[inference]fix import bug and delete down useless init (#4830)

* fix import bug and release useless init

* fix

* fix

* fix

colossalai/inference/tensor_parallel/modeling/__init__.py

-import _utils
-
 from .bloom import BloomInferenceForwards
 from .chatglm2 import ChatGLM2InferenceForwards
 from .llama import LlamaInferenceForwards

colossalai/inference/tensor_parallel/modeling/_utils.py

 """
 Utils for model inference
 """
+import os
+
+import torch
+
 from colossalai.kernel.triton.copy_kv_cache_dest import copy_kv_cache_to_dest
 
 
 def copy_kv_to_mem_cache(layer_id, key_buffer, value_buffer, context_mem_index, mem_manager):
+    """
+    This function copies the key and value cache to the memory cache
+    Args:
+        layer_id : id of current layer
+        key_buffer : key cache
+        value_buffer : value cache
+        context_mem_index : index of memory cache in kv cache manager
+        mem_manager : cache manager
+    """
     copy_kv_cache_to_dest(key_buffer, context_mem_index, mem_manager.key_buffer[layer_id])
     copy_kv_cache_to_dest(value_buffer, context_mem_index, mem_manager.value_buffer[layer_id])
-    return
+
+
+def init_to_get_rotary(self, base=10000, use_elem=False):
+    """
+    This function initializes the rotary positional embedding, it is compatible for all models and is called in ShardFormer
+    Args:
+        self : Model that holds the rotary positional embedding
+        base : calculation arg
+        use_elem : activated when using chatglm-based models
+    """
+    self.config.head_dim_ = self.config.hidden_size // self.config.num_attention_heads
+    if not hasattr(self.config, "rope_scaling"):
+        rope_scaling_factor = 1.0
+    else:
+        rope_scaling_factor = self.config.rope_scaling.factor if self.config.rope_scaling is not None else 1.0
+
+    if hasattr(self.config, "max_sequence_length"):
+        max_seq_len = self.config.max_sequence_length
+    elif hasattr(self.config, "max_position_embeddings"):
+        max_seq_len = self.config.max_position_embeddings * rope_scaling_factor
+    else:
+        max_seq_len = 2048 * rope_scaling_factor
+    base = float(base)
+
+    # NTK  ref: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+    ntk_alpha = float(os.environ.get("INFER_NTK_ALPHA", None))
+
+    if ntk_alpha is not None:
+        ntk_alpha = float(ntk_alpha)
+        assert ntk_alpha >= 1, "NTK alpha must be greater than or equal to 1"
+        if ntk_alpha > 1:
+            print(f"Note: NTK enabled, alpha set to {ntk_alpha}")
+        max_seq_len *= ntk_alpha
+        base = base * (ntk_alpha ** (self.head_dim_ / (self.head_dim_ - 2)))  # Base change formula
+
+    n_elem = self.config.head_dim_
+    if use_elem:
+        n_elem //= 2
+
+    inv_freq = 1.0 / (base ** (torch.arange(0, n_elem, 2, device="cpu", dtype=torch.float32) / n_elem))
+    t = torch.arange(max_seq_len + 1024 * 64, device="cpu", dtype=torch.float32) / rope_scaling_factor
+    freqs = torch.outer(t, inv_freq)
+
+    self._cos_cached = torch.cos(freqs).to(torch.float16).cuda()
+    self._sin_cached = torch.sin(freqs).to(torch.float16).cuda()

colossalai/inference/tensor_parallel/modeling/llama.py

@@ -5,12 +5,9 @@ from transformers.modeling_outputs import BaseModelOutputWithPast
 from transformers.models.llama.modeling_llama import LlamaAttention, LlamaDecoderLayer, LlamaModel, LlamaRMSNorm
 
 from colossalai.inference.tensor_parallel.batch_infer_state import BatchInferState
-from colossalai.kernel.triton import (
-    copy_kv_cache_to_dest,
-    llama_context_attn_fwd,
-    rotary_embedding_fwd,
-    token_attention_fwd,
-)
+from colossalai.kernel.triton import llama_context_attn_fwd, rotary_embedding_fwd, token_attention_fwd
+
+from ._utils import copy_kv_to_mem_cache
 
 try:
     from vllm import layernorm_ops, pos_encoding_ops
@@ -46,12 +43,6 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
     return q_embed, k_embed
 
