Initial add of distributed model (#1063)

* Initial add of distributed model

Use parallelize_module in model

[ghstack-poisoned]

* Update on "Initial add of distributed model"


Use `parallelize_module` in model.

Added files:

`model_dist.py`: a mirror of model.py with Tensor Parallelism baked in.
`dist_run.py`: toy example of how to run the model in distributed way.

Test:
`torchrun --nproc-per-node 2 dist_run.py`


[ghstack-poisoned]

* Update on "Initial add of distributed model"


Use `parallelize_module` in model.

Added files:

`model_dist.py`: a mirror of model.py with Tensor Parallelism baked in.
`dist_run.py`: toy example of how to run the model in distributed way.

Test:
`torchrun --nproc-per-node 2 dist_run.py`


[ghstack-poisoned]

Author: kwen2501

build/model_dist.py

@@ -0,0 +1,278 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+from pathlib import Path
+from typing import Dict, Optional
+
+import torch
+import torch.nn as nn
+
+from torch import Tensor
+from torch.nn import functional as F
+from torch.distributed._tensor import DTensor, Replicate
+from torch.distributed.device_mesh import _mesh_resources, DeviceMesh
+from torch.distributed.tensor.parallel import parallelize_module, ColwiseParallel, RowwiseParallel
+
+from build.utils import find_multiple
+
+from build.model import TransformerArgs, KVCache, apply_rotary_emb, precompute_freqs_cis
+
+config_path = Path(f"{str(Path(__file__).parent)}/known_model_params")
+
+
+# Use DTensor as output, by default
+Colwise = ColwiseParallel(use_local_output=False)
+Rowwise = RowwiseParallel(use_local_output=False)
+
+# Device mesh context
+device_mesh = None
+
+
+class Transformer(nn.Module):
+    def __init__(self, config: TransformerArgs) -> None:
+        super().__init__()
+        self.config = config
+
+        # Get device mesh
+        global device_mesh
+        if device_mesh is None:
+            device_mesh = _mesh_resources.get_current_mesh()
+
+        tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
+        self.tok_embeddings = parallelize_module(
+            tok_embeddings,
+            device_mesh,
+            RowwiseParallel(input_layouts=Replicate()),
+        )
+        self.layers = nn.ModuleList(
+            TransformerBlock(config) for _ in range(config.n_layers)
+        )
+        self.norm = RMSNorm(config.dim, eps=config.norm_eps)
+        self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
+
+        # self.freqs_cis: Optional[Tensor] = None
+        # self.mask_cache: Optional[Tensor] = None
+        self.max_batch_size = -1
+        self.max_seq_length = -1
+
+    def setup_caches(self, max_batch_size, max_seq_length):
+        if (
+            self.max_seq_length >= max_seq_length
+            and self.max_batch_size >= max_batch_size
+        ):
+            return
+        head_dim = self.config.dim // self.config.n_heads
+        max_seq_length = find_multiple(max_seq_length, 8)
+        self.max_seq_length = max_seq_length
+        self.max_batch_size = max_batch_size
+        for b in self.layers:
+            b.attention.kv_cache = KVCache(
+                max_batch_size, max_seq_length, self.config.n_local_heads, head_dim
+            )
+
+        freqs_cis = precompute_freqs_cis(
+            self.config.dim // self.config.n_heads,
+            self.config.block_size * 2,
+            self.config.rope_base,
+            use_scaled = self.config.use_scaled_rope,
+        )
+        self.register_buffer("freqs_cis", freqs_cis, persistent=True)
+        causal_mask = torch.tril(
+            torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool)
+        )
+        self.register_buffer("causal_mask", causal_mask, persistent=True)
+
+    def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+        assert self.freqs_cis is not None, "Caches must be initialized first"
+        mask = self.causal_mask[None, None, input_pos]
+        freqs_cis = self.freqs_cis[input_pos]
+        x: DTensor = self.tok_embeddings(idx)
