mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-10 07:15:01 +08:00
676 lines
21 KiB
Python
676 lines
21 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
from collections.abc import Callable
|
|
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
import torch
|
|
|
|
from vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe import (
|
|
silu_mul_fp8_quant_deep_gemm_cuda,
|
|
)
|
|
from vllm.platforms import current_platform
|
|
from vllm.triton_utils import tl, triton
|
|
from vllm.utils.deep_gemm import is_deep_gemm_e8m0_used
|
|
|
|
|
|
@triton.jit
|
|
def _silu_mul_fp8_quant_deep_gemm(
|
|
# Pointers ------------------------------------------------------------
|
|
input_ptr, # 16-bit activations (E, T, 2*H)
|
|
y_q_ptr, # fp8 quantized activations (E, T, H)
|
|
y_s_ptr, # 16-bit scales (E, T, G)
|
|
counts_ptr, # int32 num tokens per expert (E)
|
|
# Sizes ---------------------------------------------------------------
|
|
H: tl.constexpr, # hidden dimension (per output)
|
|
GROUP_SIZE: tl.constexpr, # elements per group (usually 128)
|
|
# Strides for input (elements) ---------------------------------------
|
|
stride_i_e,
|
|
stride_i_t,
|
|
stride_i_h,
|
|
# Strides for y_q (elements) -----------------------------------------
|
|
stride_yq_e,
|
|
stride_yq_t,
|
|
stride_yq_h,
|
|
# Strides for y_s (elements) -----------------------------------------
|
|
stride_ys_e,
|
|
stride_ys_t,
|
|
stride_ys_g,
|
|
# Stride for counts (elements)
|
|
stride_counts_e,
|
|
# Numeric params ------------------------------------------------------
|
|
eps: tl.constexpr,
|
|
fp8_min: tl.constexpr,
|
|
fp8_max: tl.constexpr,
|
|
use_ue8m0: tl.constexpr,
|
|
# Meta ---------------------------------------------------------------
|
|
BLOCK: tl.constexpr,
|
|
NUM_STAGES: tl.constexpr,
|
|
):
|
|
G = H // GROUP_SIZE
|
|
|
|
# map program id -> (e, g)
|
|
pid = tl.program_id(0)
|
|
e = pid // G
|
|
g = pid % G
|
|
|
|
e = e.to(tl.int64)
|
|
g = g.to(tl.int64)
|
|
|
|
# number of valid tokens for this expert
|
|
n_tokens = tl.load(counts_ptr + e * stride_counts_e).to(tl.int64)
|
|
|
|
cols = tl.arange(0, BLOCK).to(tl.int64)
|
|
mask = cols < BLOCK
|
|
|
|
base_input_offset = e * stride_i_e + g * GROUP_SIZE * stride_i_h
|
|
base_gate_offset = base_input_offset + cols * stride_i_h
|
|
base_up_offset = base_input_offset + H * stride_i_h + cols * stride_i_h
|
|
base_yq_offset = e * stride_yq_e + g * GROUP_SIZE * stride_yq_h + cols * stride_yq_h
|
|
base_ys_offset = e * stride_ys_e + g * stride_ys_g
|
|
|
|
for t in tl.range(0, n_tokens, num_stages=NUM_STAGES):
|
|
gate = tl.load(
|
|
input_ptr + base_gate_offset + t * stride_i_t, mask=mask, other=0.0
|
|
).to(tl.float32)
|
|
up = tl.load(input_ptr + base_up_offset + t * stride_i_t, mask=mask, other=0.0)
|
|
|
|
gate = gate * (1.0 / (1.0 + tl.exp(-gate)))
|
|
y = gate * up
|
|
|
|
y_s = tl.maximum(tl.max(tl.abs(y)), eps) / fp8_max
|
|
if use_ue8m0:
|
|
y_s = tl.exp2(tl.ceil(tl.log2(y_s)))
|
|
|
|
y_q = tl.clamp(y / y_s, fp8_min, fp8_max).to(y_q_ptr.dtype.element_ty)
|
|
|
|
tl.store(y_q_ptr + base_yq_offset + t * stride_yq_t, y_q, mask=mask)
|
|
tl.store(y_s_ptr + base_ys_offset + t * stride_ys_t, y_s)
|
|
|
|
|
|
def silu_mul_fp8_quant_deep_gemm_triton(
|
|
y: torch.Tensor, # (E, T, 2*H)
|
|
tokens_per_expert: torch.Tensor, # (E,) number of valid tokens per expert
|
|
num_parallel_tokens,
|
|
group_size: int = 128,
|
|
eps: float = 1e-10,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
"""Quantize silu(y[..., :H]) * y[..., H:] to FP8 with group per-token scales
|
|
|
|
y has shape (E, T, 2*H). The first half of the last dimension is
|
|
silu-activated, multiplied by the second half, then quantized into FP8.
