[Pipeline inference] Refactor code, add docsting, fix bug

colossalai/inference/pipeline/benchmark/benchmark.py

@@ -0,0 +1,112 @@
+import torch
+import torch.distributed as dist
+import transformers
+
+import colossalai
+import time
+from colossalai.inference import PPInferEngine
+from colossalai.inference.pipeline.policy.llama_ppinfer import LlamaForCausalLMPipelinePolicy
+import argparse
+GIGABYTE = 1024 ** 3
+MEGABYTE = 1024 * 1024
+
+colossalai.launch_from_torch(config={})
+
+def data_gen(batch_size: int=4, seq_len: int=512):
+    input_ids = torch.randint(10, 30000, (1, seq_len), dtype=torch.int32)
+    attention_mask = torch.ones((1, seq_len), dtype=torch.int32)
+    data = dict(input_ids=input_ids, attention_mask=attention_mask)
+    for k, v in data.items():
+        if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__:
+            new_shape = [1] * v.dim()
+            new_shape[0] = batch_size
+            data[k] = v.to('cuda').repeat(*new_shape)
+    return data
+
+def print_details_info(timestamps, model_config, args, whole_end2end):
+    if dist.get_rank() == 0:
+        prefill = []
+        encoder = []
+        end2end = []
+        for timestamp in timestamps:
+            prefill.append(timestamp[1] - timestamp[0])
+            encoder.append(
+                sum(timestamp[i + 1] - timestamp[i] for i in range(1,len(timestamp) - 1)) / (len(timestamp) - 2))
+            end2end.append(timestamp[-1] - timestamp[0])
+        print(whole_end2end)
+        with open(f"{args.log_path}/llama-{args.model}{args.dtype}_pp{args.pp_size}_{args.seq_len}_{args.new_length}_bsz{args.batch_size}_mbsz{args.mb_size}.log","w+") as f:
+            mb_avg_end2end = sum(end2end)/len(end2end)
+            mb_avg_latency = mb_avg_end2end/(args.new_length * args.mb_size)
+            whole_avg_latency = whole_end2end/(args.new_length * args.batch_size)
+            num_layers = getattr(model_config, "num_layers", model_config.num_hidden_layers)
+            num_parameters = num_layers * model_config.hidden_size * model_config.hidden_size * 12 / args.pp_size
+            if args.dtype in ['fp16','bf16']:
+                num_bytes = 2
+            else:
+                num_bytes = 4
+
+            f.write(f"llama-{args.model}{args.dtype}_pp{args.pp_size}, input_len:{args.seq_len}, output_len:{args.new_length}, bsz:{args.batch_size}, mbsz:{args.mb_size}\n")
+            f.write("Average prefill time: {0:8.2f} ms\n".format(sum(prefill)/len(prefill)*1000))
+            f.write("Average encode time: {0:8.2f} ms\n".format(sum(encoder)/len(encoder)*1000))
+            f.write("Average micro batch end2end time: {0:8.2f} ms\n".format(mb_avg_end2end*1000))
+            f.write("Average micro batch Per Token Latency: {0:8.2f} ms\n".format(mb_avg_latency * 1000))
+            f.write("Whole batch end2end time: {0:8.2f} ms\n".format(whole_end2end*1000))
+            f.write("Whole batch Per Token Latency: {0:8.2f} ms\n".format(whole_avg_latency * 1000))
+            f.write("Throughput: {} tokens/s\n".format((1000/(whole_avg_latency * 1000))))
+            f.write("flops: {0:8.2f} TFlops/s\n".format(1/whole_avg_latency * num_parameters * num_bytes / 1e12))
+            f.write("----------------------------------------------------------\n")
+
+
+    if torch.cuda.is_available():
+        current_device = torch.cuda.current_device()
+
+        # free memory and the total available memory in bytes
+        global_free_memory, total_GPU_memory_occupied = torch.cuda.mem_get_info()
+        memory_allocated = torch.cuda.memory_allocated()
+        max_memory_allocated = torch.cuda.max_memory_allocated()
+        memory_reserved = torch.cuda.memory_reserved()
+        max_memory_reserved = torch.cuda.max_memory_reserved()
+        with open(f"{args.log_path}/llama-{args.model}{args.dtype}_pp{args.pp_size}_{args.seq_len}_{args.new_length}_bsz{args.batch_size}_mbsz{args.mb_size}.log","a") as f:
+            f.write(
+                f"\nCurrently using GPU: {current_device}\n"
+                f"free memory : {global_free_memory / GIGABYTE:.4f} GB,\n"
+                f"total memory: {total_GPU_memory_occupied / GIGABYTE:.4f} GB,\n"
+                f"memory allocated: {memory_allocated / GIGABYTE:.4f} GB,\n"
+                f"Max CUDA memory allocated: {max_memory_allocated / GIGABYTE:.4f} GB,\n"
+                f"memory reserved/cached: {memory_reserved / GIGABYTE:.4f} GB,\n"
+                f"Max CUDA memory reserved/cached: {max_memory_reserved / GIGABYTE:.4f} GB,\n"
+            )
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model', default='toy', help='the size of model')
+    parser.add_argument('-b', '--batch_size', type=int, default=8, help='batch size')
+    parser.add_argument('-s', '--seq_len', type=int, default=8, help='sequence length')
+    parser.add_argument('--new_length', type=int, default=4, help='new tokens length')
+    parser.add_argument('--mb_size', type=int, default=1, help='micro_batch_size')
+    parser.add_argument('--pp_size', type=int, default=2, help='pipeline size')
+    parser.add_argument('--log_path', type=str, default='./log' ,help='where to store the benchmark log')
+    parser.add_argument('--dtype', type=str, default='fp16', help='data type')
+    args = parser.parse_args()
+
+    if args.model == 'toy':
+        model = transformers.LlamaForCausalLM(transformers.LlamaConfig(num_hidden_layers=8))
+    elif args.model == '7b':
+        model = transformers.LlamaForCausalLM(transformers.AutoConfig.from_pretrained('decapoda-research/llama-7b-hf'))
+    elif args.model == '13b':
+        model = transformers.LlamaForCausalLM(transformers.AutoConfig.from_pretrained('decapoda-research/llama-13b-hf'))
+    else:
+        raise NotImplementedError
+
+
+    engine = PPInferEngine(pp_size=args.pp_size, dtype=args.dtype, micro_batch_size=args.mb_size, new_length=args.new_length, model=model, model_policy=LlamaForCausalLMPipelinePolicy(),verbose=True)
+    data = data_gen(args.batch_size, args.seq_len)
+
+    torch.cuda.synchronize()
+    whole_end2end = time.time()
+    output, timestamps = engine.inference([data])
+    torch.cuda.synchronize()
+    whole_end2end = time.time() - whole_end2end
+
+    print_details_info(timestamps, model.config, args, whole_end2end)
+

