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req/response latency breakdown #1

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36 changes: 31 additions & 5 deletions examples/llm-api/llm_inference_async.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,35 +4,61 @@
import asyncio

from tensorrt_llm import LLM, SamplingParams
from tensorrt_llm._tmp_utils import (analyze_average_timestamps,
dump_timestamps_to_json,
print_enqueue_statistics)
from tensorrt_llm.llmapi import KvCacheConfig


def main():
# model could accept HF model name or a path to local HF model.
llm = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0")
kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.8,
max_tokens=4096,
enable_block_reuse=True)

llm = LLM(
#model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
model="/scratch/llm-models/llama-3.2-models/Llama-3.2-3B-Instruct-FP8",
# tensor_parallel_size=2
max_seq_len=1024,
kv_cache_config=kv_cache_config
# max_batch_size=1,
)

# Sample prompts.
prompts = [
"Hello, my name is",
"The capital of France is",
"The future of AI is",
]
] * 1000

# Create a sampling params.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

all_timestamps = []

# Async based on Python coroutines
async def task(prompt: str):
output = await llm.generate_async(prompt, sampling_params)
print(
f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}"
)

if output.outputs[0].timestamps:
all_timestamps.append(output.outputs[0].timestamps)

async def main():
tasks = [task(prompt) for prompt in prompts]
await asyncio.gather(*tasks)

asyncio.run(main())

analyze_average_timestamps(all_timestamps)
dump_timestamps_to_json(all_timestamps, "timestamps_output.json")

print(
f"executor type = {type(llm._executor)}, has enqueue_timings = {hasattr(llm._executor, 'enqueue_timings')}"
)
if hasattr(llm._executor, 'enqueue_timings'):
print_enqueue_statistics(llm._executor.enqueue_timings)

# Got output like follows:
# Prompt: 'Hello, my name is', Generated text: '\n\nJane Smith. I am a student pursuing my degree in Computer Science at [university]. I enjoy learning new things, especially technology and programming'
# Prompt: 'The capital of France is', Generated text: 'Paris.'
Expand Down
48 changes: 41 additions & 7 deletions examples/ray_orchestrator/llm_inference_async_ray.py
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Original file line number Diff line number Diff line change
@@ -1,22 +1,29 @@
# Generate text asynchronously with Ray orchestrator.
import asyncio

import ray

from tensorrt_llm import LLM, SamplingParams
from tensorrt_llm._tmp_utils import (analyze_average_timestamps,
dump_timestamps_to_json,
print_enqueue_statistics,
print_fetch_statistics)
from tensorrt_llm.llmapi import KvCacheConfig


def main():
# Configure KV cache memory usage fraction.
kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.5,
kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.8,
max_tokens=4096,
enable_block_reuse=True)

# model could accept HF model name or a path to local HF model.
llm = LLM(
model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
model="/scratch/llm-models/llama-3.2-models/Llama-3.2-3B-Instruct-FP8",
# model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
kv_cache_config=kv_cache_config,
max_seq_len=1024,
max_batch_size=1,
# max_batch_size=1,
orchestrator_type="ray", # Enable Ray orchestrator
# Enable 2-way tensor parallelism
# tensor_parallel_size=2
Expand All @@ -27,24 +34,51 @@ def main():
"Hello, my name is",
"The capital of France is",
"The future of AI is",
]
] * 1000

#* 100

# Create a sampling params.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

# Collect all timestamps
all_timestamps = []

# Async based on Python coroutines
async def task(prompt: str):
output = await llm.generate_async(prompt, sampling_params)
print(
f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}"
)

if output.outputs[0].timestamps:
all_timestamps.append(output.outputs[0].timestamps)

