[Inference]ADD Bench Chatglm2 script

examples/inference/_utils.py

@@ -0,0 +1,19 @@
+def print_perf_stats(latency_set, config, bs, warmup=3):
+    # trim warmup queries
+    latency_set = list(latency_set)
+    latency_set = latency_set[warmup:]
+    count = len(latency_set)
+
+    if count > 0:
+        latency_set.sort()
+        avg = sum(latency_set) / count
+        num_layers = (
+            getattr(config, "num_layers") if hasattr(config, "num_layers") else getattr(config, "num_hidden_layers")
+        )
+        num_parameters = num_layers * config.hidden_size * config.hidden_size * 12
+        num_bytes = 2  # float16
+
+        print("Avg Per Token Latency: {0:8.2f} ms".format(avg * 1000))
+        print("Avg BW: {0:8.2f} GB/s".format(1 / avg * num_parameters * num_bytes / 1e9))
+        print("Avg flops: {0:8.2f} TFlops/s".format(1 / avg * num_parameters * num_bytes * bs / 1e12))
+        print("Avg Throughput: tokens/s: {}".format((1000 / (avg * 1000)) * bs))

examples/inference/bench_bloom.py

@@ -3,6 +3,7 @@
 import time
 
 import torch
+from _utils import print_perf_stats
 from transformers import BloomForCausalLM, BloomTokenizerFast
 
 import colossalai
@@ -14,25 +15,6 @@
 os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
 
 
-def print_perf_stats(latency_set, config, bs, warmup=3):
-    # trim warmup queries
-    latency_set = list(latency_set)
-    latency_set = latency_set[warmup:]
-    count = len(latency_set)
-
-    if count > 0:
-        latency_set.sort()
-        avg = sum(latency_set) / count
-        num_layers = getattr(config, "num_layers", config.num_hidden_layers)
-        num_parameters = num_layers * config.hidden_size * config.hidden_size * 12
-        num_bytes = 2  # float16
-
-        print("Avg Per Token Latency: {0:8.2f} ms".format(avg * 1000))
-        print("Avg BW: {0:8.2f} GB/s".format(1 / avg * num_parameters * num_bytes / 1e9))
-        print("Avg flops: {0:8.2f} TFlops/s".format(1 / avg * num_parameters * num_bytes * bs / 1e12))
-        print("Avg Throughput: tokens/s: {}".format((1000 / (avg * 1000)) * bs))
-
-
 def bench_bloom(args):
     model_path = args.path
     max_batch_size = args.batch_size

examples/inference/bench_chatglm2.py

@@ -0,0 +1,116 @@
+import argparse
+import os
+import time
+
+import torch
+from _utils import print_perf_stats
+from transformers import AutoTokenizer
+
+import colossalai
+from colossalai.inference.tensor_parallel.engine import TPInferEngine
+from colossalai.logging import disable_existing_loggers
+from colossalai.shardformer import ShardConfig
+from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ChatGLMForConditionalGeneration
+from colossalai.testing import rerun_if_address_is_in_use, spawn
+
+os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
+
+
+def run_chatglm2_test(args):
+    chatglm2_model_path = args.path
+    max_batch_size = args.batch_size
+    max_input_len = args.input_len
+    max_output_len = args.output_len
+    args.test_mode
+
+    print("max_batch_size : " + str(max_batch_size))
+
+    tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
+    model = ChatGLMForConditionalGeneration.from_pretrained(chatglm2_model_path, pad_token_id=tokenizer.eos_token_id)
+    model = model.half()
+    model.config
+
+    shard_config = ShardConfig(enable_tensor_parallelism=True if args.tp_size > 1 else False, inference_only=True)
+    infer_engine = TPInferEngine(model, shard_config, max_batch_size, max_input_len, max_output_len)
+
+    generate_kwargs = dict(max_new_tokens=1, do_sample=False)
+    input_tokens = {
+        "input_ids": torch.randint(1, 1000, (max_batch_size, max_input_len), device="cuda"),
+        "attention_mask": torch.ones((max_batch_size, max_input_len), device="cuda"),
+    }
+
+    iters = 10
+    prefill_times = []
+
+    warmup = 3
+
+    for i in range(iters):
+        torch.cuda.synchronize()
+        start = time.time()
+        outputs = infer_engine.generate(input_tokens, **generate_kwargs)
+        torch.cuda.synchronize()
+        end = time.time()
+        out_len = outputs.shape[1]
+        print("generation time {} s".format(str(end - start)))
+        print(out_len - max_input_len)
+        prefill_times.append((end - start) / (out_len - max_input_len))
+
+    prefill_times = prefill_times[warmup:]
+    prefill_time_avg = sum(prefill_times) / len(prefill_times)
+    generate_kwargs = dict(max_new_tokens=max_output_len, do_sample=False)
+
+    times = []
+    decoder_times = []
+    for i in range(iters):
+        torch.cuda.synchronize()
+        start = time.time()
+        outputs = infer_engine.generate(input_tokens, **generate_kwargs)
+        torch.cuda.synchronize()
+        end = time.time()
+        out_len = outputs.shape[1]
+        print("generation time {} s".format(str(end - start)))
+        print(out_len - max_input_len)
+        times.append((end - start) / (out_len - max_input_len))
+        if args.test_mode == "decoder_test":
+            decoder_times.append((end - start - prefill_time_avg) / (out_len - max_input_len - 1))
+
+    times = times[warmup:]
+    latency = sum(times) / len(times)
+    print("total process latency is : " + str(latency) + " s")
+    print("total throughput is : " + str(1 / latency * max_batch_size))
+
+    if args.test_mode == "decoder_test":
+        decoder_times = decoder_times[warmup:]
+        latency = sum(decoder_times) / len(decoder_times)
+
+        print("decoder process latency is : " + str(latency) + " s")
+        print("decoder throughput is : " + str(1 / latency * max_batch_size))
+
+    print_perf_stats(times, model.config, max_batch_size)
+
+
+def check_chatglm2(rank, world_size, port, args):
+    disable_existing_loggers()
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    run_chatglm2_test(args)
+
+
+@rerun_if_address_is_in_use()
+def test_chatglm2(args):
+    spawn(check_chatglm2, args.tp_size, args=args)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-p", "--path", type=str, help="Model path", required=True)
+    parser.add_argument("-tp", "--tp_size", type=int, default=1, help="Tensor parallel size")
+    parser.add_argument("-b", "--batch_size", type=int, default=16, help="Maximum batch size")
+    parser.add_argument("--input_len", type=int, default=256, help="Maximum input length")
+    parser.add_argument("--output_len", type=int, default=128, help="Maximum output length")
+    parser.add_argument(
+        "--test_mode", type=str, help="Test mode", default="e2e_test", choices=["e2e_test", "decoder_test"]
+    )
+
+    args = parser.parse_args()
+
+    test_chatglm2(args)