colossalai/inference/pipeline/benchmark/run.sh

@@ -0,0 +1,50 @@
+script_dir=$(cd "$(dirname "$0")" && pwd)
+cd "${script_dir}"
+
+# 7b, fp32, 2 gpu, 1024, 128
+for BATCH_SIZE in 2 4 8 16; do
+    CUDA_VISIBLE_DEVICES=0,1 colossalai run --nproc_per_node 2 --master_port 29800 ./benchmark.py \
+        --model="7b" \
+        --dtype="fp16" \
+        --batch_size=${BATCH_SIZE} \
+        --seq_len=1024 \
+        --new_length=128 \
+        --mb_size=$((${BATCH_SIZE}/2)) \
+        --pp_size=2
+done
+
+# 7b, fp32, 2 gpu, 512, 512
+for BATCH_SIZE in 2 4 8 16 32; do
+    CUDA_VISIBLE_DEVICES=0,1 colossalai run --nproc_per_node 2 --master_port 29800 ./benchmark.py \
+        --model="7b" \
+        --dtype="fp16" \
+        --batch_size=${BATCH_SIZE} \
+        --seq_len=512 \
+        --new_length=512 \
+        --mb_size=$((${BATCH_SIZE}/2)) \
+        --pp_size=2
+done
+
+# 7b, fp32, 2 gpu, 1024, 128
+for BATCH_SIZE in 2 4 8; do
+    CUDA_VISIBLE_DEVICES=0,1 colossalai run --nproc_per_node 2 --master_port 29800 ./benchmark.py \
+        --model="13b" \
+        --dtype="fp16" \
+        --batch_size=${BATCH_SIZE} \
+        --seq_len=1024 \
+        --new_length=128 \
+        --mb_size=$((${BATCH_SIZE}/2)) \
+        --pp_size=2
+done
+
+# 13b, fp16, 2 gpu, 512, 512
+for BATCH_SIZE in 2 4 8 16; do
+    CUDA_VISIBLE_DEVICES=0,1 colossalai run --nproc_per_node 2 --master_port 29800 ./benchmark.py \
+        --model="13b" \
+        --dtype="fp16" \
+        --batch_size=${BATCH_SIZE} \
+        --seq_len=512 \
+        --new_length=512 \
+        --mb_size=$((${BATCH_SIZE}/2)) \
+        --pp_size=2
+done

colossalai/inference/pipeline/engine.py

@@ -1,23 +1,15 @@
-import re
-from functools import partial
-from types import MethodType
-from typing import Callable, List, Optional, Set
+from typing import Callable, List, Optional, Set, Union
 