 
-def _copy_kv_to_mem_cache(layer_id, key_buffer, value_buffer, context_mem_index, mem_manager):
-    copy_kv_cache_to_dest(key_buffer, context_mem_index, mem_manager.key_buffer[layer_id])
-    copy_kv_cache_to_dest(value_buffer, context_mem_index, mem_manager.value_buffer[layer_id])
-    return
-
-
 class LlamaInferenceForwards:
     """
     This class holds forwards for llama inference.
@@ -285,11 +276,6 @@ class LlamaInferenceForwards:
         rotary_embedding_fwd(query_states.view(-1, self.num_heads, self.head_dim), cos, sin)
         rotary_embedding_fwd(key_states.view(-1, self.num_heads, self.head_dim), cos, sin)
 
-        def _copy_kv_to_mem_cache(layer_id, key_buffer, value_buffer, context_mem_index, mem_manager):
-            copy_kv_cache_to_dest(key_buffer, context_mem_index, mem_manager.key_buffer[layer_id])
-            copy_kv_cache_to_dest(value_buffer, context_mem_index, mem_manager.value_buffer[layer_id])
-            return
-
         query_states = query_states.reshape(-1, self.num_heads, self.head_dim)
         key_states = key_states.reshape(-1, self.num_heads, self.head_dim)
         value_states = value_states.reshape(-1, self.num_heads, self.head_dim)
@@ -298,7 +284,7 @@ class LlamaInferenceForwards:
             # first token generation
 
             # copy key and value calculated in current step to memory manager
-            _copy_kv_to_mem_cache(
+            copy_kv_to_mem_cache(
                 infer_state.decode_layer_id,
                 key_states,
                 value_states,
@@ -331,7 +317,7 @@ class LlamaInferenceForwards:
             else:
                 # if decode is not contiguous, use triton kernel to copy key and value cache
                 # k, v shape: [batch_size, num_heads, head_dim/embed_size_per_head
-                _copy_kv_to_mem_cache(
+                copy_kv_to_mem_cache(
                     infer_state.decode_layer_id,
                     key_states,
                     value_states,

colossalai/inference/tensor_parallel/policies/chatglm2.py

 from functools import partial
 
-import torch
-
 from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import (
     ChatGLMForConditionalGeneration,
     ChatGLMModel,
@@ -9,13 +7,14 @@ from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import (
     GLMTransformer,
     SelfAttention,
 )
+
 # import colossalai
 from colossalai.shardformer.policies.chatglm2 import ChatGLMModelPolicy
 
-from ..modeling.chatglm2 import ChatGLM2InferenceForwards, _init_to_get_rotary
+from ..modeling._utils import init_to_get_rotary
+from ..modeling.chatglm2 import ChatGLM2InferenceForwards
 
 try:
-    from colossalai.kernel.triton.rms_norm import rmsnorm_forward
     HAS_TRITON_RMSNORM = True
 except:
     print("you should install triton from https://github.com/openai/triton")
@@ -23,7 +22,6 @@ except:
 
 
 class ChatGLM2InferPolicy(ChatGLMModelPolicy):
-
     def __init__(self) -> None:
         super().__init__()
 
