M1 TF2 dev error: test_distributions can not find reasonable starting values after 20 attempts for certain distributions

njtierney commented 1 year ago

NOTE: This is in the Tensorflow 2 development branch (https://github.com/greta-dev/greta/pull/534)

4 errors for the distributions, multinomial, dirichlet, and categorical:

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
source("tests/testthat/helpers.R")

## error 1
# multivariate discrete
y <- extraDistr::rmnom(5, size = 4, prob = runif(3))
p <- uniform(0, 1, dim = 3)
distribution(y) <- multinomial(4, t(p), n_realisations = 5)
sample_distribution(p)
#> Error: Could not find reasonable starting values after 20 attempts.
#> Please specify initial values manually via the `initial_values` argument

## error 2
alpha <- uniform(0, 10, dim = c(1, 5))
x <- dirichlet(alpha)
m <- model(x)
draws <- mcmc(m, n_samples = 100, warmup = 100, verbose = FALSE)
#> Error: Could not find reasonable starting values after 20 attempts.
#> Please specify initial values manually via the `initial_values` argument

## error 3
n <- 10
k <- 3

# multinomial
size <- 5
x <- t(rmultinom(n, size, runif(k)))
p <- uniform(0, 1, dim = c(n, k))
distribution(x) <- multinomial(size, p)
m <- model(p)
expect_ok(draws <- mcmc(m, warmup = 0, n_samples = 5, verbose = FALSE))
#> Error: `expr` threw an unexpected error.
#> Message: Could not find reasonable starting values after 20 attempts.
#> Please specify initial values manually via the `initial_values` argument
#> Class:   simpleError/error/condition

## error 4
n <- 10
k <- 3

# categorical
x <- t(rmultinom(n, 1, runif(k)))
p <- uniform(0, 1, dim = c(n, k))
distribution(x) <- categorical(p)
m <- model(p)
expect_ok(draws <- mcmc(m, warmup = 0, n_samples = 5, verbose = FALSE))
#> Error: `expr` threw an unexpected error.
#> Message: Could not find reasonable starting values after 20 attempts.
#> Please specify initial values manually via the `initial_values` argument
#> Class:   simpleError/error/condition