|
|
|
|
Returns `(y_q, y_s)` where
|
|
* `y_q`: FP8 tensor, shape (E, T, H), same layout as y[..., :H]
|
|
* `y_s`: FP32 tensor, shape (E, T, H // group_size), strides (T*G, 1, T)
|
|
"""
|
|
assert y.ndim == 3, "y must be (E, T, 2*H)"
|
|
E, T, H2 = y.shape
|
|
assert H2 % 2 == 0, "last dim of y must be even (2*H)"
|
|
H = H2 // 2
|
|
G = (H + group_size - 1) // group_size
|
|
assert H % group_size == 0, "H must be divisible by group_size"
|
|
assert tokens_per_expert.ndim == 1 and tokens_per_expert.shape[0] == E, (
|
|
"tokens_per_expert must be shape (E,)"
|
|
)
|
|
tokens_per_expert = tokens_per_expert.to(device=y.device, dtype=torch.int32)
|
|
|
|
# allocate outputs
|
|
fp8_dtype = torch.float8_e4m3fn
|
|
y_q = torch.empty((E, T, H), dtype=fp8_dtype, device=y.device)
|
|
|
|
# strides (elements)
|
|
stride_i_e, stride_i_t, stride_i_h = y.stride()
|
|
stride_yq_e, stride_yq_t, stride_yq_h = y_q.stride()
|
|
|
|
# desired scale strides (elements): (T*G, 1, T)
|
|
stride_ys_e = T * G
|
|
stride_ys_t = 1
|
|
stride_ys_g = T
|
|
y_s = torch.empty_strided(
|
|
(E, T, G),
|
|
(stride_ys_e, stride_ys_t, stride_ys_g),
|
|
dtype=torch.float32,
|
|
device=y.device,
|
|
)
|
|
|
|
stride_cnt_e = tokens_per_expert.stride()[0]