@@ -32,45 +30,44 @@ class ChatGLM2InferPolicy(ChatGLMModelPolicy):
         self.shard_config._infer()
 
         model_infer_forward = ChatGLM2InferenceForwards.chatglm_model_forward
-        method_replacement = {'forward': model_infer_forward}
+        method_replacement = {"forward": model_infer_forward}
         self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=ChatGLMModel)
 
         encoder_infer_forward = ChatGLM2InferenceForwards.chatglm_encoder_forward
-        method_replacement = {'forward': encoder_infer_forward}
-        self.append_or_create_method_replacement(description=method_replacement,
-                                                 policy=policy,
-                                                 target_key=GLMTransformer)
+        method_replacement = {"forward": encoder_infer_forward}
+        self.append_or_create_method_replacement(
+            description=method_replacement, policy=policy, target_key=GLMTransformer
+        )
 
         encoder_layer_infer_forward = ChatGLM2InferenceForwards.chatglm_glmblock_forward
-        method_replacement = {'forward': encoder_layer_infer_forward}
+        method_replacement = {"forward": encoder_layer_infer_forward}
         self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=GLMBlock)
 
         attn_infer_forward = ChatGLM2InferenceForwards.chatglm_flash_attn_kvcache_forward
-        method_replacement = {'forward': attn_infer_forward}
-        self.append_or_create_method_replacement(description=method_replacement,
-                                                 policy=policy,
-                                                 target_key=SelfAttention)
+        method_replacement = {"forward": attn_infer_forward}
+        self.append_or_create_method_replacement(
+            description=method_replacement, policy=policy, target_key=SelfAttention
+        )
 
         # for rmsnorm and others, we need to check the shape
         return policy
 
     def postprocess(self):
-        _init_to_get_rotary(self.model)
+        init_to_get_rotary(self.model)
         return self.model
 
 
 class ChatGLM2ForConditionalGenerationInferPolicy(ChatGLM2InferPolicy):
-
     def __init__(self) -> None:
         super().__init__()
 
     def module_policy(self):
         policy = super().module_policy()
         model_infer_forward = ChatGLM2InferenceForwards.chatglm_for_conditional_generation_forward
-        method_replacement = {'forward': partial(model_infer_forward)}
-        self.append_or_create_method_replacement(description=method_replacement,
-                                                 policy=policy,
-                                                 target_key=ChatGLMForConditionalGeneration)
+        method_replacement = {"forward": partial(model_infer_forward)}
+        self.append_or_create_method_replacement(
+            description=method_replacement, policy=policy, target_key=ChatGLMForConditionalGeneration
+        )
         return policy
 
     def postprocess(self):

colossalai/inference/tensor_parallel/policies/llama.py

@@ -3,11 +3,12 @@ from functools import partial
 import torch
 from transformers.models.llama.modeling_llama import LlamaAttention, LlamaDecoderLayer, LlamaModel, LlamaRMSNorm
 
-from colossalai.shardformer.layer import VocabParallelEmbedding1D
-from colossalai.shardformer.policies.base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
+from colossalai.shardformer.policies.base_policy import ModulePolicyDescription, SubModuleReplacementDescription
+
 # import colossalai
 from colossalai.shardformer.policies.llama import LlamaForCausalLMPolicy
 
+from ..modeling._utils import init_to_get_rotary
 from ..modeling.llama import LlamaInferenceForwards, get_llama_vllm_rmsnorm_forward
 
 try:
@@ -50,38 +51,38 @@ class LlamaModelInferPolicy(LlamaForCausalLMPolicy):
                     SubModuleReplacementDescription(
                         suffix="self_attn.q_proj",
                         target_module=ColCaiQuantLinear,
-                        kwargs={'split_num': 1},
+                        kwargs={"split_num": 1},
                     ),
                     SubModuleReplacementDescription(
                         suffix="self_attn.k_proj",
                         target_module=ColCaiQuantLinear,
-                        kwargs={'split_num': 1},
+                        kwargs={"split_num": 1},
                     ),
                     SubModuleReplacementDescription(
                         suffix="self_attn.v_proj",
                         target_module=ColCaiQuantLinear,
-                        kwargs={'split_num': 1},
+                        kwargs={"split_num": 1},
                     ),
                     SubModuleReplacementDescription(
                         suffix="self_attn.o_proj",
                         target_module=RowCaiQuantLinear,
-                        kwargs={'split_num': 1},
+                        kwargs={"split_num": 1},
                     ),
                     SubModuleReplacementDescription(
                         suffix="mlp.gate_proj",
                         target_module=ColCaiQuantLinear,
-                        kwargs={'split_num': 1},
+                        kwargs={"split_num": 1},
                     ),
                     SubModuleReplacementDescription(
                         suffix="mlp.up_proj",
                         target_module=ColCaiQuantLinear,
-                        kwargs={'split_num': 1},
+                        kwargs={"split_num": 1},
                     ),
                     SubModuleReplacementDescription(
                         suffix="mlp.down_proj",
                         target_module=RowCaiQuantLinear,
-                        kwargs={'split_num': 1},
-                    )
+                        kwargs={"split_num": 1},
+                    ),
                 ],
             )
 
