Comments (3)
njtierney
commented on August 26, 2024
Related - TF2 error - Failure (test_inference.R:305): mcmc supports rwmh sampler with uniform proposals
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 2.10.0
source(here::here("tests", "testthat", "helpers.R"))
set.seed(5)
x <- uniform(0, 1)
m <- model(x)
expect_ok(draws <- mcmc(m,
sampler = rwmh("uniform"),
n_samples = 100, warmup = 100,
verbose = FALSE
))
#> Error: `expr` threw an unexpected error.
#> Message: greta hit a tensorflow error:
#> Error in py_call_impl(callable, dots$args, dots$keywords): RuntimeError:
#> Evaluation error: ValueError: Cannot reshape a tensor with 0 elements to shape
#> [1] (1 elements) for '{{node Reshape}} = Reshape[T=DT_DOUBLE,
#> Tshape=DT_INT32](Mul, Reshape/shape)' with input shapes: [0], [1] and with
#> input tensors computed as partial shapes: input[1] = [1]. .
#> Class: simpleError/error/conditionCreated on 2022-12-09 with reprex v2.0.2
Session info
sessioninfo::session_info()
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#> ──────────────────────────────────────────────────────────────────────────────from greta.
njtierney
commented on August 26, 2024
Current progress is linked in file, tf2-test-inference-mcmcm-rwmh-normal-uniform.qmd locally
The file contains the following:
We get an error for rwmh like so
x <- normal(0, 1)
m <- model(x)
# this errors
draws <- mcmc(m,
sampler = rwmh("normal"),
n_samples = 100,
warmup = 100,
verbose = FALSE)
The error being
Error: greta hit a tensorflow error:
Error in py_call_impl(callable,
dots$args, dots$keywords):
RuntimeError: Evaluation error:
ValueError: Cannot reshape a tensor
with 0 elements to shape [1] (1
elements) for '{{node Reshape}} =
Reshape[T=DT_DOUBLE,
Tshape=DT_INT32](Mul, Reshape/shape)'
with input shapes: [0], [1] and with
input tensors computed as partial
shapes: input[1] = [1]. .
Minimally, the code to fail is:
x <- normal(0, 1)
m <- model(x)
draws <- mcmc(m, sampler = rwmh())
So my thought is that there is something the wrong shape or dimension here
related to rwmh sampler?
We don't get this error with the default sampler, hmc.
x <- normal(0, 1)
m <- model(x)
draws <- mcmc(m, sampler = hmc())
Let's go a journey - first step into mcmc
debugonce(mcmc)
x <- normal(0, 1)
m <- model(x)
draws <- mcmc(m, sampler = rwmh())
OK so the error occurs here in sample_carefully
#| eval: false
result <- cleanly(
self$tf_evaluate_sample_batch(
free_state = tensorflow::as_tensor(
free_state,
dtype = tf_float()
),
sampler_burst_length = tensorflow::as_tensor(sampler_burst_length),
sampler_thin = tensorflow::as_tensor(sampler_thin),
sampler_param_vec = tensorflow::as_tensor(
sampler_param_vec,
dtype = tf_float(),
shape = length(sampler_param_vec)
)
)
)
I'll place a browser there and take a look
x <- normal(0, 1)
m <- model(x)
draws <- mcmc(m, sampler = rwmh())
OK and because the code uses tf_function it needs to be undone there somehow
Breaking down this code
result <- cleanly(
self$tf_evaluate_sample_batch(
free_state = tensorflow::as_tensor(
free_state,
dtype = tf_float()
),
sampler_burst_length = tensorflow::as_tensor(sampler_burst_length),
sampler_thin = tensorflow::as_tensor(sampler_thin),
sampler_param_vec = tensorflow::as_tensor(
sampler_param_vec,
dtype = tf_float(),
shape = length(sampler_param_vec)
)
)
)
The outputs of the free_state, sampler_burst_length, and sampler_thin, sampler_param_vec are:
free_state
sampler_burst_length
sampler_thin
sampler_param_vec
Browse[2]> free_state
all_forward_variable_1
[1,] 0.04096190
[2,] 0.01090323
[3,] -0.15063224
[4,] 0.08087937
Browse[2]> sampler_burst_length
[1] 3
Browse[2]> sampler_thin
[1] 1
Browse[2]> sampler_param_vec
rwmh_epsilon rwmh_diag_sd
0.1 1.0
And what does this comparative part look like in hmc()?
x <- normal(0, 1)
m <- model(x)
draws <- mcmc(m, sampler = hmc())
free_state
sampler_burst_length
sampler_thin
sampler_param_vec
Browse[2]> free_state
all_forward_variable_1
[1,] 0.13124380
[2,] -0.12720201
[3,] -0.15659195
[4,] 0.08557503
Browse[2]> sampler_burst_length
[1] 3
Browse[2]> sampler_thin
[1] 1
Browse[2]> sampler_param_vec
hmc_l hmc_epsilon hmc_diag_sd
6.0 0.1 1.0
OK so it's not going to awry at that point...
Overall, tf_evaluate_sample_batch is a tf function, written like so:
#| eval: false
self$tf_evaluate_sample_batch <- tensorflow::tf_function(
f = self$define_tf_draws,
input_signature = list(
# free state
tf$TensorSpec(shape = list(NULL, self$n_free),
dtype = tf_float()),
# sampler_burst_length
tf$TensorSpec(shape = list(),
dtype = tf$int32),
# sampler_thin
tf$TensorSpec(shape = list(),
dtype = tf$int32),
# sampler_param_vec
tf$TensorSpec(shape = list(
length(
unlist(
self$sampler_parameter_values()
)
)
),
dtype = tf_float())
)
)
I can't seem to work out how to remove the tf_function part of so we can debug
it.
I tried to just use self$define_tf_draws as it's own function, defining
the inputs, free_state etc, as either the named variables, or as the Tensors
above, but I think I'm missing something. Not sure how to proceed further from here.
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hrlai
commented on August 26, 2024
Hi @njtierney I got similar error from mcmc today when testing out the TF2 version from #534
mcmc(m,
sampler = hmc(10, 15),
warmup = 4000,
n_samples = 1000,
chains = 1,
one_by_one = TRUE)
gave:
warmup 0/4000 | eta: ?s
Error in self$sample_carefully(free_state = self$free_state, sampler_burst_length = as.integer(n_samples), :
object 'n_samples' not found
Sorry I wasn't able to give a minimal working example to reproduce the error now. But will keep looking into it. Posting here in case it is something that you're tackling at the moment.
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- Deprecate optimisers accessed through compat?
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- Creating 'greta-env' conda environment using python v3.7 failed HOT 2
- TF2 Errors in `test_inference.R` - bad proposals are not rejected HOT 1
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