[infer] fix test bug (#4838)

* fix test bug

* delete useless code

* fix typo

colossalai/shardformer/modeling/chatglm2_6b/modeling_chatglm.py

@@ -873,7 +873,7 @@ class ChatGLMModel(ChatGLMPreTrainedModel):
 
         self.rotary_pos_emb = RotaryEmbedding(
             rotary_dim // 2,
-            original_impl=config.original_rope,
+            # original_impl=config.original_rope, # config has no attribute original_rope
             device=device,
             dtype=config.torch_dtype,
         )

examples/inference/bench_llama.py

@@ -43,7 +43,6 @@ def run_llama_test(args):
     tokenizer.pad_token_id = tokenizer.unk_token_id
     model = LlamaForCausalLM.from_pretrained(llama_model_path, pad_token_id=tokenizer.eos_token_id)
     model = model.half()
-
     model_config = model.config
 
     shard_config = ShardConfig(enable_tensor_parallelism=True if args.tp_size > 1 else False, inference_only=True)

tests/test_infer/test_bloom_infer.py

 import pytest
 import torch
 from packaging import version
+from transformers import BloomForCausalLM
+from transformers.models.bloom.configuration_bloom import BloomConfig
 
 import colossalai
 from colossalai.inference.tensor_parallel import TPInferEngine
 from colossalai.logging import disable_existing_loggers
 from colossalai.shardformer import ShardConfig
 from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
-from tests.kit.model_zoo import model_zoo
 
 TP_SIZE = 2
 MAX_BATCH_SIZE = 4
@@ -26,19 +27,21 @@ CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse("11.5")
     ],
 )
 def run(test_config):
-    sub_model_zoo = model_zoo.get_sub_registry("transformers_bloom_for_causal_lm")
-    for name, (model_fn, data_gen_fn, _, _, _) in sub_model_zoo.items():
-        orig_model = model_fn()
-        orig_model = orig_model.half()
-        data = data_gen_fn()
+    bloom_config = BloomConfig(num_hidden_layers=2, bos_token_id=0, eos_token_id=1, vocab_size=1200, hidden_size=1024)
+    model = BloomForCausalLM(bloom_config)
+    model = model.half()
 
     shard_config = ShardConfig(
         enable_tensor_parallelism=True if test_config["tp_size"] > 1 else False, inference_only=True
     )
-        infer_engine = TPInferEngine(orig_model, shard_config, MAX_BATCH_SIZE, MAX_INPUT_LEN, MAX_OUTPUT_LEN)
+    infer_engine = TPInferEngine(model, shard_config, MAX_BATCH_SIZE, MAX_INPUT_LEN, MAX_OUTPUT_LEN)
+    generate_kwargs = dict(max_new_tokens=MAX_OUTPUT_LEN, do_sample=False)
 
-        generate_kwargs = dict(do_sample=False)
-        outputs = infer_engine.generate(data, **generate_kwargs)
+    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"),
+    }
+    outputs = infer_engine.generate(input_tokens, **generate_kwargs)
 
     assert outputs is not None
 

tests/test_infer/test_chatglm2_infer.py

@@ -2,17 +2,15 @@ import os
 
 import pytest
 import torch
-import torch.distributed as dist
 from packaging import version
-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.configuration_chatglm import ChatGLMConfig
 from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ChatGLMForConditionalGeneration
 from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
-from tests.kit.model_zoo.transformers.chatglm2 import infer_config
 
 os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
 TPSIZE = 1
@@ -31,28 +29,31 @@ CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse("11.5")
     ],
 )
 def run_chatglm2_test(test_config):
-    tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
-    # pad_token_id = 0
-    model_fn = lambda: ChatGLMForConditionalGeneration(infer_config, empty_init=False)
-    orig_model = model_fn()
-    orig_model = orig_model.half()
-    text = ["how is the weather today?"]
-    input_ids = tokenizer.batch_encode_plus(text, return_tensors="pt", padding=True)
+    chatglm_config = ChatGLMConfig(
+        num_layers=2,
+        vocab_size=1200,
+        use_cache=True,
+        multi_query_attention=True,
+        multi_query_group_num=2,
+        num_attention_heads=8,
+        hidden_size=1024,
+    )
+    model = ChatGLMForConditionalGeneration(chatglm_config)
+    model = model.half()
+
     shard_config = ShardConfig(
         enable_tensor_parallelism=True if test_config["tp_size"] > 1 else False, inference_only=True
     )
-    infer_engine = TPInferEngine(orig_model, shard_config, BATCH_SIZE, MAX_INPUT_LEN, MAX_OUTPUT_LEN)
-
+    infer_engine = TPInferEngine(model, shard_config, BATCH_SIZE, MAX_INPUT_LEN, MAX_OUTPUT_LEN)
     generate_kwargs = dict(max_new_tokens=MAX_OUTPUT_LEN, do_sample=False)
-    outputs = infer_engine.generate(input_ids, **generate_kwargs)
-    assert outputs is not None
 