-import numpy as np
 import torch
-import torch.distributed as dist
 import torch.nn as nn
 
 from colossalai.cluster import ProcessGroupMesh
 from colossalai.pipeline.schedule.generate import GenerateSchedule
 from colossalai.pipeline.stage_manager import PipelineStageManager
 from colossalai.shardformer import ShardConfig, ShardFormer
-from colossalai.shardformer._utils import getattr_
 from colossalai.shardformer.policies.base_policy import Policy
 
 from .microbatch_manager import MicroBatchManager
-from .policy.gpt2_ppinfer import GPT2LMHeadModelPipelinePolicy
-from .utils import get_suffix_name, set_tensors_to_none
 
 
 class PPInferEngine:
@@ -54,6 +46,7 @@ class PPInferEngine:
     def __init__(
         self,
         pp_size: int,
+        dtype: str = 'fp16',
         pp_model: nn.Module = None,
         model: nn.Module = None,
         model_policy: Policy = None,
@@ -72,12 +65,22 @@ def __init__(
         self.stage_manager = PipelineStageManager(self.pg_mesh, 0, True)
         self.mb_manager = MicroBatchManager(self.stage_manager.stage, new_length, micro_batch_size,
                                             micro_batch_buffer_size or pp_size)
+        self.verbose = verbose
         self.schedule = GenerateSchedule(self.stage_manager, self.mb_manager, verbose)
+
+        assert dtype in ['fp16', 'fp32', 'bf16'], "dtype should be one of 'fp16', 'fp32', 'bf16'"
+        if dtype == 'fp16':
+            model.half()
+        elif dtype == 'bf16':
+            model.to(torch.bfloat16)
         self.model = pp_model or self._shardformer(model, model_policy)
 
     def inference(self, input_list):
-        out = self.schedule.generate_step(self.model, iter(input_list))
-        return out
+        out, timestamp = self.schedule.generate_step(self.model, iter(input_list))
+        if self.verbose:
+            return out, timestamp
+        else:
+            return out
 
     def _shardformer(self, model, model_policy):
         shardconfig = ShardConfig(

colossalai/pipeline/p2p.py

@@ -159,12 +159,27 @@ def _recv_object(src: int, dst: int, group: ProcessGroup) -> Any:
     return object_list[0]
 
 
-def _p2p_comm_shape(
+def _p2p_comm(
     tensor_send_next: torch.Tensor,
     recv_prev: bool,
     peer: int,
     group: ProcessGroup,
+    comm_dtype: torch.dtype = torch.float16,
 ):
+    """ 
+    Send and recv tensor using P2P communication, used when pipeline size is 2 to solve the race communication.
+
+    Agrs:
+        tensor_send_next (torch.Tensor): tensor to be sent to next stage
+        recv_prev (bool): whether to receive tensor from previous stage
+        peer (int): rank of the peer
+        group (ProcessGroup): process group
+        comm_dtype (torch.dtype): dtype of the tensor to be sent
+
+    Returns:
+        torch.Tensor: tensor received from previous stage
+    """
+    # send and recv shape
     send_next_shape = None
     recv_prev_shape = None
 
@@ -194,20 +209,10 @@ def _p2p_comm_shape(
     if recv_prev_shape is not None:
         recv_prev_shape = recv_prev_shape.tolist()
 
-    return recv_prev_shape
-
-
-def _p2p_comm(
-    tensor_send_next: torch.Tensor,
-    recv_pre: bool,
-    peer: int,
-    group: ProcessGroup,
-    comm_type: torch.dtype = torch.float32,
-):
+    # send and recv data
     tensor_recv_prev = None
-    recv_prev_shape = _p2p_comm_shape(tensor_send_next, recv_pre, peer, group)
-    if recv_pre:
-        tensor_recv_prev = torch.empty(recv_prev_shape, device=torch.cuda.current_device(), dtype=comm_type)
+    if recv_prev:
+        tensor_recv_prev = torch.empty(recv_prev_shape, device=torch.cuda.current_device(), dtype=comm_dtype)
 
     ops = []
     if tensor_send_next is not None:
@@ -296,7 +301,7 @@ def send_backward(self, input_object: Any, prev_rank: int = None) -> None:
         cur_rank = self.stage_manager.get_rank()
         _send_object(input_object, cur_rank, prev_rank, self.stage_manager.get_p2p_process_group(cur_rank, prev_rank))
 
-    def p2p_communicate(self, output_object: Any, recv_pre: bool, peer: int = None) -> None:
+    def p2p_communicate(self, output_object: Any, recv_pre: bool, peer: int = None, comm_dtype: torch.dtype = torch.float16) -> None:
         """
         Sends the input tensor to the next stage in pipeline, using `P2Pop` in torch.
 
@@ -307,5 +312,5 @@ def p2p_communicate(self, output_object: Any, recv_pre: bool, peer: int = None)
         if peer is None:
             peer = self.stage_manager.get_next_rank()
         cur_rank = self.stage_manager.get_rank()
-        recv_tensor = _p2p_comm(output_object, recv_pre, peer, self.stage_manager.get_p2p_process_group(cur_rank, peer))
+        recv_tensor = _p2p_comm(output_object, recv_pre, peer, self.stage_manager.get_p2p_process_group(cur_rank, peer), comm_dtype)
         return recv_tensor