# print(
# f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}"
# )

async def main():
tasks = [task(prompt) for prompt in prompts]
await asyncio.gather(*tasks)

asyncio.run(main())

analyze_average_timestamps(all_timestamps)
dump_timestamps_to_json(all_timestamps, "timestamps_output.json")

if hasattr(llm._executor, 'enqueue_timings'):
print_enqueue_statistics(llm._executor.enqueue_timings)

if hasattr(llm._executor, 'workers'):
for i, worker in enumerate(llm._executor.workers):
try:
stats = worker.call_worker_method.remote('get_fetch_statistics')
result = ray.get(stats)
if result:
print_fetch_statistics(result['num_fetched_requests'],
result['fetch_call_count'],
rank=result['rank'])
except Exception as e:
print(f"Could not get fetch statistics from worker {i}: {e}")

# Got output like follows:
# Prompt: 'Hello, my name is', Generated text: '\n\nJane Smith. I am a student pursuing my degree in Computer Science at [university]. I enjoy learning new things, especially technology and programming'
# Prompt: 'The capital of France is', Generated text: 'Paris.'
Expand Down
211 changes: 211 additions & 0 deletions tensorrt_llm/_tmp_utils.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,211 @@
"""
Temporary utilities for timestamp analysis and Ray vs MPI latency comparison.
"""
import json
import os
from collections import Counter

from tensorrt_llm._utils import mpi_disabled


def is_timestamp_debug_enabled():
return os.environ.get('TIMESTAMP_DEBUG', '0') == '1'


def calculate_latencies(timestamps):
"""
Calculate latency metrics from a single set of timestamps.
Returns a dict of latencies in milliseconds, or None if timestamps missing.
"""
if not timestamps:
return None

latencies = {}

latencies['submit_request_to_enqueue'] = (
timestamps['worker_enqueue_request'] -
timestamps['executor_submit_request']) * 1000

# only for the fetch
latencies['queue_wait_time'] = (timestamps['request_fetched'] -
timestamps['request_queued']) * 1000

latencies['num_iterations'] = timestamps['num_iterations']
latencies['scheduling_wait_time'] = timestamps['scheduling_wait_time']
latencies['pre_forward_overhead'] = timestamps['pre_forward_overhead']
latencies['forward_step_time'] = timestamps['forward_step_time']
latencies['post_processing_time'] = timestamps['post_processing_time']

latencies['execution_time'] = (timestamps['response_created'] -
timestamps['request_fetched']) * 1000

latencies['response_handling'] = (timestamps['response_enqueued'] -
timestamps['response_created']) * 1000

latencies['enqueue_response_to_handle'] = (
timestamps['handle_response'] - timestamps['response_enqueued']) * 1000

latencies['total_e2e'] = (timestamps['handle_response'] -
timestamps['executor_submit_request']) * 1000

latencies['communication_overhead'] = (
(timestamps['worker_enqueue_request'] -
timestamps['executor_submit_request']) +
(timestamps['handle_response'] -
timestamps['response_enqueued'])) * 1000

return latencies


def analyze_average_timestamps(all_timestamps):
if not is_timestamp_debug_enabled():
return

if not all_timestamps:
print("No timestamps available")
return

mode = "[Ray]" if mpi_disabled() else "[MPI]"
# Calculate latencies for each request
all_latencies = []
for ts in all_timestamps:
latencies = calculate_latencies(ts)
if latencies:
all_latencies.append(latencies)

if not all_latencies:
print("No valid latencies calculated")
return

# Calculate averages
print(
f"\n=== [{mode}] Latency Breakdown (milliseconds) - Average over {len(all_timestamps)} request ==="
)

# Print first 20 submit_request_to_enqueue values
submit_to_enqueue_values = [
lat['submit_request_to_enqueue'] for lat in all_latencies
if 'submit_request_to_enqueue' in lat
]
if submit_to_enqueue_values:
first_20 = ', '.join(
[f"{x:.2f}" for x in submit_to_enqueue_values[:20]])
print(f" Submit to enqueue (first 20, ms): {first_20}", flush=True)
print(flush=True)