^{Created on 2022-09-30 by the reprex package (v2.0.1)}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.2.0 (2022-04-22) #> os macOS Monterey 12.3.1 #> system aarch64, darwin20 #> ui X11 #> language (EN) #> collate en_AU.UTF-8 #> ctype en_AU.UTF-8 #> tz Australia/Brisbane #> date 2022-09-30 #> pandoc 2.19.2 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> ! package * version date (UTC) lib source #> abind 1.4-5 2016-07-21 [1] CRAN (R 4.2.0) #> backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0) #> base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.2.0) #> brio 1.1.3 2021-11-30 [1] CRAN (R 4.2.0) #> cachem 1.0.6 2021-08-19 [1] CRAN (R 4.2.0) #> callr 3.7.2 2022-08-22 [1] CRAN (R 4.2.0) #> cli 3.3.0.9000 2022-06-15 [1] Github (r-lib/cli@31a5db5) #> coda 0.19-4 2020-09-30 [1] CRAN (R 4.2.0) #> codetools 0.2-18 2020-11-04 [1] CRAN (R 4.2.0) #> crayon 1.5.1 2022-03-26 [1] CRAN (R 4.2.0) #> desc 1.4.2 2022-09-08 [1] CRAN (R 4.2.0) #> devtools 2.4.4 2022-07-20 [1] CRAN (R 4.2.0) #> digest 0.6.29 2021-12-01 [1] CRAN (R 4.2.0) #> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0) #> evaluate 0.16 2022-08-09 [1] CRAN (R 4.2.0) #> extraDistr 1.9.1 2020-09-07 [1] CRAN (R 4.2.0) #> fansi 1.0.3 2022-03-24 [1] CRAN (R 4.2.0) #> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.0) #> fs 1.5.2 2021-12-08 [1] CRAN (R 4.2.0) #> future 1.27.0 2022-07-22 [1] CRAN (R 4.2.0) #> globals 0.16.0 2022-08-05 [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 2022-09-19 [?] load_all() #> here 1.0.1 2020-12-13 [1] CRAN (R 4.2.0) #> highr 0.9 2021-04-16 [1] CRAN (R 4.2.0) #> hms 1.1.1 2021-09-26 [1] CRAN (R 4.2.0) #> htmltools 0.5.3 2022-07-18 [1] CRAN (R 4.2.0) #> htmlwidgets 1.5.4 2021-09-08 [1] CRAN (R 4.2.0) #> httpuv 1.6.5 2022-01-05 [1] CRAN (R 4.2.0) #> jsonlite 1.8.0 2022-02-22 [1] CRAN (R 4.2.0) #> knitr 1.40 2022-08-24 [1] CRAN (R 4.2.0) #> later 1.3.0 2021-08-18 [1] CRAN (R 4.2.0) #> lattice 0.20-45 2021-09-22 [1] CRAN (R 4.2.0) #> lifecycle 1.0.2 2022-09-09 [1] CRAN (R 4.2.0) #> listenv 0.8.0 2019-12-05 [1] CRAN (R 4.2.0) #> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0) #> Matrix 1.4-1 2022-03-23 [1] CRAN (R 4.2.0) #> memoise 2.0.1 2021-11-26 [1] CRAN (R 4.2.0) #> mime 0.12 2021-09-28 [1] CRAN (R 4.2.0) #> miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.2.0) #> parallelly 1.32.1 2022-07-21 [1] CRAN (R 4.2.0) #> pillar 1.8.1 2022-08-19 [1] CRAN (R 4.2.0) #> pkgbuild 1.3.1 2021-12-20 [1] CRAN (R 4.2.0) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0) #> pkgload 1.3.0 2022-06-27 [1] CRAN (R 4.2.0) #> png 0.1-7 2013-12-03 [1] CRAN (R 4.2.0) #> prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.2.0) #> processx 3.7.0 2022-07-07 [1] CRAN (R 4.2.0) #> profvis 0.3.7 2020-11-02 [1] CRAN (R 4.2.0) #> progress 1.2.2 2019-05-16 [1] CRAN (R 4.2.0) #> promises 1.2.0.1 2021-02-11 [1] CRAN (R 4.2.0) #> ps 1.7.1 2022-06-18 [1] CRAN (R 4.2.0) #> purrr 0.3.4 2020-04-17 [1] CRAN (R 4.2.0) #> R.cache 0.16.0 2022-07-21 [1] CRAN (R 4.2.0) #> R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.2.0) #> R.oo 1.25.0 2022-06-12 [1] CRAN (R 4.2.0) #> R.utils 2.12.0 2022-06-28 [1] CRAN (R 4.2.0) #> R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0) #> Rcpp 1.0.9 2022-07-08 [1] CRAN (R 4.2.0) #> remotes 2.4.2 2021-11-30 [1] CRAN (R 4.2.0) #> reprex 2.0.1 2021-08-05 [1] CRAN (R 4.2.0) #> reticulate 1.25 2022-05-11 [1] CRAN (R 4.2.0) #> rlang 1.0.5 2022-08-31 [1] CRAN (R 4.2.0) #> rmarkdown 2.16 2022-08-24 [1] CRAN (R 4.2.0) #> rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.2.0) #> rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.2.0) #> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0) #> shiny 1.7.2 2022-07-19 [1] CRAN (R 4.2.0) #> stringi 1.7.8 2022-07-11 [1] CRAN (R 4.2.0) #> stringr 1.4.1 2022-08-20 [1] CRAN (R 4.2.0) #> styler 1.7.0 2022-03-13 [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) #> tibble 3.1.8 2022-07-22 [1] CRAN (R 4.2.0) #> urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.0) #> usethis 2.1.6 2022-05-25 [1] CRAN (R 4.2.0) #> utf8 1.2.2 2021-07-24 [1] CRAN (R 4.2.0) #> 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 0.33 2022-09-12 [1] CRAN (R 4.2.0) #> xtable 1.8-4 2019-04-21 [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-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 #> #> ────────────────────────────────────────────────────────────────────────────── ```

Most likely some issue with how the distributions are specified

This will be resolved in the large PR, #534

njtierney commented 1 year ago

So far, tracing this back to

valid_parameters = function(parameters) {
      browser()
      dag <- self$model$dag
      # tfe <- dag$tf_environment
      #
      # if (!live_pointer("joint_density_adj", envir = tfe)) {
      #   # dag$on_graph(
      #     # dag$define_joint_density()
      #     # )
      # }
      #
      # dag$send_parameters(parameters)
      # ld <- self$model$dag$log_density()
      tf_parameters <- fl(array(
        data = parameters,
        dim = c(1, length(parameters))
        ))
      ld <- lapply(
        dag$tf_log_prob_function(tf_parameters),
        as.numeric
        )
      is.finite(ld$adjusted) && is.finite(ld$unadjusted)
    },

in inference_class.R

where tf_parameters is

tf.Tensor([[ 0.04595352 -0.12635538 -0.02149962]], shape=(1, 3), dtype=float64)

And then running tf_log_prob_function(tf_parameters) returns

tf.Tensor(
[[[-inf]
  [-inf]
  [-inf]]], shape=(1, 3, 1), dtype=float64)

njtierney commented 1 year ago

Similarly, in test_joint.R we get these errors:

## related to the above - test_joint.R ========================================
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
source("tests/testthat/helpers.R")

