File size: 3,907 Bytes
21ad80b | 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 | library(ggplot2)
library(ggthemes)
library(openxlsx)
library(patchwork)
library(readr)
library(tidyverse)
calc_bpp <- function(b1, g1, b2, g2) {
return(round(b1 + 2 * b2 / g1 + 32 / g1 / g2, digits = 2))
}
# df_cfg1 <- read_csv("data/mxq-quant-cfgs-mxq1.csv")
# df_cfg2 <- read_csv("data/kurt/global/llama-mxq-cfgs.csv")
# df_cfg3 <- read_csv("data/kurt/scaled/llama-mxq-cfgs.csv")
# df_cfg4 <- read_csv("data/mxq-quant-cfgs-mxq1-5pct-tol.csv")
# df_cfg5 <- read_csv("data/mxq-quant-cfgs-kurt-scaled-6pct-tol.csv")
# df_cfg1$attempt <- "MXQ1"
# df_cfg2$attempt <- "kurt-global"
# df_cfg3$attempt <- "kurt-scaled"
# df_cfg4$attempt <- "PCT5"
# df_cfg5$attempt <- "kscaled-pct6"
# df_cfg <- bind_rows(df_cfg1, df_cfg2, df_cfg3, df_cfg4, df_cfg5)
df_cfg <- read_csv("mxq-mem-bound-check.csv")
df_mem_sum <- df_cfg |>
group_by(
model, bit_budget, attempt
) |>
summarise(
mem_tot = sum(memmb)
) |>
pivot_wider(
names_from = "attempt",
values_from = "mem_tot"
) |>
ungroup()
df_7b <- read_csv("data/fnorm/fnorm-Llama-2-7b-hf.csv")
df_13b <- read_csv("data/fnorm/fnorm-Llama-2-13b-hf.csv")
df_8b <- read_csv("data/fnorm/fnorm-Meta-Llama-3-8B.csv")
df_7b$model <- "Llama-2-7b-hf"
df_13b$model <- "Llama-2-13b-hf"
df_8b$model <- "Meta-Llama-3-8B"
df_llama <- bind_rows(df_7b, df_13b, df_8b) |>
mutate(
bit_budget = calc_bpp(nbit1, gsize1, nbit2, gsize2)
) |>
group_by(model, bit_budget) |>
summarise(
mem_tot = sum(memmb),
param_tot = sum(params)
)
df_mem <- df_mem_sum |>
left_join(
df_llama,
by = c("model", "bit_budget")
) |>
rename(
hqq = mem_tot
) |>
mutate(
theory = param_tot * bit_budget / 8 / 1024^2,
) |>
select(!c("param_tot"))
write.xlsx(df_mem, "df_mem.xlsx", overwrite = TRUE, asTable = TRUE)
mem_gap_grid <- function(df_mem, mod, show_legend = FALSE, show_x_label = FALSE) {
df_disp <- df_mem |>
filter(model == mod) |>
pivot_longer(
cols = c("mxq1", "pct5", "pct6", "kurt-global", "kurt-scaled", "hqq", "theory"),
names_to = "attempt",
values_to = "memory"
)
df_gap <- df_mem |>
filter(model == mod) |>
mutate(
gap_in_pct = 100 * (theory - mxq1) / mxq1
) |>
select(c("bit_budget", "gap_in_pct"))
# Gap percentage line plot (on top)
gap_line_plot <- ggplot(df_gap, aes(x = bit_budget, y = gap_in_pct)) +
geom_line(color = "blue") +
theme_gray(base_size = 12) +
labs(y = "% Min Mem Gap") +
theme_minimal() +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank()
)
# line plot (on bottom)
line_plot <- ggplot(
df_disp,
aes(x = bit_budget, y = memory)
) +
geom_line(aes(color = attempt)) +
geom_point(aes(shape = attempt, color = attempt)) +
labs(x = "Bit Budget", y = "Memory") +
theme_gray(base_size = 12) +
guides(color = guide_legend(ncol = 1)) +
facet_wrap(~model, scales = "free", ncol = 1)
if (!show_x_label) {
line_plot <- line_plot +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank()
)
}
if (show_legend) {
line_plot <- line_plot +
theme(
legend.position = "right",
legend.text = element_text(size = 12),
legend.title = element_text(size = 12)
) +
scale_color_solarized()
} else {
line_plot <- line_plot +
theme(legend.position = "none") +
scale_color_solarized()
}
# Combine the line and bar plot vertically
combined_plot <- gap_line_plot / line_plot + plot_layout(heights = c(1, 3))
return(combined_plot)
}
p1 <- mem_gap_grid(df_mem, "Llama-2-7b-hf")
p2 <- mem_gap_grid(df_mem, "Llama-2-13b-hf", show_x_label = TRUE)
p3 <- mem_gap_grid(df_mem, "Meta-Llama-3-8B", show_legend = TRUE)
final_plot1 <- (p1 | p2 | p3)
final_plot1
ggsave(
paste0("pdfs/", "mxq-mem-gap.pdf"),
plot = final_plot1, width = 16, height = 9
)
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