|
|
|
|
# Static grid over experts and H-groups.
|
|
# A loop inside the kernel handles the token dim
|
|
grid = (E * G,)
|
|
|
|
f_info = torch.finfo(fp8_dtype)
|
|
fp8_max = f_info.max
|
|
fp8_min = f_info.min
|
|
|
|
_silu_mul_fp8_quant_deep_gemm[grid](
|
|
y,
|
|
y_q,
|
|
y_s,
|
|
tokens_per_expert,
|
|
H,
|
|
group_size,
|
|
stride_i_e,
|
|
stride_i_t,
|
|
stride_i_h,
|
|
stride_yq_e,
|
|
stride_yq_t,
|
|
stride_yq_h,
|
|
stride_ys_e,
|
|
stride_ys_t,
|
|
stride_ys_g,
|
|
stride_cnt_e,
|
|
eps,
|
|
fp8_min,
|
|
fp8_max,
|
|
is_deep_gemm_e8m0_used(),
|
|
BLOCK=group_size,
|
|
NUM_STAGES=4,
|
|
num_warps=1,
|
|
)
|
|
|
|
return y_q, y_s
|
|
|
|
|
|
# Parse generation strategies
|
|
strategies = ["uniform", "max_t", "first_t"]
|
|
|
|
|
|
def benchmark(
|
|
kernel: Callable,
|
|
E: int,
|
|
T: int,
|
|
H: int,
|
|
total_tokens: int,
|
|
num_parallel_tokens: int = 64,
|
|
G: int = 128,
|
|
runs: int = 200,
|
|
num_warmups: int = 20,
|
|
gen_strategy: str = "default",
|
|
iterations_per_run: int = 20,
|
|
):
|
|
def generate_data(seed_offset=0):
|
|
"""Generate input data with given seed offset"""
|
|
current_platform.seed_everything(42 + seed_offset)
|
|
y = torch.rand((E, T, 2 * H), dtype=torch.bfloat16, device="cuda").contiguous()
|
|
|
|
if gen_strategy == "uniform":
|
|
r = torch.rand(size=(E,), device="cuda")
|
|
r /= r.sum()
|
|
r *= total_tokens
|
|
tokens_per_expert = r.int()
|
|
tokens_per_expert = torch.minimum(
|
|
tokens_per_expert,
|
|
torch.ones((E,), device=r.device, dtype=torch.int) * T,
|
|
)
|
|
elif gen_strategy == "max_t":
|
|
tokens_per_expert = torch.empty(size=(E,), dtype=torch.int32, device="cuda")
|
|
tokens_per_expert.fill_(total_tokens / E)
|
|
elif gen_strategy == "first_t":
|
|
tokens_per_expert = torch.zeros(size=(E,), dtype=torch.int32, device="cuda")
|
|
tokens_per_expert[0] = min(T, total_tokens)
|
|
else:
|
|
raise ValueError(f"Unknown generation strategy: {gen_strategy}")
|
|
return y, tokens_per_expert
|
|
|
|
dataset_count = 4
|
|
# Pre-generate different input matrices for each iteration to avoid cache effects
|
|
data_sets = [generate_data(i) for i in range(dataset_count)]
|
|
|
|
# Warmup
|
|
y, tokens_per_expert = data_sets[0]
|
|
for _ in range(num_warmups):
|
|
kernel(
|
|
y, tokens_per_expert, num_parallel_tokens=num_parallel_tokens, group_size=G
|
|
)
|
|
torch.cuda.synchronize()
|
|
|
|
start_event = torch.cuda.Event(enable_timing=True)
|
|
end_event = torch.cuda.Event(enable_timing=True)
|
|
|
|
# Benchmark
|
|
latencies: list[float] = []
|
|
for _ in range(runs):
|
|
torch.cuda.synchronize()
|
|
|
|
start_event.record()
|
|
for i in range(iterations_per_run):
|
|
y, tokens_per_expert = data_sets[i % dataset_count]
|
|
kernel(
|
|
y,
|
|
tokens_per_expert,
|
|
num_parallel_tokens=num_parallel_tokens,
|
|
group_size=G,
|
|
)
|
|
end_event.record()
|
|
end_event.synchronize()
|
|
|
|
total_time_ms = start_event.elapsed_time(end_event)
