Silicon (M1)
#> We are working on getting this resolved ASAP, see
#> <https://github.com/greta-dev/greta/issues/458> for current progress.
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✓ Initialising python and checking dependencies ... done!
#>
#> eager flag value is TRUE
#> ℹ Disabling TF eager execution with: `tf$compat$v1$disable_eager_execution()`
#> Loaded Tensorflow version 2.6.0
tensorflow::tf$compat$v1$executing_eagerly()
#> [1] TRUE
m <- model(normal(0,1))
#> Warning: greta does not currently work with Apple Silicon (M1)
#> We are working on getting this resolved ASAP, see
#> <https://github.com/greta-dev/greta/issues/458> for current progress.
#> Warning: greta does not currently work with Apple Silicon (M1)
#> We are working on getting this resolved ASAP, see
#> <https://github.com/greta-dev/greta/issues/458> for current progress.
mcmc(m)
#> Error in py_call_impl(callable, dots$args, dots$keywords): RuntimeError: Evaluation error: ValueError: Tensor("mcmc_sample_chain/trace_scan/while/mcmc_sample_chain_trace_scan_while_chain_of_mcmc_sample_chain_trace_scan_while_reshape_of_mcmc_sample_chain_trace_scan_while_identity/forward_log_det_jacobian/mcmc_sample_chain_trace_scan_while_reshape/forward/Const:0", shape=(2,), dtype=int32) must be from the same graph as Tensor("leapfrog_integrate_one_step/maybe_call_fn_and_grads/value_and_gradients/value_and_gradient/Shape:0", shape=(2,), dtype=int32) (graphs are <tensorflow.python.framework.ops.Graph object at 0x117977ee0> and FuncGraph(name=mh_one_step_hmc_kernel_one_step_leapfrog_integrate_while_body_439, id=5875312768)).
#> .Created on 2022-03-17 by the reprex package (v2.0.1) Session infosessioninfo::session_info()
#> ─ Session info ↘️ 👌 🌧️ ───────────────────────────────────────────────────
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#> Rcpp 1.0.8 2022-01-13 [1] CRAN (R 4.1.1)
#> reprex 2.0.1 2021-08-05 [1] CRAN (R 4.1.1)
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#> rlang 1.0.2 2022-03-04 [1] CRAN (R 4.1.1)
#> rmarkdown 2.11 2021-09-14 [1] CRAN (R 4.1.1)
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#> rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.1.0)
#> sessioninfo 1.2.1 2021-11-02 [1] CRAN (R 4.1.1)
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#> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.1.1)
#> styler 1.6.2 2021-09-23 [1] CRAN (R 4.1.1)
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#> withr 2.5.0 2022-03-03 [1] CRAN (R 4.1.1)
#> xfun 0.30 2022-03-02 [1] CRAN (R 4.1.1)
#> yaml 2.3.5 2022-02-21 [1] CRAN (R 4.1.1)
#>
#> [1] /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library
#>
#> ─ Python configuration ───────────────────────────────────────────────────────
#> python: /Users/nick/Library/r-miniconda-arm64/envs/greta-env/bin/python
#> libpython: /Users/nick/Library/r-miniconda-arm64/envs/greta-env/lib/libpython3.8.dylib
#> pythonhome: /Users/nick/Library/r-miniconda-arm64/envs/greta-env:/Users/nick/Library/r-miniconda-arm64/envs/greta-env
#> version: 3.8.12 | packaged by conda-forge | (default, Jan 30 2022, 23:13:24) [Clang 11.1.0 ]
#> numpy: /Users/nick/Library/r-miniconda-arm64/envs/greta-env/lib/python3.8/site-packages/numpy
#> numpy_version: 1.19.5
#> tensorflow: /Users/nick/Library/r-miniconda-arm64/envs/greta-env/lib/python3.8/site-packages/tensorflow
#>
#> NOTE: Python version was forced by use_python function
#>
#> ──────────────────────────────────────────────────────────────────────────────So there is probably some other update here in the API that I need to consider |
|
For relevance, here's what the And after that commit (here: a2d625b) here's what these different parameter values are
|
|
So my understanding is that we either need to do something to the |
|
OK so after a bit of poking around and fixing parts of library(tictoc)
devtools::load_all(".")