@@ -117,3 +118,7 @@ class LlamaModelInferPolicy(LlamaForCausalLMPolicy):
             )
 
         return policy
+
+    def postprocess(self):
+        init_to_get_rotary(self.model.model)
+        return self.model

colossalai/kernel/triton/__init__.py

@@ -3,6 +3,12 @@ try:
 
     HAS_TRITON = True
 
+except ImportError:
+    HAS_TRITON = False
+    print("Triton is not installed. Please install Triton to use Triton kernels.")
+
+# There may exist import error even if we have triton installed.
+if HAS_TRITON:
     from .context_attention import bloom_context_attn_fwd, llama_context_attn_fwd
     from .copy_kv_cache_dest import copy_kv_cache_to_dest
     from .fused_layernorm import layer_norm
@@ -23,7 +29,3 @@ try:
         "token_attention_fwd",
         "gptq_fused_linear_triton",
     ]
-
-except ImportError:
-    HAS_TRITON = False
-    print("Triton is not installed. Please install Triton to use Triton kernels.")

examples/inference/bench_llama.py

@@ -15,30 +15,6 @@ from colossalai.testing import clear_cache_before_run, rerun_if_address_is_in_us
 os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
 
 
-def init_to_get_rotary(self, base=10000):
-    self.config.head_dim_ = self.config.hidden_size // self.config.num_attention_heads
-    if not hasattr(self.config, "rope_scaling"):
-        rope_scaling_factor = 1.0
-    else:
-        rope_scaling_factor = self.config.rope_scaling.factor if self.config.rope_scaling is not None else 1.0
-    if hasattr(self.config, "max_sequence_length"):
-        max_seq_len = self.config.max_sequence_length
-    elif hasattr(self.config, "max_position_embeddings"):
-        max_seq_len = self.config.max_position_embeddings * rope_scaling_factor
-    else:
-        max_seq_len = 2048 * rope_scaling_factor
-    base = float(base)
-    inv_freq = 1.0 / (
-        base ** (torch.arange(0, self.config.head_dim_, 2, device="cpu", dtype=torch.float32) / self.config.head_dim_)
-    )
-    t = torch.arange(max_seq_len + 1024 * 64, device="cpu", dtype=torch.float32) / rope_scaling_factor
-    freqs = torch.outer(t, inv_freq)
-
-    self._cos_cached = torch.cos(freqs).to(torch.float16).cuda()
-    self._sin_cached = torch.sin(freqs).to(torch.float16).cuda()
-    return
-
-
 def print_perf_stats(latency_set, config, bs, warmup=3):
     # trim warmup queries
     latency_set = list(latency_set)
@@ -66,7 +42,6 @@ def run_llama_test(args):
     tokenizer = LlamaTokenizer.from_pretrained(llama_model_path)
     tokenizer.pad_token_id = tokenizer.unk_token_id
     model = LlamaForCausalLM.from_pretrained(llama_model_path, pad_token_id=tokenizer.eos_token_id)
-    init_to_get_rotary(model.model, base=10000)
     model = model.half()
 
     model_config = model.config

examples/inference/gptq_llama.py

 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 torch import distributed as dist
-from torch.profiler import ProfilerActivity, profile, record_function
-from transformers import AutoTokenizer, LlamaForCausalLM, LlamaTokenizer, TextGenerationPipeline
+from auto_gptq import AutoGPTQForCausalLM
+from transformers import LlamaTokenizer
 