|
|
per_iter_time_ms = total_time_ms / iterations_per_run
|
|
latencies.append(per_iter_time_ms)
|
|
|
|
# Use median instead of average for better outlier handling
|
|
median_time_ms = np.median(latencies)
|
|
median_time_s = median_time_ms / 1000
|
|
|
|
# Calculate actual work done (using first dataset for consistency)
|
|
_, tokens_per_expert = data_sets[0]
|
|
actual_tokens = tokens_per_expert.sum().item()
|
|
actual_elements = actual_tokens * H
|
|
|
|
# GFLOPS: operations per element = exp + 3 muls + 1 div + quantization ops ≈ 8 ops
|
|
ops_per_element = 8
|
|
total_ops = actual_elements * ops_per_element
|
|
gflops = total_ops / median_time_s / 1e9
|
|
|
|
# Memory bandwidth: bfloat16 inputs (2 bytes), fp8 output (1 byte), scales (4 bytes)
|
|
input_bytes = actual_tokens * 2 * H * 2 # 2*H bfloat16 inputs
|
|
output_bytes = actual_tokens * H * 1 # H fp8 outputs
|
|
scale_bytes = actual_tokens * (H // G) * 4 # scales in float32
|
|
total_bytes = input_bytes + output_bytes + scale_bytes
|
|
memory_bw = total_bytes / median_time_s / 1e9
|
|
|
|
HOPPER_BANDWIDTH_TBPS = 3.35
|
|
return (
|
|
median_time_ms,
|
|
gflops,
|
|
memory_bw,
|
|
(memory_bw / (HOPPER_BANDWIDTH_TBPS * 1024)) * 100,
|
|
)
|
|
|
|
|
|
def create_comparison_plot(
|
|
ratio, cuda_times, baseline_times, config_labels, strategy_name, id
|
|
):
|
|
"""Create a comparison plot for a specific generation strategy"""
|
|
fig, ax = plt.subplots(1, 1, figsize=(16, 6))
|
|
|
|
# Configure x-axis positions
|
|
x = np.arange(len(config_labels))
|
|
width = 0.35
|
|
|
|
# Execution Time plot (lower is better)
|
|
ax.bar(
|
|
x - width / 2, cuda_times, width, label="CUDA Kernel", alpha=0.8, color="blue"
|
|
)
|
|
ax.bar(
|
|
x + width / 2,
|
|
baseline_times,
|
|
width,
|
|
label="Baseline",
|
|
alpha=0.8,
|
|
color="orange",
|
|
)
|
|
|
|
# Add speedup labels over each bar pair
|
|
for i in range(len(x)):
|
|
speedup = ratio[i]
|
|
max_height = max(cuda_times[i], baseline_times[i])
|
|
ax.text(
|
|
x[i],
|
|
max_height + max_height * 0.02,
|
|
f"{speedup:.2f}x",
|
|
ha="center",
|
|
va="bottom",
|
|
fontweight="bold",
|
|
fontsize=9,
|
|
)
|
|
|
|
ax.set_xlabel("Configuration")
|
|
ax.set_ylabel("% Utilization")
|
|
ax.set_title(
|
|
f"Memory Bandwidth Utilization (%) - {strategy_name}\n(Higher is Better)"
|
|
)
|
|
ax.set_xticks(x)
|
|
ax.set_xticklabels(config_labels, rotation=45, ha="right")
|
|
ax.legend()
|
|
ax.grid(True, alpha=0.3)
|
|
|
|
plt.tight_layout()
|
|
return fig, ax
|
|
|
|
|
|
def create_combined_plot(all_results):
|
|
"""Create a combined plot with all strategies in one PNG"""
|
|
num_strategies = len(all_results)
|
|
fig, axes = plt.subplots(num_strategies, 1, figsize=(20, 6 * num_strategies))
|
|
|
|
if num_strategies == 1:
|
|
axes = [axes]
|
|
|
|
for idx, (
|
|
strategy_name,
|
|
ratio,
|
|
cuda_times,
|
|
baseline_times,
|
|
config_labels,
|
|
) in enumerate(all_results):