#> ℹ Loading greta
#> ℹ Initialising python and checking dependencies, this may take a moment.
#>
#> ✔ Initialising python and checking dependencies ... done!
#> Loaded Tensorflow version 2.9.2
x <- normal(0, 1)
m <- model(x)
tic()
draws <- mcmc(m, n_samples = 500, warmup = 500)
#> running 4 chains simultaneously on up to 8 cores
#> warmup 0/500 | eta: ?s warmup ==== 50/500 | eta: 13s warmup ======== 100/500 | eta: 9s warmup ============ 150/500 | eta: 7s warmup ================ 200/500 | eta: 6s warmup ==================== 250/500 | eta: 5s warmup ======================== 300/500 | eta: 4s warmup ============================ 350/500 | eta: 3s warmup ================================ 400/500 | eta: 2s warmup ==================================== 450/500 | eta: 1s warmup ======================================== 500/500 | eta: 0s
#> sampling 0/500 | eta: ?s sampling ==== 50/500 | eta: 7s sampling ======== 100/500 | eta: 6s sampling ============ 150/500 | eta: 6s sampling ================ 200/500 | eta: 5s sampling ==================== 250/500 | eta: 4s sampling ======================== 300/500 | eta: 3s sampling ============================ 350/500 | eta: 2s sampling ================================ 400/500 | eta: 2s sampling ==================================== 450/500 | eta: 1s sampling ======================================== 500/500 | eta: 0s
toc()
#> 17.709 sec elapsed
library(coda)
#>
#> Attaching package: 'coda'
#>
#> The following object is masked from 'package:greta':
#>
#> mcmc
plot(draws)Created on 2022-07-26 by the reprex package (v2.0.1) Session infosessioninfo::session_info()
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#>
#> [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library
#>
#> P ── Loaded and on-disk path mismatch.
#>
#> ─ Python configuration ───────────────────────────────────────────────────────
#> python: /Users/nick/Library/r-miniconda-arm64/envs/greta-env/bin/python
#> libpython: /Users/nick/Library/r-miniconda-arm64/envs/greta-env/lib/libpython3.8.dylib
#> pythonhome: /Users/nick/Library/r-miniconda-arm64/envs/greta-env:/Users/nick/Library/r-miniconda-arm64/envs/greta-env
#> version: 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:05:16) [Clang 12.0.1 ]
#> numpy: /Users/nick/Library/r-miniconda-arm64/envs/greta-env/lib/python3.8/site-packages/numpy
#> numpy_version: 1.22.4
#> tensorflow: /Users/nick/Library/r-miniconda-arm64/envs/greta-env/lib/python3.8/site-packages/tensorflow
#>
#> NOTE: Python version was forced by use_python function
#>
#> ──────────────────────────────────────────────────────────────────────────────Not super happy with how well it is sampling though... |
|
The next step is to replace the tuning algorithm that we use internally from within R, to instead use the TFP version: https://www.tensorflow.org/probability/api_docs/python/tfp/mcmc/DualAveragingStepSizeAdaptation |
|
OK so it turns out that we had hmc_l and hmc_epsilon indexed the wrong way 😅 So the upshot of this is that we now get proper sampling library(tictoc)
devtools::load_all(".")
#> ℹ Loading greta
#> ℹ Initialising python and checking dependencies, this may take a moment.
#>
#> ✔ Initialising python and checking dependencies ... done!