## error 1 - Error: Could not find reasonable starting values after 20 attempts.
obs <- matrix(rbinom(300, 1, 0.5), 100, 3)
probs <- variable(0, 1, dim = 3)
distribution(obs) <- joint(
  bernoulli(probs[1]),
  bernoulli(probs[2]),
  bernoulli(probs[3]),
  dim = 100
)

sample_distribution(probs)
#> Error: Could not find reasonable starting values after 20 attempts.
#> Please specify initial values manually via the `initial_values` argument

## error 2 - Error: all(above_lower & below_upper) is not TRUE
x <- joint(
  normal(0, 1, truncation = c(0, Inf)),
  normal(0, 2, truncation = c(-Inf, 0)),
  normal(-1, 1, truncation = c(1, 2))
)

sample_distribution(x, lower = c(0, -Inf, 1), upper = c(Inf, 0, 2))
#> Error: all(above_lower & below_upper) is not TRUE
#> 
#> `actual`:   FALSE
#> `expected`: TRUE

## error 3 - Error: Could not find reasonable starting values after 20 attempts.
x <- joint(
  uniform(0, 1),
  uniform(0, 2),
  uniform(-1, 0)
)

sample_distribution(x, lower = c(0, 0, -1), upper = c(1, 2, 0))
#> Error: Could not find reasonable starting values after 20 attempts.
#> Please specify initial values manually via the `initial_values` argument

# intriguingly, this also fails:

sample_distribution(uniform(0,1))
#> Error: Could not find reasonable starting values after 20 attempts.
#> Please specify initial values manually via the `initial_values` argument

^{Created on 2022-10-04 by the reprex package (v2.0.1)}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.2.0 (2022-04-22) #> os macOS Monterey 12.3.1 #> system aarch64, darwin20 #> ui X11 #> language (EN) #> collate en_AU.UTF-8 #> ctype en_AU.UTF-8 #> tz Australia/Brisbane #> date 2022-10-04 #> pandoc 2.19.2 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> ! package * version date (UTC) lib source #> abind 1.4-5 2016-07-21 [1] CRAN (R 4.2.0) #> backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0) #> base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.2.0) #> brio 1.1.3 2021-11-30 [1] CRAN (R 4.2.0) #> cachem 1.0.6 2021-08-19 [1] CRAN (R 4.2.0) #> callr 3.7.2 2022-08-22 [1] CRAN (R 4.2.0) #> cli 3.3.0.9000 2022-06-15 [1] Github (r-lib/cli@31a5db5) #> coda 0.19-4 2020-09-30 [1] CRAN (R 4.2.0) #> codetools 0.2-18 2020-11-04 [1] CRAN (R 4.2.0) #> crayon 1.5.1 2022-03-26 [1] CRAN (R 4.2.0) #> desc 1.4.2 2022-09-08 [1] CRAN (R 4.2.0) #> devtools 2.4.4 2022-07-20 [1] CRAN (R 4.2.0) #> diffobj 0.3.5 2021-10-05 [1] CRAN (R 4.2.0) #> digest 0.6.29 2021-12-01 [1] CRAN (R 4.2.0) #> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0) #> evaluate 0.16 2022-08-09 [1] CRAN (R 4.2.0) #> fansi 1.0.3 2022-03-24 [1] CRAN (R 4.2.0) #> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.0) #> fs 1.5.2 2021-12-08 [1] CRAN (R 4.2.0) #> future 1.27.0 2022-07-22 [1] CRAN (R 4.2.0) #> globals 0.16.0 2022-08-05 [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 2022-09-19 [?] load_all() #> here 1.0.1 2020-12-13 [1] CRAN (R 4.2.0) #> highr 0.9 2021-04-16 [1] CRAN (R 4.2.0) #> hms 1.1.1 2021-09-26 [1] CRAN (R 4.2.0) #> htmltools 0.5.3 2022-07-18 [1] CRAN (R 4.2.0) #> htmlwidgets 1.5.4 2021-09-08 [1] CRAN (R 4.2.0) #> httpuv 1.6.5 2022-01-05 [1] CRAN (R 4.2.0) #> jsonlite 1.8.0 2022-02-22 [1] CRAN (R 4.2.0) #> knitr 1.40 2022-08-24 [1] CRAN (R 4.2.0) #> later 1.3.0 2021-08-18 [1] CRAN (R 4.2.0) #> lattice 0.20-45 2021-09-22 [1] CRAN (R 4.2.0) #> lifecycle 1.0.2 2022-09-09 [1] CRAN (R 4.2.0) #> listenv 0.8.0 2019-12-05 [1] CRAN (R 4.2.0) #> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0) #> Matrix 1.4-1 2022-03-23 [1] CRAN (R 4.2.0) #> memoise 2.0.1 2021-11-26 [1] CRAN (R 4.2.0) #> mime 0.12 2021-09-28 [1] CRAN (R 4.2.0) #> miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.2.0) #> parallelly 1.32.1 2022-07-21 [1] CRAN (R 4.2.0) #> pillar 1.8.1 2022-08-19 [1] CRAN (R 4.2.0) #> pkgbuild 1.3.1 2021-12-20 [1] CRAN (R 4.2.0) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0) #> pkgload 1.3.0 2022-06-27 [1] CRAN (R 4.2.0) #> png 0.1-7 2013-12-03 [1] CRAN (R 4.2.0) #> prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.2.0) #> processx 3.7.0 2022-07-07 [1] CRAN (R 4.2.0) #> profvis 0.3.7 2020-11-02 [1] CRAN (R 4.2.0) #> progress 1.2.2 2019-05-16 [1] CRAN (R 4.2.0) #> promises 1.2.0.1 2021-02-11 [1] CRAN (R 4.2.0) #> ps 1.7.1 2022-06-18 [1] CRAN (R 4.2.0) #> purrr 0.3.4 2020-04-17 [1] CRAN (R 4.2.0) #> R.cache 0.16.0 2022-07-21 [1] CRAN (R 4.2.0) #> R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.2.0) #> R.oo 1.25.0 2022-06-12 [1] CRAN (R 4.2.0) #> R.utils 2.12.0 2022-06-28 [1] CRAN (R 4.2.0) #> R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0) #> Rcpp 1.0.9 2022-07-08 [1] CRAN (R 4.2.0) #> rematch2 2.1.2 2020-05-01 [1] CRAN (R 4.2.0) #> remotes 2.4.2 2021-11-30 [1] CRAN (R 4.2.0) #> reprex 2.0.1 2021-08-05 [1] CRAN (R 4.2.0) #> reticulate 1.25 2022-05-11 [1] CRAN (R 4.2.0) #> rlang 1.0.5 2022-08-31 [1] CRAN (R 4.2.0) #> rmarkdown 2.16 2022-08-24 [1] CRAN (R 4.2.0) #> rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.2.0) #> rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.2.0) #> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0) #> shiny 1.7.2 2022-07-19 [1] CRAN (R 4.2.0) #> stringi 1.7.8 2022-07-11 [1] CRAN (R 4.2.0) #> stringr 1.4.1 2022-08-20 [1] CRAN (R 4.2.0) #> styler 1.7.0 2022-03-13 [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) #> tibble 3.1.8 2022-07-22 [1] CRAN (R 4.2.0) #> urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.0) #> usethis 2.1.6 2022-05-25 [1] CRAN (R 4.2.0) #> utf8 1.2.2 2021-07-24 [1] CRAN (R 4.2.0) #> vctrs 0.4.1 2022-04-13 [1] CRAN (R 4.2.0) #> waldo 0.4.0 2022-03-16 [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 0.33 2022-09-12 [1] CRAN (R 4.2.0) #> xtable 1.8-4 2019-04-21 [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-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 #> #> ────────────────────────────────────────────────────────────────────────────── ```