|
|
ax = axes[idx]
|
|
|
|
# Configure x-axis positions
|
|
x = np.arange(len(config_labels))
|
|
width = 0.35
|
|
|
|
# Execution Time plot (lower is better)
|
|
ax.bar(
|
|
x - width / 2,
|
|
cuda_times,
|
|
width,
|
|
label="CUDA Kernel",
|
|
alpha=0.8,
|
|
color="blue",
|
|
)
|
|
ax.bar(
|
|
x + width / 2,
|
|
baseline_times,
|
|
width,
|
|
label="Baseline",
|
|
alpha=0.8,
|
|
color="orange",
|
|
)
|
|
|
|
# Add speedup labels over each bar pair
|
|
for i in range(len(x)):
|
|
speedup = ratio[i]
|
|
max_height = max(cuda_times[i], baseline_times[i])
|
|
ax.text(
|
|
x[i],
|
|
max_height + max_height * 0.02,
|
|
f"{speedup:.2f}x",
|
|
ha="center",
|
|
va="bottom",
|
|
fontweight="bold",
|
|
fontsize=9,
|
|
)
|
|
|
|
ax.set_xlabel("Configuration")
|
|
ax.set_ylabel("% Utilization")
|
|
ax.set_title(
|
|
f"Memory Bandwidth Utilization (%) - {strategy_name}\n(Higher is Better)"
|
|
)
|
|
ax.set_xticks(x)
|
|
ax.set_xticklabels(config_labels, rotation=45, ha="right")
|
|
ax.legend()
|
|
ax.grid(True, alpha=0.3)
|
|
|
|
plt.tight_layout()
|
|
filename = "../../silu_bench/silu_benchmark_combined.png"
|
|
plt.savefig(filename, dpi=300, bbox_inches="tight")
|
|
plt.show()
|
|
|
|
return filename
|
|
|
|
|
|
outer_dim = 7168
|
|
configs = [
|
|
# DeepSeekV3 Configs
|
|
(8, 1024, 7168),
|
|
# DeepSeekV3 Configs
|
|
(32, 1024, 7168),
|
|
# DeepSeekV3 Configs
|
|
(256, 1024, 7168),
|
|
]
|
|
|
|
runs = 100
|
|
num_warmups = 20
|
|
|
|
strategy_descriptions = {
|
|
"uniform": "Uniform Random",
|
|
"max_t": "Even Assignment",
|
|
"first_t": "experts[0] = T, experts[1:] = 0",
|
|
}
|
|
|
|
print(f"GPU: {torch.cuda.get_device_name()}")
|
|
print(f"Testing strategies: {', '.join(strategies)}")
|
|
print(f"Configurations: {len(configs)} configs")
|
|
|
|
all_results = []
|
|
|
|
# Run benchmarks for each strategy
|
|
for id, strategy in enumerate(strategies):
|
|
print(f"\n{'=' * 60}")
|
|
print(f"Testing strategy: {strategy_descriptions[strategy]}")
|
|
print(f"{'=' * 60}")
|
|
|
|
# Collect benchmark data for both algorithms
|
|
config_labels = []
|
|
config_x_axis = []
|
|
all_cuda_results = []
|
|
all_baseline_results = []
|
|
all_ratios = []
|
|
|
|
for E, T, H in configs:
|
|
total_tokens_config = [8 * E, 16 * E, 32 * E, 64 * E, 128 * E, 256 * E]
|
|
config_x_axis.append(total_tokens_config)
|
|
|
|
cuda_results = []
|
|
baseline_results = []
|
|
ratios = []
|
|
|
|
for total_tokens in total_tokens_config:
|
|
config_label = f"E={E},T={T},H={H},TT={total_tokens}"
|
|
config_labels.append(config_label)
|
|
|
|
# CUDA kernel results
|
|
time_ms_cuda, gflops, gbps, perc = benchmark(
|
|
silu_mul_fp8_quant_deep_gemm_cuda,
|
|
E,
|
|
T,
|
|
H,
|
|
total_tokens,
|
|
runs=runs,
|
|
num_warmups=num_warmups,
|
|
gen_strategy=strategy,
|
|
)
|
|
cuda_results.append((time_ms_cuda, gflops, gbps, perc))
|
|
|
|
# Baseline results
|
|
time_ms_triton, gflops, gbps, perc = benchmark(
|
