228 lines
9.0 KiB
Python
228 lines
9.0 KiB
Python
import torch
|
|
import time
|
|
import math
|
|
from einops import rearrange
|
|
from functools import partial
|
|
from torch.nn.attention.flex_attention import flex_attention
|
|
from csrc.sliding_tile_attention.test.sba import get_sliding_block_attention_mask
|
|
import tk_4090_cuda as tk
|
|
from fastvideo.models.flash_attn_no_pad import flash_attn_no_pad
|
|
import torch.nn.functional as F
|
|
# Import benchmark utilities from flash_attn
|
|
from flash_attn.utils.benchmark import benchmark_forward
|
|
|
|
def tile(x, sp_size):
|
|
x = rearrange(x, "b (sp t h w) head d -> b (t sp h w) head d", sp=sp_size, t=12 // sp_size, h=48, w=80)
|
|
return rearrange(x,
|
|
"b (n_t ts_t n_h ts_h n_w ts_w) h d -> b (n_t n_h n_w ts_t ts_h ts_w) h d",
|
|
n_t=2,
|
|
n_h=6,
|
|
n_w=10,
|
|
ts_t=6,
|
|
ts_h=8,
|
|
ts_w=8)
|
|
|
|
def flops_attention_forward(batch, seqlen, headdim, nheads):
|
|
"""
|
|
Calculate FLOPs for attention forward pass operation.
|
|
"""
|
|
return 4 * batch * seqlen**2 * nheads * headdim
|
|
|
|
def efficiency(flop, time):
|
|
"""
|
|
Calculate efficiency in TFLOPs/s
|
|
"""
|
|
return (flop / time / 10**12) if not math.isnan(time) and time > 0 else 0.
|
|
|
|
def benchmark_tk_attention(func, q, k, v, o, text_length, repeats=100, desc=""):
|
|
time_f, m = benchmark_forward(func, q, k, v, o, text_length, repeats=repeats, desc=desc, verbose=False)
|
|
return m.mean
|
|
|
|
def benchmark_baseline_attention(func, qkv, attn_mask, causal, dropout_p, softmax_sacle, repeats=100, desc=""):
|
|
time_f, m = benchmark_forward(func, qkv, attn_mask, causal, dropout_p, softmax_sacle, repeats=repeats, desc=desc, verbose=False)
|
|
return m.mean
|
|
|
|
|
|
def benchmark_attention(func, q, k, v, repeats=100, desc=""):
|
|
"""
|
|
Benchmark forward pass of attention function
|
|
"""
|
|
time_f, m = benchmark_forward(func, q, k, v, repeats=repeats, desc=desc, verbose=False)
|
|
return m.mean
|
|
|
|
def main():
|
|
debug = False
|
|
|
|
# Load tensors
|
|
print("Loading tensors...")
|
|
if debug:
|
|
debug_seq_len = 46080
|
|
debug_batch_size = 1
|
|
debug_nheads = 24
|
|
debug_headdim = 128
|
|
q = torch.randn((debug_batch_size, debug_seq_len+256, debug_nheads, debug_headdim), device="cuda", dtype=torch.bfloat16)
|
|
k = torch.randn((debug_batch_size, debug_seq_len+256, debug_nheads, debug_headdim), device="cuda", dtype=torch.bfloat16)
|
|
v = torch.randn((debug_batch_size, debug_seq_len+256, debug_nheads, debug_headdim), device="cuda", dtype=torch.bfloat16)
|
|
else:
|
|
q = torch.load("query.pt").to("cuda")
|
|
k = torch.load("key.pt").to("cuda")
|
|
v = torch.load("value.pt").to("cuda")
|
|
text_mask = torch.load("text_mask.pt")
|
|
text_length = text_mask.sum()
|
|
|
|
batch_size = q.shape[0]
|
|
nheads = q.shape[2]
|
|
seqlen = q.shape[1]
|
|
headdim = q.shape[3]
|
|
warmup = 50
|
|
repeats = 100 # Number of repeats for reliable timing
|
|
|
|
# Baseline measurement (standard flex attention without masking)
|
|
baseline_attn = flash_attn_no_pad
|
|
|
|
# Perform warmup for baseline
|
|
print("\nWarming up baseline...")