#> Loaded Tensorflow version 2.9.2
x <- normal(0, 1)
m <- model(x)
tic()
draws <- mcmc(m, n_samples = 500, warmup = 500)
#> running 4 chains simultaneously on up to 8 cores
#> warmup 0/500 | eta: ?s warmup ==== 50/500 | eta: 27s warmup ======== 100/500 | eta: 22s warmup ============ 150/500 | eta: 18s warmup ================ 200/500 | eta: 15s warmup ==================== 250/500 | eta: 12s warmup ======================== 300/500 | eta: 10s warmup ============================ 350/500 | eta: 7s warmup ================================ 400/500 | eta: 5s warmup ==================================== 450/500 | eta: 2s warmup ======================================== 500/500 | eta: 0s
#> sampling 0/500 | eta: ?s sampling ==== 50/500 | eta: 30s sampling ======== 100/500 | eta: 27s sampling ============ 150/500 | eta: 23s sampling ================ 200/500 | eta: 18s sampling ==================== 250/500 | eta: 14s sampling ======================== 300/500 | eta: 11s sampling ============================ 350/500 | eta: 8s sampling ================================ 400/500 | eta: 5s sampling ==================================== 450/500 | eta: 3s sampling ======================================== 500/500 | eta: 0s
toc()
#> 53.136 sec elapsed
library(coda)
#>
#> Attaching package: 'coda'
#>
#> The following object is masked from 'package:greta':
#>
#> mcmc
plot(draws)Created on 2022-07-26 by the reprex package (v2.0.1) Session infosessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
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#> ui X11
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#> collate en_AU.UTF-8
#> ctype en_AU.UTF-8
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#> vctrs 0.4.1 2022-04-13 [1] CRAN (R 4.2.0)
#> whisker 0.4 2019-08-28 [1] CRAN (R 4.2.0)
#> withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0)
#> xfun 0.31 2022-05-10 [1] CRAN (R 4.2.0)
#> xml2 1.3.3 2021-11-30 [1] CRAN (R 4.2.0)
#> yaml 2.3.5 2022-02-21 [1] CRAN (R 4.2.0)
#> yesno 0.1.2 2020-07-10 [1] CRAN (R 4.2.0)
#>
#> [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library
#>
#> P ── Loaded and on-disk path mismatch.
#>
#> ─ Python configuration ───────────────────────────────────────────────────────
#> python: /Users/nick/Library/r-miniconda-arm64/envs/greta-env/bin/python
#> libpython: /Users/nick/Library/r-miniconda-arm64/envs/greta-env/lib/libpython3.8.dylib
#> pythonhome: /Users/nick/Library/r-miniconda-arm64/envs/greta-env:/Users/nick/Library/r-miniconda-arm64/envs/greta-env
#> version: 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:05:16) [Clang 12.0.1 ]
#> numpy: /Users/nick/Library/r-miniconda-arm64/envs/greta-env/lib/python3.8/site-packages/numpy
#> numpy_version: 1.22.4
#> tensorflow: /Users/nick/Library/r-miniconda-arm64/envs/greta-env/lib/python3.8/site-packages/tensorflow
#>
#> NOTE: Python version was forced by use_python function
#>
#> ──────────────────────────────────────────────────────────────────────────────So overall we get correct sampling now, but it still takes about 50-60 seconds, for something that normally runs in about 3 seconds. We're going to try exploring some options for where/why this is a problem. Some of the ways we'll explore this:
# TF1 code to try out and profile
x <- normal(0, 1)
m <- model(x)
tic()
draws <- mcmc(m, n_samples = 500, warmup = 500)
glp_fun <- m$dag$generate_log_prob_function()
glp_fun(array(1, c(1, 1)))
# we can profile the code on this - TF2 code
build_array <- function(n){
array(n, c(n,1))
}
bm <- bench::mark(
x_0 = glp_fun(build_array(1)),
x_3 = glp_fun(build_array(1e3)),
x_6 = glp_fun(build_array(1e6)),
x_7 = glp_fun(build_array(1e7)),
check = FALSE
)
plot(bm)
library(greta)
library(tensorflow)
library(tfprobability)
tfp <- greta:::tfp
fun <- function(free_state) {
norm <- tfp$distributions$Normal(0, 1)
norm$log_prob(free_state)
}
free_state_value <- array(1, c(1, 1))
# TF 1
res <- fun(free_state_value)
sess <- tf$compat$v1$Session()
system.time(
sess$run(res)
)
# TF 2
system.time(
fun(free_state_value)
) |
|
OK so the mystery continues... Looks like currently running the same code on a 2016 Macbook Pro, using TF2 and on the tf-poke branch...takes 5 times less time to run MCMC than on a 2021 Macbook pro with an M1 chip. devtools::load_all(".")