njtierney commented 1 year ago

There's an interesting clue here, in test_joint.R, I was noticing a pattern and here's a small example of some distribution code that fails.

sample_distribution(uniform(0,1))

which seems crazy, given that uniform should be easy to sample? can we run mcmc on it?

uniform_samples <- mcmc(model(uniform(0,1)))

This errors - what?

But we can sample a normal, like below, which is good

normal_samples <- mcmc(model(normal(0,1)))

So this is happening at a pretty deep place I guess. Looking at the trace:

# 1: mcmc(model(uniform(0, 1)))
# 2: inference.R#201: with(tf$device(compute_options), {
# trace_batch_size
# 3: with.python.builtin.object(tf$device(compute_options), {
#   trace_batch
#   4: tryCatch(force(expr), finally = {
#     data$`__exit__`(NULL, NULL, NULL)
#   }
#   5: tryCatchList(expr, classes, parentenv, handlers)
#   6: force(expr)
#   7: inference.R#258: lapply(initial_values_split, build_sampler, sampler, m
#   8: FUN(X[[i]], ...)
#   9: utils.R#554: sampler$class$new(initial_values, model, sampler$parameter
#   10: initialize(...)
#   11: inference_class.R#302: super$initialize(initial_values = initial_values
#   12: inference_class.R#51: self$set_initial_values(initial_values)
#   13: inference_class.R#154: lapply(init_list, self$check_initial_values)
#   14: FUN(X[[i]], ...)

