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998922f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | #!/usr/bin/env Rscript
library(tidyverse)
library(readr)
library(ggthemes)
library(ggplot2)
library(patchwork)
library(optparse)
weight_grid <- function(
df_wdist, df_kurtosis, mod, show_legend = FALSE, show_cfg = TRUE) {
df_mod_wdist <- df_wdist |> filter(module == mod)
df_mod_kurt <- df_kurtosis |> filter(module == mod)
# Line plot (on top)
line_plot <- ggplot(df_mod_kurt, aes(x = layer, y = kurtosis)) +
geom_line(color = "blue") +
theme_gray(base_size = 14) +
theme_minimal() +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank()
)
# Bar plot (on bottom)
module_disp <- df_mod_wdist$mod_disp[1]
bar_plot <- ggplot(
df_mod_wdist, aes(x = layer, y = abs_val, fill = nth_percentile)
) +
geom_bar(stat = "identity", color = "gray50") +
theme_gray(base_size = 14) +
labs(
x = module_disp, y = "Absolute Value", fill = "nth percentile"
)
if (show_cfg) {
bar_plot <- bar_plot +
geom_text(
data = subset(df_mod_wdist, nth_percentile == 100),
aes(x = layer, label = quant_cfg),
angle = 90,
vjust = 0.20,
position = position_stack(vjust = 0.5),
colour = "white",
size = 2
)
}
if (show_legend) {
bar_plot <- bar_plot +
theme(
legend.position = "bottom",
legend.text = element_text(size = 16),
legend.title = element_text(size = 16)
) +
# coord_flip() +
scale_color_solarized()
} else {
bar_plot <- bar_plot +
theme(legend.position = "none") +
scale_color_solarized()
}
# Combine the line and bar plot vertically
combined_plot <- line_plot / bar_plot + plot_layout(heights = c(1, 3))
return(combined_plot)
}
weight_grid_only <- function(
df_wdist, df_kurtosis, mod, show_legend = FALSE) {
return(weight_grid(df_wdist, df_kurtosis, mod, show_legend, show_cfg = FALSE))
}
parser <- OptionParser()
parser <- add_option(
parser, c("-w", "--weight_dist"),
action = "store_true",
default = FALSE,
type = "logical",
help = "Generate weight column diagram without cfg",
metavar = "logical"
)
parser <- add_option(
parser, c("-m", "--model"),
type = "character",
help = "The model for which the cfg column diagram is generated",
metavar = "character"
)
args <- parse_args(parser)
if (is.null(args$model)) {
model_id <- "Llama-2-7b-hf"
} else {
model_id <- args$model
}
is_plot_weight <- args$weight_dist
df_cfgs <- read_csv("data/llama-mxq-cfgs.csv")
df_all <- read_csv(paste0("data/wdist/wdist-", model_id, ".csv"))
k_cols <- c("module", "layer", "kurtosis")
df_kurtosis <- df_all |>
select(all_of(k_cols))
percentiles <- c("0", "99", "99.9", "99.99", "100")
module_param_count <- df_all |>
select(
module, param_count
) |>
group_by(module) |>
summarise(
param_count = sum(param_count)
) |>
mutate(
mod_disp = paste0(module, "(", formatC(param_count, big.mark = ","), ")")
)
budgets <- list(3.51, 3.25, 3.13, 4.51, 4.25, 4.13)
for (budget in budgets) {
df_cfg_1 <- df_cfgs |>
filter(bit_budget == budget & model == model_id) |>
mutate(
quant_cfg = paste0("b", b1, "g", g1)
) |>
select(-c("b1", "g1", "b2", "g2", "bit_budget"))
all_cols <- c("module", "layer", percentiles)
df_wdist <- df_all |>
mutate(
`0` = percentile_0,
`99` = percentile_99 - percentile_0,
`99.9` = percentile_999 - percentile_99,
`99.99` = percentile_9999 - percentile_999,
`100` = percentile_100 - percentile_9999,
) |>
select(all_of(all_cols)) |>
pivot_longer(
cols = percentiles,
names_to = "nth_percentile",
names_transform = list(nth_percentile = as.numeric),
values_to = "abs_val"
) |>
mutate(
nth_percentile = factor(nth_percentile, levels = rev(percentiles))
) |>
# filter(!grepl("_layernorm", module)) |>
left_join(module_param_count, by = c("module")) |>
left_join(df_cfg_1, by = c("module", "layer"))
if (is_plot_weight) {
p1 <- weight_grid_only(df_wdist, df_kurtosis, "input_layernorm")
p2 <- weight_grid_only(df_wdist, df_kurtosis, "mlp.down_proj")
p3 <- weight_grid_only(df_wdist, df_kurtosis, "mlp.gate_proj")
p4 <- weight_grid_only(df_wdist, df_kurtosis, "mlp.up_proj")
p5 <- weight_grid_only(df_wdist, df_kurtosis, "post_attention_layernorm")
p6 <- weight_grid_only(df_wdist, df_kurtosis, "self_attn.k_proj")
p7 <- weight_grid_only(df_wdist, df_kurtosis, "self_attn.o_proj")
p8 <- weight_grid_only(df_wdist, df_kurtosis, "self_attn.q_proj", TRUE)
p9 <- weight_grid_only(df_wdist, df_kurtosis, "self_attn.v_proj")
# Create a 3x3 grid of combined plots
final_plot1 <- (p1 | p2 | p3) / (p4 | p5 | p6) / (p7 | p8 | p9)
ggsave(
paste0("pdfs/", model_id, "-", budget, "-wdist-kurtosis.pdf"),
plot = final_plot1,
width = 16, height = 9
)
} else {
p2 <- weight_grid(df_wdist, df_kurtosis, "mlp.down_proj")
p3 <- weight_grid(df_wdist, df_kurtosis, "mlp.gate_proj")
p4 <- weight_grid(df_wdist, df_kurtosis, "mlp.up_proj")
p6 <- weight_grid(df_wdist, df_kurtosis, "self_attn.k_proj")
p7 <- weight_grid(df_wdist, df_kurtosis, "self_attn.o_proj")
p8 <- weight_grid(df_wdist, df_kurtosis, "self_attn.q_proj", TRUE)
p9 <- weight_grid(df_wdist, df_kurtosis, "self_attn.v_proj")
final_plot2 <- (p2 | p3 | p4) / (p6 | p7) / (p8 | p9)
ggsave(
paste0("pdfs/", model_id, "-", budget, "-mxq-cfgs-from-model.pdf"),
plot = final_plot2,
width = 12, height = 9
)
}
}
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