[chat] add distributed impl

applications/ColossalChat/coati/distributed/README.md

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+# Requirements
+
+```bash
+pip install cupy-cuda12x
+python -m cupyx.tools.install_library --cuda 12.x --library nccl
+```

applications/ColossalChat/coati/distributed/comm.py

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+from typing import Any, Dict
+
+import ray.util.collective as cc
+import torch
+import torch.distributed.distributed_c10d as c10d
+from packaging.version import Version
+
+
+def ray_broadcast_object(obj: Any, src: int = 0, device=None, group_name: str = "default") -> Any:
+    rank = cc.get_rank(group_name)
+    if rank == src:
+        if Version(torch.__version__) >= Version("2.3.0"):
+            obj_tensor, size_tensor = c10d._object_to_tensor(obj, device=device, group=None)
+        elif Version(torch.__version__) >= Version("1.13.0"):
+            obj_tensor, size_tensor = c10d._object_to_tensor(obj, device=device)
+        else:
+            obj_tensor, size_tensor = c10d._object_to_tensor(obj)
+        obj_tensor = obj_tensor.to(device)
+        size_tensor = size_tensor.to(device)
+    else:
+        size_tensor = torch.empty(1, dtype=torch.int64, device=device)
+    cc.broadcast(size_tensor, src, group_name)
+    if rank != src:
+        obj_tensor = torch.empty(size_tensor.item(), dtype=torch.uint8, device=device)
+    cc.broadcast(obj_tensor, src, group_name)
+    if rank != src:
+        if Version(torch.__version__) >= Version("2.3.0"):
+            obj = c10d._tensor_to_object(obj_tensor, size_tensor.item(), group=None)
+        else:
+            obj = c10d._tensor_to_object(obj, size_tensor.item())
+    return obj
+
+
+def ray_broadcast_tensor_dict(
+    tensor_dict: Dict[str, torch.Tensor], src: int = 0, device=None, group_name: str = "default"
+) -> Dict[str, torch.Tensor]:
+    rank = cc.get_rank(group_name)
+    if rank == src:
+        metadata = []
+        for k, v in tensor_dict.items():
+            metadata.append((k, v.shape, v.dtype))
+    else:
+        metadata = None
+    metadata = ray_broadcast_object(metadata, src, device, group_name)
+    if rank != src:
+        out_dict = {}
+    for k, shape, dtype in metadata:
+        if rank == src:
+            tensor = tensor_dict[k]
+        else:
+            tensor = torch.empty(shape, dtype=dtype, device=device)
+        cc.broadcast(tensor, src, group_name)
+        if rank != src:
+            out_dict[k] = tensor
+    if rank == src:
+        out_dict = tensor_dict
+    return out_dict