So this is happening at self$check_initial_values

self$check_initial_values does the following

checks the values are intialised/set - for those that aren't
it samples a small value from rnorm
then it checks that the values are valid with self$valid_parameters
self$valid_parameters does the following:

      valid_parameters = function(parameters) {
      dag <- self$model$dag
      tf_parameters <- fl(array(
        data = parameters,
        dim = c(1, length(parameters))
      ))
       ld <- lapply(
         dag$tf_log_prob_function(tf_parameters),
         as.numeric
       )
       is.finite(ld$adjusted) && is.finite(ld$unadjusted)

my understanding of this (tf_log_prob_function) does

make a TF array of the parameters - which are the rnorm values
apply the log prob function on it, this calls self$generate_log_prob_function() which has a lot of code to fit in here.
check that the log prob values aren't infinite

The issue is that the values returned by tf_log_prob_function are -Inf or Inf for when sampling from uniform, but not for normal so when we run

uniform_samples <- mcmc(model(uniform(0,1)))

and trace this through to the valid_parameters function, here tf_parameters contains:

tf.Tensor([[-0.06387527]], shape=(1, 1), dtype=float64)

and then dag$tf_log_prob_function(tf_parameters)

returns

$adjusted
tf.Tensor([-inf], shape=(1), dtype=float64)
$unadjusted
tf.Tensor([-inf], shape=(1), dtype=float64)

But this is what happens with normal

normal_samples <- mcmc(model(normal(0,1)))
tf_parameters
tf.Tensor([[-0.11163191]], shape=(1, 1), dtype=float64)
lapply(
  dag$tf_log_prob_function(tf_parameters),
   as.numeric
)

gives

# $adjusted
# [1] -0.9251694
#
# $unadjusted
# [1] -0.9251694

So this tells us that something is going on with tf_log_prob_function

Need to investigate more there

goldingn commented 1 year ago

Is tf_parameters supposed to be the free state? Or the actual values for the parameters? I think from the context it's supposed to be the free state.

-0.06387527 would be an invalid suggestion for the actual parameter of U(0, 1), so it should return Inf when checking the density. But that should never happen; the rnorm() bit should be generating proposals on the free state, and then inside tf_log_prob_function() those should be transformed into appropriate parameter values (so converted to the unit interval via a logit transform in the case of uniform). It's worth double checking that that is happening. there was some funny stuff with the chained bijectors (which do this transformation) before, so maybe that's where the bug is for this one?

njtierney commented 1 year ago

Thanks, Nick!

Tracing the function through the debugger I believe tf_parameters is the free state.

-0.06387527 would be an invalid suggestion for the actual parameter of U(0, 1), so it should return Inf when checking the density.

Yup that makes sense

But that should never happen; the rnorm() bit should be generating proposals on the free state, and then inside tf_log_prob_function() those should be transformed into appropriate parameter values (so converted to the unit interval via a logit transform in the case of uniform). It's worth double checking that that is happening. there was some funny stuff with the chained bijectors (which do this transformation) before, so maybe that's where the bug is for this one?

Hmmm! So I wonder if that is supposed to happen inside of evaluate_density?

Basically the parts inside of tf_log_prob_function are:

# temporarily define a new environment
 tfe_old <- self$tf_environment
 on.exit(self$tf_environment <- tfe_old)
 tfe <- self$tf_environment <- new.env()
# put the free state in the environment, and build out the tf graph
tfe$free_state <- free_state

# we now make all of the operations define themselves now
 self$define_tf()
# define the densities
 self$define_joint_density()

And then define_joint_density() is

    define_joint_density = function() {
      # browser()
      tfe <- self$tf_environment

      # get all distribution nodes that have a target
      distribution_nodes <- self$node_list[self$node_types == "distribution"]
      target_nodes <- lapply(distribution_nodes, member, "get_tf_target_node()")
      has_target <- !vapply(target_nodes, is.null, FUN.VALUE = TRUE)
      distribution_nodes <- distribution_nodes[has_target]
      target_nodes <- target_nodes[has_target]

      # get the densities, evaluated at these targets
      densities <- mapply(self$evaluate_density,
                          distribution_nodes,
                          target_nodes,
                          SIMPLIFY = FALSE
      )

Then stepping into evaluate_density - it looks like:

    evaluate_density = function(distribution_node, target_node) {
      tfe <- self$tf_environment

      parameter_nodes <- distribution_node$parameters

      # get the tensorflow objects for these
      distrib_constructor <- self$get_tf_object(distribution_node)
      tf_target <- self$get_tf_object(target_node)
      tf_parameter_list <- lapply(parameter_nodes, self$get_tf_object)

      # execute the distribution constructor functions to return a tfp
      # distribution object
      tfp_distribution <- distrib_constructor(tf_parameter_list, dag = self)
      # browser()
      self$tf_evaluate_density(tfp_distribution,
                               tf_target,
                               truncation = distribution_node$truncation,
                               bounds = distribution_node$bounds
      )
    },

debugging this, and running

self$tf_evaluate_density(tfp_distribution,
                                tf_target,
                                truncation = distribution_node$truncation,
                                bounds = distribution_node$bounds
       )