|
|
if debug:
|
|
attn_mask = F.pad(text_mask, (debug_seq_len, 0), value=True)
|
|
else:
|
|
attn_mask = F.pad(text_mask, (seqlen - 256, 0), value=True)
|
|
for _ in range(warmup):
|
|
flash_attn_no_pad(torch.stack([q, k, v], dim=2), attn_mask, causal=False, dropout_p=0.0, softmax_scale=None)
|
|
|
|
# Benchmark baseline
|
|
print("\nBenchmarking baseline flex attention...")
|
|
torch.cuda.synchronize()
|
|
baseline_time = benchmark_baseline_attention(baseline_attn, torch.stack([q, k, v], dim=2), attn_mask, causal=False, dropout_p=0.0, softmax_sacle=None, repeats=repeats, desc="Baseline")
|
|
|
|
# benchmark tk_4090_cuda
|
|
# tk.attention_fwd_4090(query, key, value, result, text_length)
|
|
print("Benchmarking tk_4090_cuda...")
|
|
result = torch.empty_like(q)
|
|
for _ in range(warmup):
|
|
tk.attention_fwd_4090(q, k, v, result, text_length)
|
|
torch.cuda.synchronize()
|
|
|
|
tk_time = benchmark_tk_attention(tk.attention_fwd_4090, q, k, v, result, text_length, repeats=repeats, desc="tk_4090_cuda")
|
|
|
|
|
|
# Prepare tensors
|
|
strategies = []
|
|
if not debug:
|
|
query, encoder_query = q.split_with_sizes((q.shape[1] - 256, 256), dim=1)
|
|
key, encoder_key = k.split_with_sizes((k.shape[1] - 256, 256), dim=1)
|
|
value, encoder_value = v.split_with_sizes((v.shape[1] - 256, 256), dim=1)
|
|
|
|
q = torch.cat([tile(query, 1), encoder_query], dim=1).transpose(1, 2)
|
|
k = torch.cat([tile(key, 1), encoder_key], dim=1).transpose(1, 2)
|
|
v = torch.cat([tile(value, 1), encoder_value], dim=1).transpose(1, 2)
|
|
|
|
# Strategy definitions
|
|
strategies = [
|
|
(2, 6, 1), # (t, h, w)
|
|
(1, 6, 10),
|
|
(2, 3, 3),
|
|
(2, 6, 10),
|
|
(2, 1, 10),
|
|
(2, 3, 5)
|
|
]
|
|
|
|
# Strategy names for better reporting
|
|
strategy_names = [f"Strategy_{t}_{h}_{w}" for t, h, w in strategies]
|
|
|
|
print(f"Text length: {text_length}")
|
|
|
|
# flex full baseline
|
|
print("Benchmarking flex full baseline...")
|
|
flex_baseline = torch.compile(
|
|
partial(flex_attention, block_mask=None),
|
|
)
|
|
|
|
for _ in range(warmup):
|
|
flex_baseline(q, k, v)
|
|
torch.cuda.synchronize()
|
|
flex_baseline_time = benchmark_attention(flex_baseline, q, k, v, repeats=repeats, desc="FA2 Baseline")
|
|
|
|
|
|
# Create attention processors for each strategy
|
|
attn_processors = []
|
|
for strategy in strategies:
|
|
mask = get_sliding_block_attention_mask(strategy, (6, 8, 8), (12, 48, 80), text_length, "cuda")
|
|
attn_processor = torch.compile(
|
|
partial(flex_attention, block_mask=mask),
|
|
)
|
|
attn_processors.append(attn_processor)
|
|
|
|
# Setup benchmarking parameters
|
|
|
|
print(f"Tensor shapes - Q/K/V: {q.shape}")
|
|
print(f"Benchmarking with: batch_size={batch_size}, nheads={nheads}, seqlen={seqlen}, headdim={headdim}")
|
|
|
|
# Calculate theoretical FLOPS for forward pass
|
|
flops = flops_attention_forward(batch_size, seqlen, headdim, nheads)
|
|
print(f"Theoretical FLOPs: {flops / 10**12:.2f} TFLOPs")
|
|
|
|
tk_efficiency = efficiency(flops, tk_time)
|
|
print(f"tk_4090_cuda forward: {tk_time:.6f}s, {tk_efficiency:.2f} TFLOPs/s")
|
|
|
|
baseline_efficiency = efficiency(flops, baseline_time)
|
|
|
|
print(f"Baseline forward: {baseline_time:.6f}s, {baseline_efficiency:.2f} TFLOPs/s")
|
|
|
|
# Create result containers
|
|
times = {}
|
|
speeds = {}
|
|
speedups = {}
|
|
|
|
# Benchmark each attention strategy
|
|
print("\nBenchmarking attention strategies...")