 import colossalai
-from colossalai.gptq import CaiQuantLinear
-from colossalai.gptq.gptq_tp import replace_autogptq_linear
 from colossalai.inference.tensor_parallel.engine import TPInferEngine
+from colossalai.inference.tensor_parallel.modeling._utils import init_to_get_rotary
 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 init_to_get_rotary(self, base=10000):
-    self.config.head_dim_ = self.config.hidden_size // self.config.num_attention_heads
-    if not hasattr(self.config, "rope_scaling"):
-        rope_scaling_factor = 1.0
-    else:
-        rope_scaling_factor = self.config.rope_scaling.factor if self.config.rope_scaling is not None else 1.0
-    if hasattr(self.config, "max_sequence_length"):
-        max_seq_len = self.config.max_sequence_length
-    elif hasattr(self.config, "max_position_embeddings"):
-        max_seq_len = self.config.max_position_embeddings * rope_scaling_factor
-    else:
-        max_seq_len = 2048 * rope_scaling_factor
-    base = float(base)
-    inv_freq = 1.0 / (base**(torch.arange(0, self.config.head_dim_, 2, device="cpu", dtype=torch.float32) /
-                             self.config.head_dim_))
-    t = torch.arange(max_seq_len + 1024 * 64, device="cpu", dtype=torch.float32) / rope_scaling_factor
-    freqs = torch.outer(t, inv_freq)
-
-    self._cos_cached = torch.cos(freqs).to(torch.float16).cuda()
-    self._sin_cached = torch.sin(freqs).to(torch.float16).cuda()
-    return
+os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
 
 
 def print_perf_stats(latency_set, config, bs, warmup=3):
@@ -74,23 +46,23 @@ def run_llama_test(args):
     tokenizer.pad_token_id = tokenizer.eos_token_id
 
     # 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
+    )
 
     init_to_get_rotary(model.model.model, base=10000)
 
     model_config = model.config
-    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)
 
     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"),
     }
 
     iters = 10
@@ -111,7 +83,7 @@ def run_llama_test(args):
 
 def check_llama(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")
     run_llama_test(args)
 
 
@@ -123,12 +95,12 @@ def test_llama(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()
 

tests/test_infer/test_llama_infer.py

@@ -20,30 +20,6 @@ MAX_OUTPUT_LEN = 100
 CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse("11.5")
 
 
-def init_to_get_rotary(self, base=10000):
-    self.config.head_dim_ = self.config.hidden_size // self.config.num_attention_heads
-    if not hasattr(self.config, "rope_scaling"):
-        rope_scaling_factor = 1.0
-    else:
-        rope_scaling_factor = self.config.rope_scaling.factor if self.config.rope_scaling is not None else 1.0
-    if hasattr(self.config, "max_sequence_length"):
-        max_seq_len = self.config.max_sequence_length
-    elif hasattr(self.config, "max_position_embeddings"):
-        max_seq_len = self.config.max_position_embeddings * rope_scaling_factor
-    else:
-        max_seq_len = 2048 * rope_scaling_factor
-    base = float(base)
-    inv_freq = 1.0 / (
-        base ** (torch.arange(0, self.config.head_dim_, 2, device="cpu", dtype=torch.float32) / self.config.head_dim_)
-    )
-    t = torch.arange(max_seq_len + 1024 * 64, device="cpu", dtype=torch.float32) / rope_scaling_factor
-    freqs = torch.outer(t, inv_freq)
-
-    self._cos_cached = torch.cos(freqs).to(torch.float16).cuda()
-    self._sin_cached = torch.sin(freqs).to(torch.float16).cuda()
-    return
-
-
 @parameterize(
     "test_config",
     [
@@ -56,7 +32,6 @@ 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()
-        init_to_get_rotary(orig_model.model, base=10000)
         orig_model = orig_model.half()
         data = data_gen_fn()