Gives

tf.Tensor([[[-inf]]], shape=(1, 1, 1), dtype=float64)

So, then, stepping into self$tf_evaluate_density

We get:

    tf_evaluate_density = function(tfp_distribution,
                                   tf_target,
                                   truncation = NULL,
                                   bounds = NULL) {

      # get the uncorrected log density
      ld <- tfp_distribution$log_prob(tf_target)

      # if required, calculate the log-adjustment to the truncation term of
      # the density function i.e. the density of a distribution, truncated
      # between a and b, is the non truncated density, divided by the integral
      # of the density function between the truncation bounds. This can be
      # calculated from the distribution's CDF
      if (!is.null(truncation)) {
        lower <- truncation[[1]]
        upper <- truncation[[2]]

        if (all(lower == bounds[1])) {

          # if only upper is constrained, just need the cdf at the upper
          offset <- tfp_distribution$log_cdf(fl(upper))
        } else if (all(upper == bounds[2])) {

          # if only lower is constrained, get the log of the integral above it
          offset <- tf$math$log(fl(1) - tfp_distribution$cdf(fl(lower)))
        } else {

          # if both are constrained, get the log of the integral between them
          offset <- tf$math$log(tfp_distribution$cdf(fl(upper)) -
                                  tfp_distribution$cdf(fl(lower)))
        }

        ld <- ld - offset
      }

      ld
    },

So debugging this, tf_target is

Browse[5]> tf_target
tf.Tensor([[[1.47212097]]], shape=(1, 1, 1), dtype=float64)

And then we're at the point where we calculate the log_prob

ld <- tfp_distribution$log_prob(tf_target)

And it seems that perhaps tf_target hasn't been transformed yet? Because then we get

Browse[5]> tfp_distribution$log_prob(tf_target)

tf.Tensor([[[-inf]]], shape=(1, 1, 1), dtype=float64)

And having a bit of a poke around https://github.com/greta-dev/greta/blob/master/R/dag_class.R

It looks like this hasn't changed much from the TF2 branch.

So I'm not quite sure how to solve this issue of ensuring that the values are appropriately transformed? I'm most likely missing something!

njtierney commented 1 year ago

OK so I think we might be onto the right path regarding the issue with the chained bijector.

I'm just not sure how to get the TF code to evaluate so I can interactively debug the bijectors, which would make debugging it a lot easier.

njtierney commented 1 year ago

OK so the bug was that

tf_scalar_neg_pos_bijector <- function(dim, lower, upper) {
  tf_scalar_biject(
    # tfp$bijectors$AffineScalar(shift = fl(lower), scale = fl(upper - lower)),
    tfb_shift_scale(shift = fl(lower), scale = fl(upper - lower)),
    tfp$bijectors$Sigmoid(),
    dim = dim
  )
}

had the wrong value for shift - it was upper. So we were getting a shift and scale of 1 and 1 for uniform...and other cases.

njtierney commented 1 year ago

test_distributions.R now works

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
source("tests/testthat/helpers.R")

## error 1
# multivariate discrete
y <- extraDistr::rmnom(5, size = 4, prob = runif(3))
p <- uniform(0, 1, dim = 3)
distribution(y) <- multinomial(4, t(p), n_realisations = 5)
sample_distribution(p)

## error 2
alpha <- uniform(0, 10, dim = c(1, 5))
x <- dirichlet(alpha)
m <- model(x)
draws <- mcmc(m, n_samples = 100, warmup = 100, verbose = FALSE)

## error 3
n <- 10
k <- 3

# multinomial
size <- 5
x <- t(rmultinom(n, size, runif(k)))
p <- uniform(0, 1, dim = c(n, k))
distribution(x) <- multinomial(size, p)
m <- model(p)
expect_ok(draws <- mcmc(m, warmup = 0, n_samples = 5, verbose = FALSE))

## error 4
n <- 10
k <- 3

# categorical
x <- t(rmultinom(n, 1, runif(k)))
p <- uniform(0, 1, dim = c(n, k))
distribution(x) <- categorical(p)
m <- model(p)
expect_ok(draws <- mcmc(m, warmup = 0, n_samples = 5, verbose = FALSE))

