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from logging import Logger
import os
from threading import Event, Thread
from time import perf_counter, sleep
from typing import Optional
from benchmarks_entrypoint import MetricsRecorder
import gpustat
import psutil
import psycopg2
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, StaticCache
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
os.environ["TOKENIZERS_PARALLELISM"] = "1"
torch.set_float32_matmul_precision("high")
def collect_metrics(benchmark_id, continue_metric_collection, metrics_recorder):
p = psutil.Process(os.getpid())
while not continue_metric_collection.is_set():
with p.oneshot():
cpu_util = p.cpu_percent()
mem_megabytes = p.memory_info().rss / (1024 * 1024)
gpu_stats = gpustat.GPUStatCollection.new_query()
gpu_util = gpu_stats[0]["utilization.gpu"]
gpu_mem_megabytes = gpu_stats[0]["memory.used"]
metrics_recorder.collect_device_measurements(
benchmark_id, cpu_util, mem_megabytes, gpu_util, gpu_mem_megabytes
)
sleep(0.01)
def run_benchmark(
logger: Logger, repository: str, branch: str, commit_id: str, commit_msg: str, num_tokens_to_generate=100
):
continue_metric_collection = Event()
metrics_thread = None
model_id = "meta-llama/Llama-2-7b-hf"
metrics_recorder = MetricsRecorder(
psycopg2.connect("dbname=metrics"), logger, repository, branch, commit_id, commit_msg
)
try:
gpu_stats = gpustat.GPUStatCollection.new_query()
gpu_name = gpu_stats[0]["name"]
benchmark_id = metrics_recorder.initialise_benchmark({"gpu_name": gpu_name, "model_id": model_id})
logger.info(f"running benchmark #{benchmark_id} on {gpu_name} for {model_id}")
metrics_thread = Thread(
target=collect_metrics,
args=[benchmark_id, continue_metric_collection, metrics_recorder],
)
metrics_thread.start()
logger.info("started background thread to fetch device metrics")
os.environ["TOKENIZERS_PARALLELISM"] = "false" # silence warnings when compiling
device = "cuda"
logger.info("downloading weights")
# This is to avoid counting download in model load time measurement
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16)
gen_config = GenerationConfig(do_sample=False, top_p=1, temperature=1)
logger.info("loading model")
start = perf_counter()
model = AutoModelForCausalLM.from_pretrained(
model_id, torch_dtype=torch.float16, generation_config=gen_config
).eval()
model.to(device)
torch.cuda.synchronize()
end = perf_counter()
model_load_time = end - start
logger.info(f"loaded model in: {model_load_time}s")
tokenizer = AutoTokenizer.from_pretrained(model_id)
prompt = "Why dogs are so cute?"
inputs = tokenizer(prompt, return_tensors="pt").to(device)
# Specify the max length (including both the prompt and the response)
# When calling `generate` with `cache_implementation="static" later, this is also used to create a `StaticCache` object
# with sequence length = `max_length`. The longer the more you will re-use it
seq_length = inputs["input_ids"].shape[1]
model.generation_config.max_length = seq_length + num_tokens_to_generate
batch_size = inputs["input_ids"].shape[0]
# Copied from the gpt-fast repo
def multinomial_sample_one_no_sync(probs_sort): # Does multinomial sampling without a cuda synchronization
q = torch.empty_like(probs_sort).exponential_(1)
return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)
def logits_to_probs(logits, temperature: float = 1.0, top_k: Optional[int] = None):
logits = logits / max(temperature, 1e-5)
if top_k is not None:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
pivot = v.select(-1, -1).unsqueeze(-1)
logits = torch.where(logits < pivot, -float("Inf"), logits)
probs = torch.nn.functional.softmax(logits, dim=-1)
return probs
def sample(logits, temperature: float = 1.0, top_k: Optional[int] = None):
probs = logits_to_probs(logits[:, -1], temperature, top_k)
idx_next = multinomial_sample_one_no_sync(probs)
return idx_next, probs
def decode_one_token(model, cur_token, cache_position, past_key_values):
logits = model(
cur_token,
cache_position=cache_position,
past_key_values=past_key_values,
return_dict=False,
use_cache=True,
)[0]
new_token = sample(logits, temperature=0.6, top_k=5)[0]
return new_token
#########
# Eager #
#########
with torch.no_grad():
past_key_values = StaticCache(
model.config,
max_batch_size=batch_size,
device=device,
dtype=torch.float16,
max_cache_len=seq_length + num_tokens_to_generate,
)
cache_position = torch.arange(seq_length, device=device)
start = perf_counter()
model(
**inputs,
cache_position=cache_position,
past_key_values=past_key_values,
return_dict=False,
use_cache=True,
)
end = perf_counter()
first_eager_fwd_pass_time = end - start
logger.info(f"completed first eager fwd pass in: {first_eager_fwd_pass_time}s")
start = perf_counter()
output = model.generate(**inputs, do_sample=False)
end = perf_counter()
first_eager_generate_time = end - start
logger.info(f"completed first eager generation in: {first_eager_generate_time}s")
logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
past_key_values = StaticCache(
model.config,
max_batch_size=batch_size,
device=device,
dtype=torch.float16,
max_cache_len=seq_length + num_tokens_to_generate,
)
cache_position = torch.arange(seq_length, device=device)
start = perf_counter()
model(
**inputs,
cache_position=cache_position,
past_key_values=past_key_values,
return_dict=False,
use_cache=True,
)
end = perf_counter()
second_eager_fwd_pass_time = end - start
logger.info(f"completed second eager fwd pass in: {second_eager_fwd_pass_time}s")
start = perf_counter()
model.generate(**inputs, do_sample=False)
end = perf_counter()
second_eager_generate_time = end - start
logger.info(f"completed second eager generation in: {second_eager_generate_time}s")
logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
torch.compiler.reset()
