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from typing import List
import argparse
import os
import tvm
from tvm import relax
from tvm.runtime import ShapeTuple
from tvm import rpc
from tvm.relax.testing.lib_comparator import LibCompareVMInstrument
import numpy as np
import torch
from transformers import AutoTokenizer
from mlc_llm import utils
class LibCompare(LibCompareVMInstrument):
def __init__(self, mod, device, time_eval, skip_rounds=0):
super().__init__(mod, device, True)
self.time_eval = time_eval
self.time_eval_results = {}
self.visited = set([])
self.skip_rounds = skip_rounds
self.atol = 1e-2
self.rtol = 1e-3
def skip_instrument(self, func, name, before_run, ret_val, *args):
print(f"run {name}")
if name.startswith("shape_func"):
return True
if self.counter < self.skip_rounds:
self.counter += 1
print(f"[{self.counter}] Skip validating {name}..")
return True
if name in self.visited:
if self.time_eval and name in self.time_eval_results:
record = self.time_eval_results[name]
self.time_eval_results[name] = (record[0], record[1] + 1)
return True
self.visited.add(name)
return False
def compare(
self,
name: str,
ref_args: List[tvm.nd.NDArray],
new_args: List[tvm.nd.NDArray],
ret_indices: List[int],
):
super().compare(name, ref_args, new_args, ret_indices)
if self.time_eval and name not in self.time_eval_results:
res = self.mod.time_evaluator(name, self.device)(*new_args)
self.time_eval_results[name] = (res.mean, 1)
print(f"Time-eval result {name} on {self.device}: {res}")
def print_as_table(sorted_list):
print(
"Name".ljust(50)
+ "Time (ms)".ljust(12)
+ "Count".ljust(8)
+ "Total time (ms)".ljust(18)
+ "Percentage (%)"
)
total_time = sum([record[1][0] * record[1][1] for record in sorted_list]) * 1000
for record in sorted_list:
time = record[1][0] * 1000
weighted_time = time * record[1][1]
percentage = weighted_time / total_time * 100
print(
record[0].ljust(50)
+ "{:.4f}".format(time).ljust(12)
+ str(record[1][1]).ljust(8)
+ "{:.4f}".format(weighted_time).ljust(18)
+ "{:.2f}".format(percentage)
)
print("Total time: {:.4f} ms".format(total_time))
print()
class TestState:
def __init__(self, args):
self.primary_device = tvm.device(args.primary_device)
ex = tvm.runtime.load_module(
os.path.join(
args.artifact_path,
f"{args.model}_{args.primary_device}_{args.dtype}.so",
)
)
self.vm = relax.VirtualMachine(ex, self.primary_device)
if args.cmp_device == "iphone":
lib_name = f"{args.model}_{args.cmp_device}_{args.dtype}.dylib"
local_lib_path = os.path.join(args.artifact_path, lib_name)
proxy_host = os.environ.get("TVM_RPC_PROXY_HOST", "127.0.0.1")
proxy_port = int(os.environ.get("TVM_RPC_PROXY_PORT", "9090"))
self.sess = rpc.connect(proxy_host, proxy_port, "iphone")
self.sess.upload(local_lib_path)
self.lib = self.sess.load_module(lib_name)
self.cmp_device = self.sess.metal()
elif args.cmp_device == "android":
lib_name = f"{args.model}_{args.cmp_device}_{args.dtype}.so"
local_lib_path = os.path.join(args.artifact_path, lib_name)
tracker_host = os.environ.get("TVM_TRACKER_HOST", "0.0.0.0")
tracker_port = int(os.environ.get("TVM_TRACKER_PORT", "9190"))
tracker = rpc.connect_tracker(tracker_host, tracker_port)
self.sess = tracker.request("android")
self.sess.upload(local_lib_path)
self.lib = self.sess.load_module(lib_name)
self.cmp_device = self.sess.cl(0)
else:
self.sess = None
self.lib = tvm.runtime.load_module(
os.path.join(
args.artifact_path,
f"{args.model}_{args.cmp_device}_{args.dtype}.so",
)
)
self.cmp_device = tvm.device(args.cmp_device)
self.const_params_dict = utils.load_params(
args.artifact_path, self.primary_device
)
self.cmp_instrument = LibCompare(
self.lib,
self.cmp_device,
time_eval=args.time_eval,
skip_rounds=args.skip_rounds,
)
self.vm.set_instrument(self.cmp_instrument)
def deploy_to_pipeline(args) -> None:
primary_device = tvm.device(args.primary_device)
const_params = utils.load_params(args.artifact_path, primary_device)
state = TestState(args)
tokenizer = AutoTokenizer.from_pretrained(args.model_path, trust_remote_code=True)
print("Tokenizing...")
inputs = tvm.nd.array(
tokenizer(args.prompt, return_tensors="pt").input_ids.to(torch.int32).numpy(),
primary_device,
)
first_sampled_token = tvm.nd.array(
np.array([[6234]]).astype("int32"), primary_device
)
seq_len_shape = tvm.runtime.ShapeTuple([inputs.shape[1]])
second_seq_len_shape = tvm.runtime.ShapeTuple([inputs.shape[1] + 1])
kv_caches = state.vm["create_kv_cache"]()
print("Running inference...")
print("======================= Starts Encoding =======================")
logits, kv_caches = state.vm["encoding"](
inputs, seq_len_shape, kv_caches, const_params
)
print_as_table(
sorted(
state.cmp_instrument.time_eval_results.items(),
key=lambda x: -(x[1][0] * x[1][1]),
)
)
state.cmp_instrument.time_eval_results.clear()
state.cmp_instrument.visited.clear()
print("======================= Starts Decoding =======================")
logits, kv_caches = state.vm["decoding"](
first_sampled_token, second_seq_len_shape, kv_caches, const_params
)
print_as_table(
sorted(
state.cmp_instrument.time_eval_results.items(),
key=lambda x: -(x[1][0] * x[1][1]),
)
)
state.cmp_instrument.time_eval_results.clear()
def _parse_args():
args = argparse.ArgumentParser()
args.add_argument("--artifact-path", type=str, default="dist")
args.add_argument("--primary-device", type=str, default="auto")
args.add_argument("--cmp-device", type=str, required=True)
args.add_argument("--prompt", type=str, default="The capital of Canada is")
args.add_argument("--model", type=str, default="vicuna-v1-7b")
args.add_argument(
"--dtype", type=str, choices=["float32", "float16"], default="float16"
)
args.add_argument("--time-eval", default=False, action="store_true")
args.add_argument("--skip-rounds", type=int, default=0)
parsed = args.parse_args()
parsed.model_path = os.path.join(parsed.artifact_path, "models", parsed.model)
parsed.artifact_path = os.path.join(
parsed.artifact_path, parsed.model, parsed.dtype
)
if parsed.primary_device == "auto":
if tvm.cuda().exist:
parsed.primary_device = "cuda"
elif tvm.metal().exist:
parsed.primary_device = "metal"
else:
raise ValueError("Cannot auto deduce device-name, please set it")
return parsed
if __name__ == "__main__":
args = _parse_args()
deploy_to_pipeline(args)
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