applications/ColossalChat/coati/distributed/consumer.py

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+from contextlib import nullcontext
+from typing import Any, Dict, Optional
+
+import ray
+import ray.util.collective as cc
+import torch
+import torch.distributed as dist
+from tqdm import tqdm
+from transformers import AutoModelForCausalLM
+
+from colossalai.booster import Booster
+from colossalai.booster.plugin import HybridParallelPlugin
+from colossalai.initialize import launch
+from colossalai.nn.optimizer import HybridAdam
+from colossalai.utils import get_current_device
+
+from .comm import ray_broadcast_tensor_dict
+from .utils import bind_batch, post_recv, unbind_batch
+
+
+class BaseConsumer:
+    def __init__(
+        self,
+        num_producers: int,
+        num_episodes: int,
+        rank: int,
+        world_size: int,
+        master_addr: str,
+        master_port: int,
+        num_update_per_episode: int,
+        num_recv_per_update: int,
+        batch_size: int,
+        model_config: Dict[str, Any],
+        plugin_config: Dict[str, Any],
+        microbatch_size: int = 1,
+    ):
+        self.num_producers = num_producers
+        self.num_episodes = num_episodes
+        self.rank = rank
+        self.world_size = world_size
+        self.master_addr = master_addr
+        self.master_port = master_port
+        self.num_update_per_episode = num_update_per_episode
+        self.num_recv_per_update = num_recv_per_update
+        self.batch_size = batch_size
+        self.microbatch_size = microbatch_size
+        assert batch_size % microbatch_size == 0, "batch_size should be divisible by microbatch_size"
+        self.num_microbatches = batch_size // microbatch_size
+
+        self.model_config = model_config
+        self.plugin_config = plugin_config
+        assert self.plugin_config.get("pp_size", 1) == 1, "pp_size > 1 is not supported now"
+
+        self.device = get_current_device()
+
+    def setup(self) -> None:
+        for i in range(self.num_producers):
+            cc.init_collective_group(self.world_size + 1, self.rank + 1, group_name=f"sync_data_{i}")
+        if self.rank == 0:
+            cc.init_collective_group(self.num_producers + 1, self.num_producers, group_name="sync_model")
+        launch(self.rank, self.world_size, self.master_addr, self.master_port, local_rank=0)
+
+        plugin_config = dict(
+            tp_size=1,
+            pp_size=1,
+            precision="bf16",
+            zero_stage=1,
+        )
+        if self.plugin_config.get("pp_size", 1) > 1 and "num_microbatches" not in self.plugin_config:
+            plugin_config["microbatch_size"] = self.microbatch_size
+        plugin_config.update(self.plugin_config)
+        self.plugin = HybridParallelPlugin(**plugin_config)
+        self.booster = Booster(plugin=self.plugin)
+        self.dp_rank = dist.get_rank(self.plugin.dp_group)
+        self.dp_size = dist.get_world_size(self.plugin.dp_group)
+
+        self.buffer = []
+
+        self.recv_cnt = 0
+
+    def state_dict(self) -> Dict[str, torch.Tensor]:
+        raise NotImplementedError
+
+    def step(self, step_idx: int, **kwargs) -> Optional[float]:
+        raise NotImplementedError
+
+    def loop(self) -> None:
+        print(
+            f"Consumer{self.rank} num_update: {self.num_update_per_episode}, num_recv: {self.num_recv_per_update}, nmb: {self.num_microbatches}"
+        )
+        for episode in range(self.num_episodes):
+            with tqdm(range(self.num_update_per_episode), desc=f"Episode {episode}", disable=self.rank != 0) as pbar:
+                for step in pbar:
+                    i = 0
+                    for _ in range(self.num_recv_per_update):
+                        # receive data from producers
+
+                        for r in range(self.num_producers):
+                            print(f"[T{dist.get_rank()}] Recv data episode {episode} step {step} from {r}")
+                            self.buffer.extend(
+                                unbind_batch(
+                                    ray_broadcast_tensor_dict(
+                                        None, src=0, device=self.device, group_name=f"sync_data_{r}"
+                                    )
+                                )
+                            )
+                        while len(self.buffer) >= self.dp_size * self.microbatch_size:
+                            batches = self.buffer[
+                                self.dp_rank * self.microbatch_size : (self.dp_rank + 1) * self.microbatch_size
+                            ]
+                            self.buffer = self.buffer[self.dp_size * self.microbatch_size :]
+                            batch = bind_batch(batches)
+                            batch = post_recv(batch)
+                            loss = self.step(i, **batch)
+                            if loss is not None:
+                                pbar.set_postfix({"loss": loss})
+                            i += 1
+                    assert len(self.buffer) == 0
+                    if episode != self.num_episodes - 1 or step != self.num_update_per_episode - 1:
+                        print(f"[T{dist.get_rank()}] Sync model episode {episode} step {step}")
+                        state_dict = self.state_dict()
+                        if self.rank == 0:
+                            ray_broadcast_tensor_dict(
+                                state_dict, src=self.num_producers, device=self.device, group_name="sync_model"
+                            )
+
+
+@ray.remote
+class SimpleConsumer(BaseConsumer):
+    def __init__(
+        self,
+        num_producers,
+        num_episodes,
+        rank,
+        world_size,
+        master_addr,
+        master_port,
+        num_update_per_episode,
+        num_recv_per_update,
+        batch_size,
+        model_config,
+        plugin_config,
+        microbatch_size=1,
+    ):
+        super().__init__(
+            num_producers,
+            num_episodes,
+            rank,
+            world_size,
+            master_addr,
+            master_port,
+            num_update_per_episode,
+            num_recv_per_update,
+            batch_size,
+            model_config,
+            plugin_config,
+            microbatch_size,
+        )
+        path = model_config.pop("path")
+        self.model = AutoModelForCausalLM.from_pretrained(path, **model_config)
+        self.model.train()
+        self.model.gradient_checkpointing_enable()
+        self.optimizer = HybridAdam(self.model.parameters(), lr=1e-3)
+        self.accum_loss = torch.zeros(1, device=self.device)
+
+    def setup(self):
+        super().setup()
+        self.model, self.optimizer, *_ = self.booster.boost(self.model, self.optimizer)
+
+    def step(self, step_idx: int, **kwargs) -> Optional[float]:
+        need_update = (step_idx + 1) % self.num_microbatches == 0
+
+        ctx = nullcontext() if need_update else self.booster.no_sync(self.model, self.optimizer)
+        with ctx:
+            out = self.model(**kwargs)
+            loss = out.loss / self.num_microbatches
+            self.accum_loss.add_(loss.data)
+            self.booster.backward(loss, self.optimizer)
+        if need_update:
+            self.optimizer.step()
+            self.optimizer.zero_grad()
+            loss_scalar = self.accum_loss.item()
+            self.accum_loss.zero_()
+            return loss_scalar
+
+    def state_dict(self):
+        self.model._force_wait_all_gather()
+        model = self.model.unwrap()
+        state_dict = model.state_dict()
+        return state_dict