+        # TODO: sequence parallelize this
+
+        for _, layer in enumerate(self.layers):
+            x = layer(x, input_pos, freqs_cis, mask)
+        x = self.norm(x)
+        logits = self.output(x)
+        # print(f"logits shape: {logits.shape}")
+        return logits
+
+    @classmethod
+    def from_name(cls, name: str):
+        return cls(TransformerArgs.from_name(name))
+
+    @classmethod
+    def from_table(cls, name: str):
+        return cls(TransformerArgs.from_table(name))
+
+    @classmethod
+    def from_params(cls, params_path: str):
+        return cls(TransformerArgs.from_params(params_path))
+
+    @classmethod
+    def from_gguf(cls, gguf_path: str, **kwargs):
+        from build.gguf_loader import load_model_and_state_dict
+
+        model, state_dict = load_model_and_state_dict(gguf_path, **kwargs)
+        if state_dict != {}:
+            model.load_state_dict(state_dict, assign=True)
+        return model
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, config: TransformerArgs) -> None:
+        super().__init__()
+        self.attention = Attention(config)
+        self.feed_forward = FeedForward(config)
+        self.ffn_norm = RMSNorm(config.dim, config.norm_eps)
+        self.attention_norm = RMSNorm(config.dim, config.norm_eps)
+
+    def forward(
+        self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: Tensor
+    ) -> Tensor:
+        h = x + self.attention(self.attention_norm(x), freqs_cis, mask, input_pos)
+        out = h + self.feed_forward(self.ffn_norm(h))
+        return out
+
+
+class Attention(nn.Module):
+    def __init__(self, config: TransformerArgs):
+        super().__init__()
+        assert config.dim % config.n_heads == 0
+
+        # key, query, value projections for all heads, but in a batch
+        # total_head_dim = (config.n_heads + 2 * config.n_local_heads) * config.head_dim
+        # self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
+        wq = nn.Linear(config.dim, config.n_heads * config.head_dim, bias=False)
+        wk = nn.Linear(
+            config.dim, config.n_local_heads * config.head_dim, bias=False
+        )
+        wv = nn.Linear(
+            config.dim, config.n_local_heads * config.head_dim, bias=False
+        )
+        wo = nn.Linear(config.dim, config.dim, bias=False)
+
+        self.wq = parallelize_module(wq, device_mesh, Colwise)
+        self.wk = parallelize_module(wk, device_mesh, Colwise)
+        self.wv = parallelize_module(wv, device_mesh, Colwise)
+        self.wo = parallelize_module(wo, device_mesh, Rowwise)
+
+        self.kv_cache = None
+
+        self.n_heads = config.n_heads
+        self.head_dim = config.head_dim
+        self.n_local_heads = config.n_local_heads
+        self.dim = config.dim
+        self._register_load_state_dict_pre_hook(self.load_hook)
+
+    def load_hook(self, state_dict, prefix, *args):
+        # if prefix + "wq.weight" in state_dict:
+        #     wq = state_dict.pop(prefix + "wq.weight")
+        #     wk = state_dict.pop(prefix + "wk.weight")
+        #     wv = state_dict.pop(prefix + "wv.weight")
+        #     state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv])
+
+        if prefix + "wqkv.weight" in state_dict:
+            wqkv = state_dict.pop(prefix + "wqkv.weight")
+            q_size = self.n_heads * self.head_dim
+            kv_size = self.n_local_heads * self.head_dim
+            wq, wk, wv = torch.split(wqkv, (q_size, kv_size, kv_size), dim=0)
+            state_dict[prefix + "wq.weight"] = wq
+            state_dict[prefix + "wk.weight"] = wk
+            state_dict[prefix + "wv.weight"] = wv
+
+        return
+
+        def _unfuse_wqkv_state_dict(
+            state_dict: Dict[str, torch.Tensor],
+            dim: int,
+        ):
+            for key in list(state_dict):
+                if key.endswith("wqkv.weight"):
+                    tensor = state_dict[key]
+                    wq_key = key.replace("wqkv.weight", "wq.weight")
+                    state_dict[wq_key] = tensor[:dim]