#> ℹ Loading greta
#> ℹ Initialising python and checking dependencies, this may take a moment.
#>
#> ✔ Initialising python and checking dependencies ... done!
#> Loaded Tensorflow version 2.9.1
greta_sitrep()
#> ℹ checking if python available
#> ✔ python (v3.8) available
#>
#> ℹ checking if TensorFlow available
#> ✔ TensorFlow (v2.9.1) available
#>
#> ℹ checking if TensorFlow Probability available
#> ✔ TensorFlow Probability (v0.17.0) available
#>
#> ℹ checking if greta conda environment available
#> ✔ greta conda environment available
#>
#> ℹ greta is ready to use!
library(tictoc)
x <- normal(0,1)
m <- model(x)
tic()
draws <- mcmc(m, n_samples = 500, warmup = 500)
#> running 4 chains simultaneously on up to 8 cores
#> warmup 0/500 | eta: ?s warmup ==== 50/500 | eta: 23s warmup ======== 100/500 | eta: 12s warmup ============ 150/500 | eta: 9s warmup ================ 200/500 | eta: 6s warmup ==================== 250/500 | eta: 5s warmup ======================== 300/500 | eta: 4s warmup ============================ 350/500 | eta: 3s warmup ================================ 400/500 | eta: 2s warmup ==================================== 450/500 | eta: 1s warmup ======================================== 500/500 | eta: 0s
#> sampling 0/500 | eta: ?s sampling ==== 50/500 | eta: 2s sampling ======== 100/500 | eta: 1s sampling ============ 150/500 | eta: 1s sampling ================ 200/500 | eta: 1s sampling ==================== 250/500 | eta: 1s sampling ======================== 300/500 | eta: 1s sampling ============================ 350/500 | eta: 0s sampling ================================ 400/500 | eta: 0s sampling ==================================== 450/500 | eta: 0s sampling ======================================== 500/500 | eta: 0s
toc()
#> 9.699 sec elapsed
library(coda)
#>
#> Attaching package: 'coda'
#>
#> The following object is masked from 'package:greta':
#>
#> mcmc
plot(draws)Created on 2022-07-27 by the reprex package (v2.0.1) Session infosessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.2.0 (2022-04-22)
#> os macOS Big Sur/Monterey 10.16
#> system x86_64, darwin17.0
#> ui X11
#> language (EN)
#> collate en_AU.UTF-8
#> ctype en_AU.UTF-8
#> tz Australia/Perth
#> date 2022-07-27
#> pandoc 2.17.1.1 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/ (via rmarkdown)
#>
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#> ! package * version date (UTC) lib source
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#> backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0)
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#> globals 0.15.1 2022-06-24 [1] CRAN (R 4.2.0)
#> glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0)
#> P greta * 0.4.2.9000 2022-07-27 [?] load_all()
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#> rlang 1.0.4 2022-07-12 [1] CRAN (R 4.2.0)
#> rmarkdown 2.14 2022-04-25 [1] CRAN (R 4.2.0)
#> rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.2.0)
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#> stringi 1.7.8 2022-07-11 [1] CRAN (R 4.2.0)
#> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.2.0)
#> tensorflow 2.9.0 2022-05-21 [1] CRAN (R 4.2.0)
#> testthat * 3.1.4 2022-04-26 [1] CRAN (R 4.2.0)
#> tfautograph 0.3.2 2021-09-17 [1] CRAN (R 4.2.0)
#> tfruns 1.5.0 2021-02-26 [1] CRAN (R 4.2.0)
#> tictoc * 1.0.1 2021-04-19 [1] CRAN (R 4.2.0)
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#> yaml 2.3.5 2022-02-21 [1] CRAN (R 4.2.0)
#> yesno 0.1.2 2020-07-10 [1] CRAN (R 4.2.0)
#>
#> [1] /Library/Frameworks/R.framework/Versions/4.2/Resources/library
#>
#> P ── Loaded and on-disk path mismatch.