examples/inference/bench_llama.py

@@ -3,6 +3,7 @@
 import time
 
 import torch
+from _utils import print_perf_stats
 from transformers import LlamaForCausalLM, LlamaTokenizer
 
 import colossalai
@@ -14,25 +15,6 @@
 os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
 
 
-def print_perf_stats(latency_set, config, bs, warmup=3):
-    torch.cuda.empty_cache()
-    # trim warmup queries
-    latency_set = list(latency_set)
-    latency_set = latency_set[warmup:]
-    count = len(latency_set)
-
-    if count > 0:
-        latency_set.sort()
-        avg = sum(latency_set) / count
-        num_layers = getattr(config, "num_layers", config.num_hidden_layers)
-        num_parameters = num_layers * config.hidden_size * config.hidden_size * 12
-        num_bytes = 2
-
-        print("Avg Per Token Latency: {0:8.2f} ms".format(avg * 1000))
-        print("Avg BW: {0:8.2f} GB/s".format(1 / avg * num_parameters * num_bytes / 1e9))
-        print("Avg flops: {0:8.2f} TFlops/s".format(1 / avg * num_parameters * num_bytes * bs / 1e12))
-
-
 def run_llama_test(args):
     llama_model_path = args.path
     max_batch_size = args.batch_size
@@ -104,6 +86,8 @@ def run_llama_test(args):
         print("decoder process latency is : " + str(latency) + " s")
         print("decoder throughput is : " + str(1 / latency * max_batch_size))
 
+    print_perf_stats(times, model.config, max_batch_size)
+
 
 def check_llama(rank, world_size, port, args):
     disable_existing_loggers()

examples/inference/gptq_bloom.py

@@ -1,43 +1,22 @@
 import argparse
-import logging
 import os
 import time
 
 import torch
-from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
-from auto_gptq.nn_modules.qlinear import GeneralQuantLinear
-from transformers import AutoTokenizer, BloomForCausalLM, BloomTokenizerFast, LlamaForCausalLM, LlamaTokenizer
+from _utils import print_perf_stats
+from auto_gptq import AutoGPTQForCausalLM
+from transformers import BloomTokenizerFast
 
 import colossalai
 from colossalai.inference.tensor_parallel.engine import TPInferEngine
 from colossalai.logging import disable_existing_loggers
 from colossalai.shardformer import ShardConfig
 from colossalai.testing import clear_cache_before_run, rerun_if_address_is_in_use, spawn
 
-os.environ['TRANSFORMERS_NO_ADVISORY_WARNINGS'] = 'true'
-
-
-def print_perf_stats(latency_set, config, bs, warmup=3):
-    # trim warmup queries
-    latency_set = list(latency_set)
-    latency_set = latency_set[warmup:]
-    count = len(latency_set)
-
-    if count > 0:
-        latency_set.sort()
-        avg = sum(latency_set) / count
-        num_layers = getattr(config, "num_layers", config.num_hidden_layers)
-        num_parameters = num_layers * config.hidden_size * config.hidden_size * 12
-        num_bytes = 2    # float16
-
-        print("Avg Per Token Latency: {0:8.2f} ms".format(avg * 1000))
-        print("Avg BW: {0:8.2f} GB/s".format(1 / avg * num_parameters * num_bytes / 1e9))
-        print("Avg flops: {0:8.2f} TFlops/s".format(1 / avg * num_parameters * num_bytes * bs / 1e12))
-        print("Avg Throughput: tokens/s: {}".format((1000 / (avg * 1000)) * bs))
+os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
 
 
 def bench_bloom(args):
-
     pretrained_model_dir = args.path
     quantized_model_dir = args.quantized_path
     max_batch_size = args.batch_size
@@ -48,9 +27,9 @@ def bench_bloom(args):
     tokenizer.pad_token = tokenizer.eos_token
 