-    # print("outputs.shape: ", outputs[0].shape)
-    # print("outputs: ", outputs[0])
-    if not dist.is_initialized() or dist.get_rank() == 0:
-        for o in outputs:
-            output_text = tokenizer.decode(o)
-            print(output_text)
+    input_tokens = {
+        "input_ids": torch.randint(1, 1000, (BATCH_SIZE, MAX_INPUT_LEN), device="cuda"),
+        "attention_mask": torch.ones((BATCH_SIZE, MAX_INPUT_LEN), device="cuda"),
+    }
+    outputs = infer_engine.generate(input_tokens, **generate_kwargs)
+
+    assert outputs is not None
 
 
 def check_chatglm2(rank, world_size, port):

tests/test_infer/test_llama_infer.py

@@ -3,13 +3,14 @@ import os
 import pytest
 import torch
 from packaging import version
+from transformers import LlamaForCausalLM
+from transformers.models.llama.configuration_llama import LlamaConfig
 
 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, parameterize, rerun_if_address_is_in_use, spawn
-from tests.kit.model_zoo import model_zoo
 
 os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
 TPSIZE = 2
@@ -29,19 +30,22 @@ CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse("11.5")
     ],
 )
 def run_llama_test(test_config):
-    sub_model_zoo = model_zoo.get_sub_registry("transformers_llama_for_casual_lm")
-    for name, (model_fn, data_gen_fn, _, _, _) in sub_model_zoo.items():
-        orig_model = model_fn()
-        orig_model = orig_model.half()
-        data = data_gen_fn()
+    llama_config = LlamaConfig(num_hidden_layers=2, bos_token_id=0, eos_token_id=1, vocab_size=1200, hidden_size=1024)
+    model = LlamaForCausalLM(llama_config)
+    model = model.half()
 
     shard_config = ShardConfig(
         enable_tensor_parallelism=True if test_config["tp_size"] > 1 else False, inference_only=True
     )
-        infer_engine = TPInferEngine(orig_model, shard_config, BATCH_SIZE, MAX_INPUT_LEN, MAX_OUTPUT_LEN)
+    infer_engine = TPInferEngine(model, shard_config, BATCH_SIZE, MAX_INPUT_LEN, MAX_OUTPUT_LEN)
+    init_to_get_rotary(model.model, base=10000)
+    generate_kwargs = dict(max_new_tokens=MAX_OUTPUT_LEN, do_sample=False)
 
-        generate_kwargs = dict(do_sample=False)
-        outputs = infer_engine.generate(data, **generate_kwargs)
+    input_tokens = {
+        "input_ids": torch.randint(1, 1000, (BATCH_SIZE, MAX_INPUT_LEN), device="cuda"),
+        "attention_mask": torch.ones((BATCH_SIZE, MAX_INPUT_LEN), device="cuda"),
+    }
+    outputs = infer_engine.generate(input_tokens, **generate_kwargs)
 
     assert outputs is not None
 

tests/test_infer_ops/triton/test_llama2_token_attn.py

@@ -38,9 +38,7 @@ def test():
     q = torch.empty((Z, head_num, head_dim), dtype=dtype, device="cuda").normal_(mean=0.1, std=0.2)
     k = torch.empty((Z * seq_len, head_num, head_dim), dtype=dtype, device="cuda").normal_(mean=0.4, std=0.2)
     v = torch.empty((Z * seq_len, head_num, head_dim), dtype=dtype, device="cuda").normal_(mean=0.3, std=0.2)
-    o = torch.empty_like()
-    # o = torch.empty((Z, head_num, head_dim), dtype=dtype, device="cuda").normal_(mean=0.3, std=0.2)
-
+    o = torch.empty((Z, head_num, head_dim), dtype=dtype, device="cuda")
     max_kv_cache_len = seq_len
     kv_cache_start_loc = torch.zeros((Z,), dtype=torch.int32, device="cuda")
     kv_cache_loc = torch.zeros((Z, seq_len), dtype=torch.int32, device="cuda")