#>
#> ─ Python configuration ───────────────────────────────────────────────────────
#> python: /Users/njtierney/Library/r-miniconda/envs/greta-env/bin/python
#> libpython: /Users/njtierney/Library/r-miniconda/envs/greta-env/lib/libpython3.8.dylib
#> pythonhome: /Users/njtierney/Library/r-miniconda/envs/greta-env:/Users/njtierney/Library/r-miniconda/envs/greta-env
#> version: 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:05:47) [Clang 12.0.1 ]
#> numpy: /Users/njtierney/Library/r-miniconda/envs/greta-env/lib/python3.8/site-packages/numpy
#> numpy_version: 1.23.1
#> tensorflow: /Users/njtierney/Library/r-miniconda/envs/greta-env/lib/python3.8/site-packages/tensorflow
#>
#> NOTE: Python version was forced by use_python function
#>
#> ──────────────────────────────────────────────────────────────────────────────weird. Still about twice as slow as on TF1...but still...so strange. |
|
An on the same intel mac, running 500 samples for TF1 library(greta)
#>
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#>
#> binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#>
#> %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,
#> eigen, forwardsolve, gamma, identity, rowMeans, rowSums, sweep,
#> tapply
library(tictoc)
x <- normal(0,1)
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✔ Initialising python and checking dependencies ... done!
#>
m <- model(x)
tic()
draws <- mcmc(m, n_samples = 500, warmup = 500)
#> running 4 chains simultaneously on up to 8 cores
#>
#> warmup 0/500 | eta: ?s warmup ==== 50/500 | eta: 6s warmup ======== 100/500 | eta: 4s warmup ============ 150/500 | eta: 3s warmup ================ 200/500 | eta: 3s warmup ==================== 250/500 | eta: 2s warmup ======================== 300/500 | eta: 2s warmup ============================ 350/500 | eta: 1s warmup ================================ 400/500 | eta: 1s warmup ==================================== 450/500 | eta: 0s warmup ======================================== 500/500 | eta: 0s
#> sampling 0/500 | eta: ?s sampling ==== 50/500 | eta: 1s sampling ======== 100/500 | eta: 1s sampling ============ 150/500 | eta: 1s sampling ================ 200/500 | eta: 1s sampling ==================== 250/500 | eta: 1s sampling ======================== 300/500 | eta: 0s sampling ============================ 350/500 | eta: 0s sampling ================================ 400/500 | eta: 0s sampling ==================================== 450/500 | eta: 0s sampling ======================================== 500/500 | eta: 0s
toc()
#> 6.293 sec elapsed
library(coda)
#>
#> Attaching package: 'coda'
#>
#> The following object is masked from 'package:greta':
#>
#> mcmc
plot(draws)Created on 2022-08-05 by the reprex package (v2.0.1) Session infosessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.2.0 (2022-04-22)
#> os macOS Big Sur/Monterey 10.16
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#>
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#>
#> ─ Python configuration ───────────────────────────────────────────────────────
#> python: /Users/njtierney/Library/r-miniconda/envs/greta-env/bin/python
#> libpython: /Users/njtierney/Library/r-miniconda/envs/greta-env/lib/libpython3.7m.dylib
#> pythonhome: /Users/njtierney/Library/r-miniconda/envs/greta-env:/Users/njtierney/Library/r-miniconda/envs/greta-env
#> version: 3.7.12 | packaged by conda-forge | (default, Oct 26 2021, 05:59:23) [Clang 11.1.0 ]
#> numpy: /Users/njtierney/Library/r-miniconda/envs/greta-env/lib/python3.7/site-packages/numpy
#> numpy_version: 1.16.4
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#>
#> NOTE: Python version was forced by use_python function
#>
#> ────────────────────────────────────────────────────────────────────────────── |
|
So, after some exploration of the TF profiler on greta, we need to do the following
|
|
OK so there's something very interesting. Turning off the GPU makes the M1 mac...about 19 times faster? 53. seconds compared to 2.7 seconds library(greta)
#>
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#>
#> binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#>
#> %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,
#> eigen, forwardsolve, gamma, identity, rowMeans, rowSums, sweep,
#> tapply
library(tictoc)
greta_sitrep()
#> ℹ checking if python available
#> ✔ python (v3.8) available
#>
#> ℹ checking if TensorFlow available
#> ✔ TensorFlow (v2.9.2) available
#>
#> ℹ checking if TensorFlow Probability available
#> ✔ TensorFlow Probability (v0.17.0) available
#>
#> ℹ checking if greta conda environment available
#> ✔ greta conda environment available
#>
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✔ Initialising python and checking dependencies ... done!