^{Created on 2022-10-11 by the reprex package (v2.0.1)}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.2.1 (2022-06-23) #> os macOS Monterey 12.3.1 #> system aarch64, darwin20 #> ui X11 #> language (EN) #> collate en_AU.UTF-8 #> ctype en_AU.UTF-8 #> tz Australia/Perth #> date 2022-10-11 #> pandoc 2.19.2 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> ! package * version date (UTC) lib source #> abind 1.4-5 2016-07-21 [1] CRAN (R 4.2.0) #> backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0) #> base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.2.0) #> brio 1.1.3 2021-11-30 [1] CRAN (R 4.2.0) #> cachem 1.0.6 2021-08-19 [1] CRAN (R 4.2.0) #> callr 3.7.2 2022-08-22 [1] CRAN (R 4.2.0) #> cli 3.3.0.9000 2022-06-15 [1] Github (r-lib/cli@31a5db5) #> coda 0.19-4 2020-09-30 [1] CRAN (R 4.2.0) #> codetools 0.2-18 2020-11-04 [1] CRAN (R 4.2.1) #> crayon 1.5.1 2022-03-26 [1] CRAN (R 4.2.0) #> desc 1.4.2 2022-09-08 [1] CRAN (R 4.2.0) #> devtools 2.4.4 2022-07-20 [1] CRAN (R 4.2.0) #> digest 0.6.29 2021-12-01 [1] CRAN (R 4.2.0) #> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0) #> evaluate 0.16 2022-08-09 [1] CRAN (R 4.2.0) #> extraDistr 1.9.1 2020-09-07 [1] CRAN (R 4.2.0) #> fansi 1.0.3 2022-03-24 [1] CRAN (R 4.2.0) #> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.0) #> fs 1.5.2 2021-12-08 [1] CRAN (R 4.2.0) #> future 1.27.0 2022-07-22 [1] CRAN (R 4.2.0) #> globals 0.16.0 2022-08-05 [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 2022-10-10 [?] load_all() #> here 1.0.1 2020-12-13 [1] CRAN (R 4.2.0) #> highr 0.9 2021-04-16 [1] CRAN (R 4.2.0) #> hms 1.1.1 2021-09-26 [1] CRAN (R 4.2.0) #> htmltools 0.5.3 2022-07-18 [1] CRAN (R 4.2.0) #> htmlwidgets 1.5.4 2021-09-08 [1] CRAN (R 4.2.0) #> httpuv 1.6.5 2022-01-05 [1] CRAN (R 4.2.0) #> jsonlite 1.8.0 2022-02-22 [1] CRAN (R 4.2.0) #> knitr 1.40 2022-08-24 [1] CRAN (R 4.2.0) #> later 1.3.0 2021-08-18 [1] CRAN (R 4.2.0) #> lattice 0.20-45 2021-09-22 [1] CRAN (R 4.2.1) #> lifecycle 1.0.2 2022-09-09 [1] CRAN (R 4.2.0) #> listenv 0.8.0 2019-12-05 [1] CRAN (R 4.2.0) #> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0) #> Matrix 1.4-1 2022-03-23 [1] CRAN (R 4.2.1) #> memoise 2.0.1 2021-11-26 [1] CRAN (R 4.2.0) #> mime 0.12 2021-09-28 [1] CRAN (R 4.2.0) #> miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.2.0) #> parallelly 1.32.1 2022-07-21 [1] CRAN (R 4.2.0) #> pillar 1.8.1 2022-08-19 [1] CRAN (R 4.2.0) #> pkgbuild 1.3.1 2021-12-20 [1] CRAN (R 4.2.0) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0) #> pkgload 1.3.0 2022-06-27 [1] CRAN (R 4.2.0) #> png 0.1-7 2013-12-03 [1] CRAN (R 4.2.0) #> prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.2.0) #> processx 3.7.0 2022-07-07 [1] CRAN (R 4.2.0) #> profvis 0.3.7 2020-11-02 [1] CRAN (R 4.2.0) #> progress 1.2.2 2019-05-16 [1] CRAN (R 4.2.0) #> promises 1.2.0.1 2021-02-11 [1] CRAN (R 4.2.0) #> ps 1.7.1 2022-06-18 [1] CRAN (R 4.2.0) #> purrr 0.3.4 2020-04-17 [1] CRAN (R 4.2.0) #> R.cache 0.16.0 2022-07-21 [1] CRAN (R 4.2.0) #> R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.2.0) #> R.oo 1.25.0 2022-06-12 [1] CRAN (R 4.2.0) #> R.utils 2.12.0 2022-06-28 [1] CRAN (R 4.2.0) #> R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0) #> Rcpp 1.0.9 2022-07-08 [1] CRAN (R 4.2.0) #> remotes 2.4.2 2021-11-30 [1] CRAN (R 4.2.0) #> reprex 2.0.1 2021-08-05 [1] CRAN (R 4.2.0) #> reticulate 1.25 2022-05-11 [1] CRAN (R 4.2.0) #> rlang 1.0.5 2022-08-31 [1] CRAN (R 4.2.0) #> rmarkdown 2.16 2022-08-24 [1] CRAN (R 4.2.0) #> rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.2.0) #> rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.2.0) #> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0) #> shiny 1.7.2 2022-07-19 [1] CRAN (R 4.2.0) #> stringi 1.7.8 2022-07-11 [1] CRAN (R 4.2.0) #> stringr 1.4.1 2022-08-20 [1] CRAN (R 4.2.0) #> styler 1.7.0 2022-03-13 [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) #> tibble 3.1.8 2022-07-22 [1] CRAN (R 4.2.0) #> urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.2.0) #> usethis 2.1.6 2022-05-25 [1] CRAN (R 4.2.0) #> utf8 1.2.2 2021-07-24 [1] CRAN (R 4.2.0) #> vctrs 0.4.1 2022-04-13 [1] CRAN (R 4.2.0) #> waldo 0.4.0 2022-03-16 [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 0.33 2022-09-12 [1] CRAN (R 4.2.0) #> xtable 1.8-4 2019-04-21 [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-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 #> #> ────────────────────────────────────────────────────────────────────────────── ```