+                    wk_key = key.replace("wqkv.weight", "wk.weight")
+                    wv_key = key.replace("wqkv.weight", "wv.weight")
+                    wk, wv = tensor[dim:].chunk(2, 0)
+                    state_dict[wk_key] = wk
+                    state_dict[wv_key] = wv
+                    state_dict.pop(key)
+                else:
+                    continue
+
+        _unfuse_wqkv_state_dict(state_dict, self.dim)
+
+    def forward(
+        self,
+        x: Tensor,
+        freqs_cis: Tensor,
+        mask: Tensor,
+        input_pos: Optional[Tensor] = None,
+    ) -> Tensor:
+        bsz, seqlen, _ = x.shape
+
+        q: DTensor = self.wq(x)
+        k: DTensor = self.wk(x)
+        v: DTensor = self.wv(x)
+        # We use `to_local()` to convert DTensor back to regular Tensor
+        q, k, v = q.to_local(), k.to_local(), v.to_local()
+        # kv_size = self.n_local_heads * self.head_dim
+        # q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)
+
+        q = q.view(bsz, seqlen, -1, self.head_dim)
+        k = k.view(bsz, seqlen, -1, self.head_dim)
+        v = v.view(bsz, seqlen, -1, self.head_dim)
+
+        q = apply_rotary_emb(q, freqs_cis)
+        k = apply_rotary_emb(k, freqs_cis)
+
+        q, k, v = (x.transpose(1, 2) for x in (q, k, v))
+
+        # TODO: enable kv cache
+        #if self.kv_cache is not None:
+        #    k, v = self.kv_cache.update(input_pos, k, v)
+
+        k = k.repeat_interleave(self.n_heads // self.n_local_heads, dim=1)
+        v = v.repeat_interleave(self.n_heads // self.n_local_heads, dim=1)
+        y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
+
+        y = y.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
+
+        y: DTensor = self.wo(y)
+        # TODO: sequence parallelize this
+        return y.full_tensor()
+
+
+class FeedForward(nn.Module):
+    def __init__(self, config: TransformerArgs) -> None:
+        super().__init__()
+        w1 = nn.Linear(config.dim, config.hidden_dim, bias=False)
+        w2 = nn.Linear(config.hidden_dim, config.dim, bias=False)
+        w3 = nn.Linear(config.dim, config.hidden_dim, bias=False)
+        self.w1 = parallelize_module(w1, device_mesh, Colwise)
+        self.w2 = parallelize_module(w2, device_mesh, Rowwise)
+        self.w3 = parallelize_module(w3, device_mesh, Colwise)
+
+    def forward(self, x: Tensor) -> Tensor:
+        y: DTensor = self.w2(F.silu(self.w1(x)) * self.w3(x))
+        # y is a DTensor with Partial placement;
+        # we convert its placement to Replicate and convert it back to a regular
+        # Tensor. `full_tensor` is the API that does both.
+        # TODO: sequence parallelize this
+        return y.full_tensor()
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-5):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
+
+    def forward(self, x: Tensor) -> Tensor:
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight

dist_run.py

@@ -0,0 +1,39 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.distributed as dist
+
+from build.model import TransformerArgs
+from build.model_dist import Transformer
+
+# Model config
+def main():
+    config = TransformerArgs.from_name("Transformer-2-7b-chat-hf")
+    print(config)
+
+    # Construct a device mesh with available devices (multi-host or single host)
+    device_mesh = dist.init_device_mesh("cuda", (2,), mesh_dim_names=("tp",))
+    rank = dist.get_rank()
+    device = torch.device(f"cuda:{rank}")
+
+    # Create parallel model with device_mesh context
+    with device:
+        with device_mesh:
+            model = Transformer(config)
+            model.setup_caches(1, 4096)
+
+    print(model)
+
+    # Distributed run
+    input_ids = torch.randint(0, config.vocab_size, (1, 4096), device=device)
+    input_pos = torch.arange(0, 4096, device=device)
+    output = model(input_ids, input_pos)
+    dist.destroy_process_group()
+    print(f"Rank {rank} completes.")
+
+if __name__ == "__main__":
+    main()