#>
#> ℹ greta is ready to use!
tensorflow::tf$config$get_visible_devices("CPU") |>
tensorflow::tf$config$set_visible_devices()
#> Loaded Tensorflow version 2.9.2
x <- normal(0,1)
m <- model(x)
tic()
draws <- mcmc(m, n_samples = 500, warmup = 500)
#> running 4 chains simultaneously on up to 8 cores
#>
#> warmup 0/500 | eta: ?s warmup ==== 50/500 | eta: 6s warmup ======== 100/500 | eta: 3s warmup ============ 150/500 | eta: 2s warmup ================ 200/500 | eta: 2s warmup ==================== 250/500 | eta: 1s warmup ======================== 300/500 | eta: 1s warmup ============================ 350/500 | eta: 1s warmup ================================ 400/500 | eta: 0s warmup ==================================== 450/500 | eta: 0s warmup ======================================== 500/500 | eta: 0s
#> sampling 0/500 | eta: ?s sampling ==== 50/500 | eta: 0s sampling ======== 100/500 | eta: 0s sampling ============ 150/500 | eta: 0s sampling ================ 200/500 | eta: 0s sampling ==================== 250/500 | eta: 0s sampling ======================== 300/500 | eta: 0s sampling ============================ 350/500 | eta: 0s sampling ================================ 400/500 | eta: 0s sampling ==================================== 450/500 | eta: 0s sampling ======================================== 500/500 | eta: 0s
toc()
#> 2.732 sec elapsed
plot(draws)Created on 2022-08-08 by the reprex package (v2.0.1) Session infosessioninfo::session_info()
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#>
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#>
#> ─ Python configuration ───────────────────────────────────────────────────────
#> python: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/bin/python
#> libpython: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/libpython3.8.dylib
#> pythonhome: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2:/Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2
#> version: 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:05:16) [Clang 12.0.1 ]
#> numpy: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/python3.8/site-packages/numpy
#> numpy_version: 1.22.4
#> tensorflow: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/python3.8/site-packages/tensorflow
#>
#> NOTE: Python version was forced by use_python function
#>
#> ──────────────────────────────────────────────────────────────────────────────Thanks to rstudio/tensorflow#541 and to @t-kalinowski for that! |
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Aha! So the GPU was being turned on by default then? If so, the fix should be to turn off the GPU by default (but make it easy to run with the GPU if wanted)? The fact that the GPU is slower (for this model) is expected, as the overhead in passing things to the GPU is higher, and there's next to no benefit of parallelising a vector length 4 on a GPU. In a big model with large matrix multiplies, the GPU will likely be faster. Looking forward to finding out how much faster! |
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We should think about the best interface for turning the GPU on/off. Assuming we can use the same interface for all platforms, then just a If they set |
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OK that sounds good to me! I imagine some kind of tensorflow::tf$config$get_visible_devices("CPU") |>
tensorflow::tf$config$set_visible_devices()Changes state permanently I think. Another thought I had was, are there other operations, like |
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Yep, that makes sense.