     # load quantized model to the first GPU
-    model = AutoGPTQForCausalLM.from_quantized(quantized_model_dir,
-                                               device=torch.cuda.current_device(),
-                                               inject_fused_attention=False)
+    model = AutoGPTQForCausalLM.from_quantized(
+        quantized_model_dir, device=torch.cuda.current_device(), inject_fused_attention=False
+    )
 
     model = model.half()
 
@@ -60,22 +39,22 @@ def bench_bloom(args):
     generate_kwargs = dict(max_new_tokens=max_output_len, do_sample=False)
 
     input_tokens = {
-        "input_ids": torch.randint(1, 1000, (max_batch_size, max_input_len), device='cuda'),
-        "attention_mask": torch.ones((max_batch_size, max_input_len), device='cuda')
+        "input_ids": torch.randint(1, 1000, (max_batch_size, max_input_len), device="cuda"),
+        "attention_mask": torch.ones((max_batch_size, max_input_len), device="cuda"),
     }
 
     # init TPInferEngine and shard the original model
     # To benchmark torch original, comment out the line of optimizing model
-    shard_config = ShardConfig(enable_tensor_parallelism=True if args.tp_size > 1 else False,
-                               inference_only=True,
-                               inference_gptq=True)
+    shard_config = ShardConfig(
+        enable_tensor_parallelism=True if args.tp_size > 1 else False, inference_only=True, inference_gptq=True
+    )
     infer_engine = TPInferEngine(model, shard_config, max_batch_size, max_input_len, max_output_len)
 
     # prepare data for generation
     generate_kwargs = dict(max_new_tokens=max_output_len, do_sample=False)
     input_tokens = {
         "input_ids": torch.randint(10, 1000, (max_batch_size, max_input_len)),
-        "attention_mask": torch.ones((max_batch_size, max_input_len))
+        "attention_mask": torch.ones((max_batch_size, max_input_len)),
     }
     for t in input_tokens:
         if torch.is_tensor(input_tokens[t]):
@@ -99,7 +78,7 @@ def bench_bloom(args):
 
 def check_bloom(rank, world_size, port, args):
     disable_existing_loggers()
-    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     bench_bloom(args)
 
 
@@ -111,12 +90,12 @@ def test_bloom(args):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument('-p', '--path', type=str, help='Model path', required=True)
-    parser.add_argument('-q', '--quantized_path', type=str, help='Model path', required=True)
-    parser.add_argument('-tp', '--tp_size', type=int, default=1, help='Tensor parallel size')
-    parser.add_argument('-b', '--batch_size', type=int, default=16, help='Maximum batch size')
-    parser.add_argument('--input_len', type=int, default=1024, help='Maximum input length')
-    parser.add_argument('--output_len', type=int, default=128, help='Maximum output length')
+    parser.add_argument("-p", "--path", type=str, help="Model path", required=True)
+    parser.add_argument("-q", "--quantized_path", type=str, help="Model path", required=True)
+    parser.add_argument("-tp", "--tp_size", type=int, default=1, help="Tensor parallel size")
+    parser.add_argument("-b", "--batch_size", type=int, default=16, help="Maximum batch size")
+    parser.add_argument("--input_len", type=int, default=1024, help="Maximum input length")
+    parser.add_argument("--output_len", type=int, default=128, help="Maximum output length")
 
     args = parser.parse_args()
 

examples/inference/gptq_llama.py

@@ -3,6 +3,7 @@
 import time
 
 import torch
+from _utils import print_perf_stats
 from auto_gptq import AutoGPTQForCausalLM
 from transformers import LlamaTokenizer
 
@@ -15,25 +16,6 @@
 os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
 
 
-def print_perf_stats(latency_set, config, bs, warmup=3):
-    # trim warmup queries
-    latency_set = list(latency_set)
-    latency_set = latency_set[warmup:]
-    count = len(latency_set)
-
-    if count > 0:
-        latency_set.sort()
-        avg = sum(latency_set) / count
-        num_layers = getattr(config, "num_layers", config.num_hidden_layers)
-        num_parameters = num_layers * config.hidden_size * config.hidden_size * 12
-        num_bytes = 2
-
-        print("Avg Per Token Latency: {0:8.2f} ms".format(avg * 1000))
-        print("Avg BW: {0:8.2f} GB/s".format(1 / avg * num_parameters * num_bytes / 1e9))
-        print("Avg flops: {0:8.2f} TFlops/s".format(1 / avg * num_parameters * num_bytes * bs / 1e12))
-        print("Avg Throughput: tokens/s: {}".format((1000 / (avg * 1000)) * bs))
-
-
 def run_llama_test(args):
     pretrained_model_dir = args.path
     quantized_model_dir = args.quantized_path