|
|
if not debug:
|
|
for i, (processor, strategy, name) in enumerate(zip(attn_processors, strategies, strategy_names)):
|
|
t, h, w = strategy
|
|
|
|
# Warmup
|
|
print(f"\nWarming up {name}...")
|
|
for _ in range(warmup):
|
|
processor(q, k, v)
|
|
|
|
# Benchmark
|
|
print(f"Benchmarking {name} (t={t}, h={h}, w={w})...")
|
|
torch.cuda.synchronize()
|
|
fwd_time = benchmark_attention(processor, q, k, v, repeats=repeats, desc=name)
|
|
|
|
# Save results
|
|
times[name] = fwd_time
|
|
speeds[name] = efficiency(flops, fwd_time)
|
|
|
|
# Calculate theoretical and actual speedup
|
|
theoretical_speedup = (2*6*10)/(t*h*w)
|
|
actual_speedup = baseline_time / fwd_time
|
|
speedups[name] = (theoretical_speedup, actual_speedup)
|
|
|
|
# Print results
|
|
print(f"Strategy: {name} (t={t}, h={h}, w={w})")
|
|
print(f"Forward: {fwd_time:.6f}s, {speeds[name]:.2f} TFLOPs/s")
|
|
print(f"Theoretical speedup: {theoretical_speedup:.2f}x")
|
|
print(f"Actual speedup: {actual_speedup:.2f}x")
|
|
print(f"Efficiency ratio (actual/theoretical): {actual_speedup/theoretical_speedup:.2f}")
|
|
|
|
# Print summary table
|
|
print("\n" + "="*80)
|
|
print("SUMMARY OF RESULTS")
|
|
print("="*80)
|
|
print(f"{'Strategy':<15} {'Time (ms)':<15} {'TFLOPs/s':<10} {'Actual Speedup':<15} {'Theoretical':<15} {'Efficiency':<10}")
|
|
print("-"*80)
|
|
|
|
# Baseline row
|
|
print(f"{'FA2 Baseline':<15} {baseline_time*1000:13.2f} {baseline_efficiency:10.2f} {1.00:15.2f} {1.00:15.2f} {1.00:10.2f}")
|
|
if not debug:
|
|
print(f"{'FA2 Flex':<15} {flex_baseline_time*1000:13.2f} {efficiency(flops, flex_baseline_time):10.2f} {baseline_time/flex_baseline_time:15.2f} {1.00:15.2f} {baseline_time/flex_baseline_time:10.2f}")
|
|
print(f"{'Tk_4090_cuda':<15} {tk_time*1000:13.2f} {tk_efficiency:10.2f} {baseline_time/tk_time:15.2f} {1.00:15.2f} {baseline_time/tk_time:10.2f}")
|
|
|
|
# Strategy rows
|
|
if not debug:
|
|
for name in strategy_names:
|
|
theoretical, actual = speedups[name]
|
|
print(f"{name:<15} {times[name]*1000:13.2f} {speeds[name]:10.2f} {actual:15.2f} {theoretical:15.2f} {actual/theoretical:10.2f}")
|
|
|
|
if __name__ == "__main__":
|
|
main() |