|
silu_mul_fp8_quant_deep_gemm_triton,
|
|
E,
|
|
T,
|
|
H,
|
|
total_tokens,
|
|
runs=runs,
|
|
num_warmups=num_warmups,
|
|
gen_strategy=strategy,
|
|
)
|
|
baseline_results.append((time_ms_triton, gflops, gbps, perc))
|
|
ratios.append(time_ms_triton / time_ms_cuda)
|
|
|
|
print(f"Completed: {config_label}")
|
|
all_cuda_results.append(cuda_results)
|
|
all_baseline_results.append(baseline_results)
|
|
all_ratios.append(ratios)
|
|
|
|
# Store results for combined plotting
|
|
all_results.append(
|
|
(
|
|
strategy_descriptions[strategy],
|
|
all_ratios,
|
|
all_cuda_results,
|
|
all_baseline_results,
|
|
config_labels,
|
|
config_x_axis,
|
|
)
|
|
)
|
|
|
|
# Print summary table for this strategy
|
|
print(f"\nSummary Table - {strategy_descriptions[strategy]}:")
|
|
print(f"{'Config':<20} {'CUDA Time(ms)':<12} {'Base Time(ms)':<12} {'Speedup':<8}")
|
|
print("-" * 60)
|
|
|
|
for i, (E, T, H) in enumerate(configs):
|
|
speedup = baseline_results[i][0] / cuda_results[i][0]
|
|
config_label = f"E={E:3d},T={T:4d},H={H:4d}"
|
|
print(
|
|
f"{config_label:<20} {cuda_results[i][0]:8.5f} "
|
|
f"{baseline_results[i][0]:8.5f} {speedup:6.2f}x"
|
|
)
|
|
|
|
|
|
def create_total_tokens_plot(all_results):
|
|
num_strategies = len(all_results)
|
|
num_configs = len(configs)
|
|
|
|
# Create side-by-side subplots: 2 columns for speedup and bandwidth percentage
|
|
fig, axs = plt.subplots(
|
|
num_strategies, num_configs * 2, figsize=(28, 6 * num_strategies)
|
|
)
|
|
|
|
# Add main title to the entire figure
|
|
fig.suptitle(
|
|
"Performance Analysis: Speedup vs Bandwidth Utilization (Triton & CUDA)",
|
|
fontsize=16,
|
|
fontweight="bold",
|
|
y=0.98,
|
|
)
|
|
|
|
# Handle single strategy case
|
|
if num_strategies == 1:
|
|
axs = axs.reshape(1, -1)
|
|
|
|
# Handle single config case
|
|
if num_configs == 1:
|
|
axs = axs.reshape(-1, 2)
|
|
|
|
for strategy_idx, result in enumerate(all_results):
|
|
(
|
|
strategy_name,
|
|
all_ratios,
|
|
all_cuda_results,
|
|
all_baseline_results,
|
|
config_labels,
|
|
config_x_axis,
|
|
) = result
|
|
|
|
for config_idx in range(num_configs):
|
|
# Speedup plot (left column)
|
|
ax_speedup = axs[strategy_idx, config_idx * 2]
|
|
# Bandwidth plot (right column)
|
|
ax_bandwidth = axs[strategy_idx, config_idx * 2 + 1]
|
|
|
|
E, T, H = configs[config_idx]
|
|
ratios = all_ratios[config_idx]
|
|
total_tokens_values = config_x_axis[config_idx]
|
|
|
|
# Extract CUDA and Triton bandwidth percentages
|
|
cuda_bandwidth_percentages = [
|
|
result[3] for result in all_cuda_results[config_idx]
|
|
]
|
|
triton_bandwidth_percentages = [
|
|
result[3] for result in all_baseline_results[config_idx]
|
|
]
|
|
|
|
# Plot speedup ratios vs total tokens (left plot)
|
|
ax_speedup.plot(
|
|
total_tokens_values, ratios, "bo-", linewidth=3, markersize=8
|
|
)
|
|
ax_speedup.set_title(
|
|
f"{strategy_name}\nSpeedup (CUDA/Triton)\nE={E}, T={T}, H={H}",