It's there a good way of checking the available devices? |
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Just lurking here, but what I've seen in the python modules I've worked with is there's usually an option to set GPU use (and resources generally) as part of a configuration that can be set when initializing intensive operations. Just about every library respects the env vars for CUDA_VISIBLE_DEVICES, but it's usually nicer to configure resources with more granularity, since as you've noted it can be pretty variable which is fastest in which circumstances. (not sure if tensorflow libs here can make use of multiple GPUs, but typically interfaces let you set number of GPUs as well). For example:
|
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Thanks @cboettig! Yeah, that's a whole other level of interface detail we should think about. It does suggest that a different interface might be more future proof. E.g.:
Which could then be changed in the future to target specific operations, like:
what do you think to that? |
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Neat! I think you have a stronger intuition than I do here, and I'm not entirely sure when evaluation takes place in each case. I was thinking of mimicking the {darts} interface via an argument to either the Maybe less elegant and not sure how it generalized to other places where you might want a GPU, but passing the GPU setting as a function argument seems easier to me. I'm always a bit nervous about the 'side-effect' mechanism, it's harder for me as a user to reason about, since there's so many places an R package can 'stash' these additional states about execution (especially if a user is attempting to wrap greta functions in some derivative package)? |
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Yeah, good point about the side-effects. greta already has a few spooky things that can happen because of the unorthodox interface to constructing a model. The fact that we can create the greta arrays before having to worry about the TF implementation of them means we have a lot of flexibility in the interface. And the fact that the user can refer to specific operation greta arrays when specifying devices should make it fairly intuitive. So a simpler approach that also enables using specific devices for specific operations might be something like:
Though we'd want to think carefully about how to make it so beginner users (or people with small models who don't need GPU acceleration) don't have to confront this stuff. So we could just do a default of:
Would that feel more natural @cboettig? |
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We could also change the interface so it's more like: which is maybe more like how people will want to configure these things? We should do an explore of the TF functionality and idioms a bit before deciding on that level of detail, but worth planning the general structure first, so we can at least roll-out a CPU-only (or GPU-only) version of the TF2 greta soon without getting too bogged down in this extra functionality. |
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Nice, yeah I like that. Another simple advantage of this construction is it is a bit easier to document and for users to discover by browsing the docs for the function, though maybe that's just a crutch for projects that lack greta's attention to good user documentation. I do think having something like Definitely agree about sensible defaults. Defaulting all evaluation to CPU-only is I think a pretty common strategy. (just like most cpu parallelization -- simple cases are better with such defaults). I'm not sure on the precise syntax. My intuition is that |
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I've moved some of the discussion of these features into #545 - just to try and help isolate some of the tasks and discussion around this. 😄 |
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Thanks Nick! Just to wrap this discussion up, I wasn't quite sure what you meant by this @cboettig: "My intuition is that compute_setup() object wouldn't reference specific ops, but rather, the user attaches the desired config to the operation, but I realize that only works for syntax that involves a function call and not the generic matrix math example." Do you mean that you were expecting an interface a bit like this?: That's a more TF-like interface, so will definitely be ore familiar to people coming from that background. It's a bit different to the current greta design of defining the mathematical model in greta arrays, and then only getting to specific aspects of the computation (like floating point precision, inference algorithm) later. I quite like that distinction, because it focuses the user on the model rather than the implementation, and because beginners (or just users without very heavy models) don't need to worry about what the additional argument does. But on the other hand, many users will be more used to specifying all the details when defining the operation, so it might be simpler. Definitely an option to consider for a more detailed interface later, since this is I think completely compatible with the planned first steps. |