metrics = [
('submit_request_to_enqueue', 'Submit to enqueue'),
('queue_wait_time', 'Request Queue wait (1st fetch)'),
('execution_time', 'Time in executor loop (sum of all iterations)'),
('scheduling_wait_time', ' ├─ Scheduling wait'),
('pre_forward_overhead', ' ├─ Pre-forward overhead'),
('forward_step_time', ' ├─ Forward step'),
('post_processing_time', ' └─ Post-processing'),
('response_handling', 'Response handling (once)'),
('enqueue_response_to_handle', 'Enqueue to handle (once)'),
# ('num_iterations', 'Avg iterations per request'),
# ('total_e2e', 'Total E2E latency'),
# ('communication_overhead', 'Total communication overhead'),
]

for metric_key, metric_name in metrics:
if metric_key == 'num_iterations':
print("")
if metric_key == 'total_e2e':
print(" " + "-" * 68)

values = [lat[metric_key] for lat in all_latencies if metric_key in lat]
if values:
avg = sum(values) / len(values)
min_val = min(values)
max_val = max(values)
variance = sum((x - avg)**2 for x in values) / len(values)

if metric_key == 'num_iterations':
print(
f" {metric_name:48s}: {avg:8.1f} (min: {min_val:8.1f}, max: {max_val:9.1f})"
)
else:
print(
f" {metric_name:48s}: {avg:8.3f} ms (min: {min_val:8.3f}, max: {max_val:9.3f}, var: {variance:10.3f})"
)

print("=" * 70)


def dump_timestamps_to_json(all_timestamps,
output_file="timestamps_output.json"):
if not is_timestamp_debug_enabled():
return

if not all_timestamps:
print("No timestamps to dump")
return

print(
f"\nDumping {len(all_timestamps)} timestamp records to {output_file}..."
)
with open(output_file, 'w') as f:
json.dump(all_timestamps, f, indent=2)
print(f"Timestamps saved to {output_file}")


def print_fetch_statistics(num_fetched_requests, fetch_call_count, rank=None):
if not is_timestamp_debug_enabled():
return

if not num_fetched_requests:
return

rank_str = f"[Rank {rank}]" if rank is not None else ""
mode = "[Ray]" if mpi_disabled() else "[MPI]"

print(f"\n=== {mode}{rank_str} Fetch Request Statistics ===")
print(f" Total fetch calls: {fetch_call_count}")

size_distribution = Counter(num_fetched_requests)
print(f"\n Fetch Size Distribution:")
for size in sorted(size_distribution.keys()):
count = size_distribution[size]
percentage = (count / len(num_fetched_requests)) * 100
print(f" {size:3d} requests: {count:5d} times ({percentage:5.1f}%)")

print(f"\n Num fetched requests (all iterations): {num_fetched_requests}")

print("=" * 70)


def print_enqueue_statistics(enqueue_timings):
if not is_timestamp_debug_enabled():
return

if not enqueue_timings:
return

mode = "[Ray]" if mpi_disabled() else "[MPI]"
num_requests = len(enqueue_timings)

print(
f"\n=== {mode} Enqueue Request Timing Statistics ({num_requests} requests) ==="
)
first_20_enqueue = ', '.join([f"{x:.2f}" for x in enqueue_timings[:20]])
print(f" Direct enqueue (first 20, ms): {first_20_enqueue}", flush=True)

avg = sum(enqueue_timings) / num_requests
min_val = min(enqueue_timings)
max_val = max(enqueue_timings)

# Calculate percentiles
sorted_timings = sorted(enqueue_timings)
p10 = sorted_timings[int(num_requests *
0.1)] if num_requests > 1 else sorted_timings[0]
p50 = sorted_timings[num_requests // 2]
p90 = sorted_timings[int(num_requests * 0.9)]

print(f" Avg: {avg:.2f} ms")
print(f" Min: {min_val:.2f} ms")
print(f" Max: {max_val:.2f} ms")
print(f" P10: {p10:.2f} ms")
print(f" P50: {p50:.2f} ms")
print(f" P90: {p90:.2f} ms")
print("=" * 70)
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