|
|
fontsize=12,
|
|
fontweight="bold",
|
|
)
|
|
ax_speedup.set_xlabel("Total Tokens", fontweight="bold", fontsize=11)
|
|
ax_speedup.set_ylabel("Speedup Ratio", fontweight="bold", fontsize=11)
|
|
ax_speedup.grid(True, alpha=0.3)
|
|
|
|
ax_bandwidth.plot(
|
|
total_tokens_values,
|
|
cuda_bandwidth_percentages,
|
|
"ro-",
|
|
linewidth=3,
|
|
markersize=8,
|
|
label="CUDA",
|
|
)
|
|
ax_bandwidth.plot(
|
|
total_tokens_values,
|
|
triton_bandwidth_percentages,
|
|
"go-",
|
|
linewidth=3,
|
|
markersize=8,
|
|
label="Triton",
|
|
)
|
|
ax_bandwidth.set_title(
|
|
f"{strategy_name}\nBandwidth Utilization (Hopper)\nE={E}, T={T}, H={H}",
|
|
fontsize=12,
|
|
fontweight="bold",
|
|
)
|
|
ax_bandwidth.set_xlabel("Total Tokens", fontweight="bold", fontsize=11)
|
|
ax_bandwidth.set_ylabel(
|
|
"% of Peak Bandwidth", fontweight="bold", fontsize=11
|
|
)
|
|
ax_bandwidth.legend(prop={"weight": "bold"})
|
|
ax_bandwidth.grid(True, alpha=0.3)
|
|
|
|
# Format x-axis labels for both plots
|
|
for ax in [ax_speedup, ax_bandwidth]:
|
|
ax.set_xticks(total_tokens_values)
|
|
ax.set_xticklabels(
|
|
[
|
|
f"{tt // 1000}K" if tt >= 1000 else str(tt)
|
|
for tt in total_tokens_values
|
|
],
|
|
fontweight="bold",
|
|
)
|
|
# Make tick labels bold
|
|
for label in ax.get_xticklabels() + ax.get_yticklabels():
|
|
label.set_fontweight("bold")
|
|
|
|
# Add value labels on speedup points
|
|
for x, y in zip(total_tokens_values, ratios):
|
|
ax_speedup.annotate(
|
|
f"{y:.2f}x",
|
|
(x, y),
|
|
textcoords="offset points",
|
|
xytext=(0, 12),
|
|
ha="center",
|
|
fontsize=10,
|
|
fontweight="bold",
|
|
bbox=dict(boxstyle="round,pad=0.3", facecolor="white", alpha=0.7),
|
|
)
|
|
|
|
# Add value labels on CUDA bandwidth points
|
|
for x, y in zip(total_tokens_values, cuda_bandwidth_percentages):
|
|
ax_bandwidth.annotate(
|
|
f"{y:.1f}%",
|
|
(x, y),
|
|
textcoords="offset points",
|
|
xytext=(0, 12),
|
|
ha="center",
|
|
fontsize=9,
|
|
fontweight="bold",
|
|
bbox=dict(boxstyle="round,pad=0.2", facecolor="red", alpha=0.3),
|
|
)
|
|
|
|
# Add value labels on Triton bandwidth points
|
|
for x, y in zip(total_tokens_values, triton_bandwidth_percentages):
|
|
ax_bandwidth.annotate(
|
|
f"{y:.1f}%",
|
|
(x, y),
|
|
textcoords="offset points",
|
|
xytext=(0, -15),
|
|
ha="center",
|
|
fontsize=9,
|
|
fontweight="bold",
|
|
bbox=dict(boxstyle="round,pad=0.2", facecolor="green", alpha=0.3),
|
|
)
|
|
|
|
plt.tight_layout()
|
|
plt.subplots_adjust(top=0.93) # Make room for main title
|
|
filename = "silu_benchmark_total_tokens.png"
|
|
plt.savefig(filename, dpi=300, bbox_inches="tight")
|
|
plt.show()
|
|
|
|
return filename
|
|
|
|
|
|
# Create combined plot with all strategies
|
|
combined_plot_filename = create_total_tokens_plot(all_results)
|
|
|
|
print(f"\n{'=' * 60}")
|
|
print("Benchmark Complete!")
|
|
print(f"Generated combined plot: {combined_plot_filename}")
|
|
print(f"{'=' * 60}")
|