################
# Forward pass #
################
# `torch.compile(model, ...)` is not recommended as you compile callbacks
# and full generate. We recommend compiling only the forward for now.
# "reduce-overhead" will use cudagraphs.
generated_ids = torch.zeros(
(batch_size, num_tokens_to_generate + seq_length), dtype=torch.int, device=device
)
generated_ids[:, :seq_length] = inputs["input_ids"]
decode_one_token = torch.compile(decode_one_token, mode="reduce-overhead", fullgraph=True)
# model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)
# TODO use decode_one_token(model, input_id.clone(), cache_position) for verification
past_key_values = StaticCache(
model.config,
max_batch_size=batch_size,
device=device,
dtype=torch.float16,
max_cache_len=seq_length + num_tokens_to_generate + 10,
)
cache_position = torch.arange(seq_length, device=device)
all_generated_tokens = []
### First compile, prefill
start = perf_counter()
next_token = decode_one_token(
model, inputs["input_ids"], cache_position=cache_position, past_key_values=past_key_values
)
torch.cuda.synchronize()
end = perf_counter()
time_to_first_token = end - start
logger.info(f"completed first compile generation in: {time_to_first_token}s")
cache_position += 1
all_generated_tokens += next_token.tolist()
cache_position = torch.tensor([seq_length], device=device)
### First compile, decoding
start = perf_counter()
next_token = decode_one_token(
model, next_token.clone(), cache_position=cache_position, past_key_values=past_key_values
)
torch.cuda.synchronize()
end = perf_counter()
time_to_second_token = end - start
logger.info(f"completed second compile generation in: {time_to_second_token}s")
cache_position += 1
all_generated_tokens += next_token.tolist()
### Second compile, decoding
start = perf_counter()
next_token = decode_one_token(
model, next_token.clone(), cache_position=cache_position, past_key_values=past_key_values
)
torch.cuda.synchronize()
end = perf_counter()
time_to_third_token = end - start
logger.info(f"completed third compile forward in: {time_to_third_token}s")
cache_position += 1
all_generated_tokens += next_token.tolist()
### Using cuda graphs decoding
start = perf_counter()
for _ in range(1, num_tokens_to_generate):
all_generated_tokens += next_token.tolist()
next_token = decode_one_token(
model, next_token.clone(), cache_position=cache_position, past_key_values=past_key_values
)
cache_position += 1
torch.cuda.synchronize()
end = perf_counter()
mean_time_to_next_token = (end - start) / num_tokens_to_generate
logger.info(f"completed next compile generation in: {mean_time_to_next_token}s")
logger.info(f"generated: {tokenizer.batch_decode(all_generated_tokens)}")
####################
# Generate compile #
####################
torch.compiler.reset()
# we will not compile full generate as it' s to intensive, tho we measure full forward!
past_key_values = StaticCache(
model.config,
max_batch_size=batch_size,
device=device,
dtype=torch.float16,
max_cache_len=seq_length + 128,
)
# 1st call
start = perf_counter()
output = model.generate(**inputs, past_key_values=past_key_values)
torch.cuda.synchronize()
end = perf_counter()
first_compile_generate_time = end - start
logger.info(f"completed first compile generation in: {first_compile_generate_time}s")
logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
past_key_values = StaticCache(
model.config,
max_batch_size=batch_size,
device=device,
dtype=torch.float16,
max_cache_len=seq_length + 128,
)
# 2nd call
start = perf_counter()
output = model.generate(**inputs, past_key_values=past_key_values)
torch.cuda.synchronize()
end = perf_counter()
second_compile_generate_time = end - start
logger.info(f"completed second compile generation in: {second_compile_generate_time}s")
logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
past_key_values = StaticCache(
model.config,
max_batch_size=batch_size,
device=device,
dtype=torch.float16,
max_cache_len=seq_length + 128,
)
# 3rd call
start = perf_counter()
output = model.generate(**inputs, past_key_values=past_key_values)
end = perf_counter()
third_compile_generate_time = end - start
logger.info(f"completed third compile generation in: {third_compile_generate_time}s")
logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
past_key_values = StaticCache(
model.config,
max_batch_size=batch_size,
device=device,
dtype=torch.float16,
max_cache_len=seq_length + 128,
)
# 4th call
start = perf_counter()
output = model.generate(**inputs, past_key_values=past_key_values)
end = perf_counter()
fourth_compile_generate_time = end - start
logger.info(f"completed fourth compile generation in: {fourth_compile_generate_time}s")
logger.info(f"generated: {tokenizer.batch_decode(output.cpu().tolist())}")
metrics_recorder.collect_model_measurements(
benchmark_id,
{
"model_load_time": model_load_time,
"first_eager_forward_pass_time_secs": first_eager_fwd_pass_time,
"second_eager_forward_pass_time_secs": second_eager_fwd_pass_time,
"first_eager_generate_time_secs": first_eager_generate_time,
"second_eager_generate_time_secs": second_eager_generate_time,
"time_to_first_token_secs": time_to_first_token,
"time_to_second_token_secs": time_to_second_token,
"time_to_third_token_secs": time_to_third_token,
"time_to_next_token_mean_secs": mean_time_to_next_token,
"first_compile_generate_time_secs": first_compile_generate_time,
"second_compile_generate_time_secs": second_compile_generate_time,
"third_compile_generate_time_secs": third_compile_generate_time,
"fourth_compile_generate_time_secs": fourth_compile_generate_time,
},
)
except Exception as e:
logger.error(f"Caught exception: {e}")
continue_metric_collection.set()
if metrics_thread is not None:
metrics_thread.join()
metrics_recorder.close()
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