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library(readr)
library(tidyverse)
library(nortest)
# Function to trim the 5% smallest and biggest values from a vector
trim_extreme_values <- function(x, trim_percent = 0.10) {
n <- length(x)
if (n <= 2) {
return(x)
} # Can't trim if there are too few values
# Calculate how many values to trim from each end
trim_count <- floor(n * trim_percent)
# If trim_count is 0 (due to small n), make it at least 1
trim_count <- max(trim_count, 1)
# Sort and trim
sorted_x <- sort(x)
trimmed_x <- sorted_x[(trim_count + 1):(n - trim_count)]
return(trimmed_x)
}
models <- c("Llama-2-7b-hf", "Meta-Llama-3-8B", "Llama-2-13b-hf")
for (model in models) {
df_fnorm <- read_csv(
paste0("~/work/llm-quant/lm-quant-toolkit/src/data/fnorm-", model, ".csv")
)
# Calculate the difference in kurtosis between adjacent layers for each module
df_diff <- df_fnorm |>
group_by(module, layer) |>
summarise(
sensi_score = min(sensitivity),
kurt_score = min(kurtosis)
) |>
group_by(module) |>
arrange(module, layer) |>
mutate(
kurt_diff = kurt_score - lag(kurt_score),
sensi_diff = sensi_score / lag(sensi_score)
) |>
filter(!is.na(kurt_diff)) |>
filter(!is.na(sensi_diff))
kurt_trim_pct <- 0.10
sensi_trim_pct <- 0.20
# Apply trimming for each module and perform Shapiro-Wilk test
trimmed_results <- df_diff |>
group_by(module) |>
summarize(
# Create a trimmed version of kurt_diff
kurt_diff_trimmed = list(
trim_extreme_values(kurt_diff, trim_percent = kurt_trim_pct)
),
kurt_n_trimmed = sapply(kurt_diff_trimmed, length),
# For Shapiro-Wilk test on trimmed data
kurt_shapiro_stat = sapply(kurt_diff_trimmed, function(x) {
if (length(x) >= 3) shapiro.test(x)$statistic else NA
}),
kurt_shapiro_p = sapply(kurt_diff_trimmed, function(x) {
if (length(x) >= 3) shapiro.test(x)$p.value else NA
}),
kurt_normal = sapply(kurt_diff_trimmed, function(x) {
if (length(x) >= 3) shapiro.test(x)$p.value > 0.05 else NA
}),
# Create a trimmed version of sensi_diff
sensi_diff_trimmed = list(
trim_extreme_values(sensi_diff, trim_percent = sensi_trim_pct)
),
sensi_n_trimmed = sapply(sensi_diff_trimmed, length),
# For Shapiro-Wilk test on trimmed data
sensi_shapiro_stat = sapply(sensi_diff_trimmed, function(x) {
if (length(x) >= 3) shapiro.test(x)$statistic else NA
}),
sensi_shapiro_p = sapply(sensi_diff_trimmed, function(x) {
if (length(x) >= 3) shapiro.test(x)$p.value else NA
}),
sensi_normal = sapply(sensi_diff_trimmed, function(x) {
if (length(x) >= 3) shapiro.test(x)$p.value > 0.05 else NA
}),
)
# Create QQ plots for each module using the trimmed data
# Unpack the trimmed data for plotting
df_plot_trimmed_kurt <- df_diff |>
group_by(module) |>
do({
kurt_trimmed_values <- trim_extreme_values(
.$kurt_diff,
trim_percent = kurt_trim_pct
)
data.frame(
module = .$module[1],
kurt_diff_trimmed = kurt_trimmed_values
)
})
df_plot_trimmed_sensi <- df_diff |>
group_by(module) |>
do({
sensi_trimmed_values <- trim_extreme_values(
.$kurt_diff,
trim_percent = sensi_trim_pct
)
data.frame(
module = .$module[1],
sensi_diff_trimmed = sensi_trimmed_values
)
})
# Create QQ plots for kurt diff
plt_qq_kurt <- ggplot(
df_plot_trimmed_kurt, aes(sample = kurt_diff_trimmed)
) +
stat_qq() +
stat_qq_line() +
facet_wrap(~module, scales = "free") +
labs(
title = paste0(model, " - QQ Plots of Kurtosis Differences by Module"),
x = "Theoretical Quantiles",
y = "Sample Quantiles"
) +
theme_minimal()
ggsave(
create.dir = TRUE,
paste0("pdfs/qq_kurt_", model, ".pdf"),
plot = plt_qq_kurt,
width = 10,
height = 6
)
# Create QQ plots for sensi diff
plt_qq_sensi <- ggplot(
df_plot_trimmed_sensi, aes(sample = sensi_diff_trimmed)
) +
stat_qq() +
stat_qq_line() +
facet_wrap(~module, scales = "free") +
labs(
title = paste0(model, " - QQ Plots of Sensitivity Differences by Module"),
x = "Theoretical Quantiles",
y = "Sample Quantiles"
) +
theme_minimal()
ggsave(
paste0("pdfs/qq_sensi_", model, ".pdf"),
plot = plt_qq_sensi,
width = 10,
height = 6
)
hist_fill_color <- "#66c2a5"
density_line_color <- "#fc8d62"
norm_line_color <- "blue"
# Create histograms with normal curve overlay for trimmed data
plt_hist_kurt <- ggplot(df_plot_trimmed_kurt, aes(x = kurt_diff_trimmed)) +
geom_histogram(
aes(y = after_stat(density)),
bins = 10,
fill = hist_fill_color,
color = "black"
) +
geom_density(color = density_line_color, linewidth = 1) +
stat_function(
fun = dnorm,
args = list(
mean = mean(df_plot_trimmed_kurt$kurt_diff_trimmed),
sd = sd(df_plot_trimmed_kurt$kurt_diff_trimmed)
),
color = norm_line_color, linewidth = 1, linetype = "dashed"
) +
facet_wrap(~module, scales = "free") +
labs(
# title = paste0(
# model,
# " - Histograms of Kurtosis Differences with Normal Curve Overlay"
# ),
x = paste0(
"Kurtosis Difference (",
formatC(kurt_trim_pct * 100, format = "f", digits = 0),
"% Trimmed)"
),
y = "Density"
) +
theme_minimal()
ggsave(
paste0("pdfs/hist-kurt-", model, ".pdf"),
plot = plt_hist_kurt,
width = 10,
height = 6
)
# Create histograms with normal curve overlay for trimmed data
plt_hist_sensi <- ggplot(df_plot_trimmed_sensi, aes(x = sensi_diff_trimmed)) +
geom_histogram(
aes(y = after_stat(density)),
bins = 10,
fill = hist_fill_color,
color = "black"
) +
geom_density(color = density_line_color, linewidth = 1) +
stat_function(
fun = dnorm,
args = list(
mean = mean(df_plot_trimmed_sensi$sensi_diff_trimmed),
sd = sd(df_plot_trimmed_sensi$sensi_diff_trimmed)
),
color = norm_line_color, linewidth = 1, linetype = "dashed"
) +
facet_wrap(~module, scales = "free") +
labs(
# title = paste0(
# model,
# " - Histograms of Sensitivity Differences with Normal Curve Overlay"
# ),
x = paste0(
"Sensitivity Difference (",
formatC(sensi_trim_pct * 100, format = "f", digits = 0),
"% Trimmed)"
),
y = "Density"
) +
theme_minimal()
ggsave(
paste0("pdfs/hist-sensi-", model, ".pdf"),
plot = plt_hist_sensi,
width = 10,
height = 6
)
}
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