njtierney commented 1 year ago

and the tests for test_joint.R now work

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
source("tests/testthat/helpers.R")

## error 1 - Error: Could not find reasonable starting values after 20 attempts.
obs <- matrix(rbinom(300, 1, 0.5), 100, 3)
probs <- variable(0, 1, dim = 3)
distribution(obs) <- joint(
  bernoulli(probs[1]),
  bernoulli(probs[2]),
  bernoulli(probs[3]),
  dim = 100
)

sample_distribution(probs)

## error 2 - Error: all(above_lower & below_upper) is not TRUE
x <- joint(
  normal(0, 1, truncation = c(0, Inf)),
  normal(0, 2, truncation = c(-Inf, 0)),
  normal(-1, 1, truncation = c(1, 2))
)

sample_distribution(x, lower = c(0, -Inf, 1), upper = c(Inf, 0, 2))

## error 3 - Error: Could not find reasonable starting values after 20 attempts.
x <- joint(
  uniform(0, 1),
  uniform(0, 2),
  uniform(-1, 0)
)

sample_distribution(x, lower = c(0, 0, -1), upper = c(1, 2, 0))

# intriguingly, this also fails:

sample_distribution(uniform(0,1))

^{Created on 2022-10-11 by the reprex package (v2.0.1)}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.2.1 (2022-06-23) #> os macOS Monterey 12.3.1 #> system aarch64, darwin20 #> ui X11 #> language (EN) #> collate en_AU.UTF-8 #> ctype en_AU.UTF-8 #> tz Australia/Perth #> date 2022-10-11 #> pandoc 2.19.2 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> ! package * version date (UTC) lib source #> abind 1.4-5 2016-07-21 [1] CRAN (R 4.2.0) #> backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0) #> base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.2.0) #> brio 1.1.3 2021-11-30 [1] CRAN (R 4.2.0) #> cachem 1.0.6 2021-08-19 [1] CRAN (R 4.2.0) #> callr 3.7.2 2022-08-22 [1] CRAN (R 4.2.0) #> cli 3.3.0.9000 2022-06-15 [1] Github (r-lib/cli@31a5db5) #> coda 0.19-4 2020-09-30 [1] CRAN (R 4.2.0) #> codetools 0.2-18 2020-11-04 [1] CRAN (R 4.2.1) #> crayon 1.5.1 2022-03-26 [1] CRAN (R 4.2.0) #> desc 1.4.2 2022-09-08 [1] CRAN (R 4.2.0) #> devtools 2.4.4 2022-07-20 [1] CRAN (R 4.2.0) #> digest 0.6.29 2021-12-01 [1] CRAN (R 4.2.0) #> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0) #> evaluate 0.16 2022-08-09 [1] CRAN (R 4.2.0) #> fansi 1.0.3 2022-03-24 [1] CRAN (R 4.2.0) #> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.0) #> fs 1.5.2 2021-12-08 [1] CRAN (R 4.2.0) #> future 1.27.0 2022-07-22 [1] CRAN (R 4.2.0) #> globals 0.16.0 2022-08-05 [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 2022-10-10 [?] load_all() #> here 1.0.1 2020-12-13 [1] CRAN (R 4.2.0) #> highr 0.9 2021-04-16 [1] CRAN (R 4.2.0) #> hms 1.1.1 2021-09-26 [1] CRAN (R 4.2.0) #> htmltools 0.5.3 2022-07-18 [1] CRAN (R 4.2.0) #> htmlwidgets 1.5.4 2021-09-08 [1] CRAN (R 4.2.0) #> httpuv 1.6.5 2022-01-05 [1] CRAN (R 4.2.0) #> jsonlite 1.8.0 2022-02-22 [1] CRAN (R 4.2.0) #> knitr 1.40 2022-08-24 [1] CRAN (R 4.2.0) #> later 1.3.0 2021-08-18 [1] 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njtierney commented 1 year ago

resolved by https://github.com/njtierney/greta/commit/f33591a0a35968675ba2639e604c66a64aa8b5fc

greta-dev / greta

M1 TF2 dev error: test_distributions can not find reasonable starting values after 20 attempts for certain distributions #556