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Right, I generally like the feel of Like you say, this could be turned into a function call but that feels awkward. Is the above matrix operation a "promise" or an execution anyway? e.g. I could almost imagine the above |
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That makes sense, thanks! It's a promise, not an execution. The greta array objects are all constructed and linked together (to create an abstract syntax tree made up of R6 objects) when writing the greta code, but they don't do any computation. Then when So when telling mcmc to run on GPU, it's actually just being lazy and saying that all the greta ops should run on GPU when evaluating the log_prob. But because it's a promise, we could also link each operation to a different compute setting at the point of running the MCMC, (like in this interface), so that in evaluating the log_prob, there is a mix of GPU and CPU ops, rather than all one or the other. That will probably be optimal in many cases. This also means the user can develop the model, and then trial different compute options, like in Nick's example here |
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For those following along, TF2 greta is approaching an alpha release, with just a couple of small issues to be ironed out, which are related to how we are representing cholesky, and trying to suppress some python warnings regarding retracing. If you would like to install this version of greta, you can do the following: remotes::install_github("greta-dev/greta#534")As we get closer to a release, I'm cleaning up issues, and I think this issue has served its purpose of helping implement most of the TF2 changes - the remaining issues can be seen in milestone 0.5: https://github.com/greta-dev/greta/milestone/2, and other TF2 related issues are marked with the TF2 label: https://github.com/greta-dev/greta/issues?q=is%3Aopen+is%3Aissue+label%3ATF2 Thank you for the discussion, everyone! |
This issue is about planning ahead to dealing with major TensorFlow API changes when a greta version has to work with TensorFlow 2.0. It doesn't affect anything yet.
optimisers
Tensorflow's various optimiser interfaces are being unified (details here). Some of the old versions will still be available in the
compatcompatibility module (though there may be speed benefit to changing to more recent versions).In addition, the TF 1.x
contribmodule will no longer be available in TF 2.0, and won't be incompat.There is a tensorflow addons repo with an 'optimizers' module (though with no apparent overlap with existing optimisers) and a TFP 'optimizer' module which has a couple of the methods.
nelder_meadtf$contrib$opt$ScipyOptimizerInterfacetfp$optimizer$nelder_mead_minimizepowelltf$contrib$opt$ScipyOptimizerInterfacecgtf$contrib$opt$ScipyOptimizerInterfacebfgstf$contrib$opt$ScipyOptimizerInterfacetfp$optimizer$bfgs_minimizenewton_cgtf$contrib$opt$ScipyOptimizerInterfacel_bfgs_btf$contrib$opt$ScipyOptimizerInterfacetnctf$contrib$opt$ScipyOptimizerInterfacecobylatf$contrib$opt$ScipyOptimizerInterfaceslsqptf$contrib$opt$ScipyOptimizerInterfacegradient_descenttf$compat$v1$train$GradientDescentOptimizertf$keras$optimizers$SGDadadeltatf$compat$v1$train$AdadeltaOptimizertf$keras$optimizers$Adadeltaadagradtf$compat$v1$train$AdagradOptimizertf$keras$optimizers$Adagradadagrad_datf$compat$v1$train$AdagradDAOptimizertf$compat$v1$train$AdagradDAOptimizermomentumtf$compat$v1$train$MomentumOptimizertf$keras$optimizers$SGDadamtf$compat$v1$train$AdamOptimizertf$keras$optimizers$Adamftrltf$compat$v1$train$FtrlOptimizertf$keras$optimizers$FTRLproximal_gradient_descenttf$compat$v1$train$ProximalGradientDescentOptimizertf$compat$v1$train$ProximalGradientDescentOptimizerproximal_adagradtf$compat$v1$train$ProximalAdagradOptimizertf$compat$v1$train$ProximalAdagradOptimizerrms_proptf$compat$v1$train$RMSPropOptimizertf$keras$optimizers$RMSProp(though arguments may have changed, sotf$compat$v1$train$RMSPropOptimizeras a backup)Unless another interface to the Scipy optimisers is developed, the optimisers without replacements will have to be removed from the API. It'll bee a little hard to deprecate them without knowing whether a replacement will be coming.
ode solvers
The
greta.dynamicspackage'sode_solve()functionality also depends oncontrib, using thetf$contrib$integrate$odeintandodeint_fixedmethods.tfp$math$ode$Solver$solvemay be a viable replacement.The text was updated successfully, but these errors were encountered: