[SLM] Baichuan Multi-GPU support (#2037)

This PR enables TP function of Baichuan2 model.

Author: tlopex

python/mlc_llm/model/baichuan/baichuan_model.py

@@ -13,6 +13,7 @@
 from mlc_llm import op as op_ext
 from mlc_llm.nn import PagedKVCache, RopeMode
 from mlc_llm.support import logging
+from mlc_llm.support import tensor_parallel as tp
 from mlc_llm.support.config import ConfigBase
 from mlc_llm.support.style import bold
 
@@ -39,6 +40,7 @@ class BaichuanConfig(ConfigBase):  # pylint: disable=too-many-instance-attribute
     prefill_chunk_size: int = 0
     tensor_parallel_shards: int = 1
     max_batch_size: int = 1
+    head_dim: int = 0
     kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict)
 
     def __post_init__(self):
@@ -59,6 +61,9 @@ def __post_init__(self):
                     "`context_window_size`, `max_position_embeddings` or `max_sequence_length` is "
                     "provided in `config.json`."
                 )
+        if self.head_dim == 0:
+            self.head_dim = self.hidden_size // self.num_attention_heads
+        assert self.head_dim * self.num_attention_heads == self.hidden_size
         if self.prefill_chunk_size == 0:
             logger.info(
                 "%s defaults to %s (%d)",
@@ -84,11 +89,9 @@ def __post_init__(self):
 class BaichuanAttention(nn.Module):  # pylint: disable=too-many-instance-attributes
     def __init__(self, config: BaichuanConfig):
         self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.hidden_size // self.num_heads
-        self.max_position_embeddings = config.context_window_size
-
-        self.W_pack = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
+        self.num_heads = config.num_attention_heads // config.tensor_parallel_shards
+        self.head_dim = config.head_dim
+        self.W_pack = nn.Linear(self.hidden_size, 3 * self.num_heads * self.head_dim, bias=False)
         self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
 
     def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
@@ -105,12 +108,13 @@ def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id:
 
 class BaichuanMLP(nn.Module):
     def __init__(self, config: BaichuanConfig):
+        self.intermediate_size = config.intermediate_size // config.tensor_parallel_shards
         self.gate_up_proj = nn.Linear(
             in_features=config.hidden_size,
-            out_features=2 * config.intermediate_size,
+            out_features=2 * self.intermediate_size,
             bias=False,
         )
-        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, config.hidden_size, bias=False)
 
     def forward(self, x):
         concat_x1_x2 = self.gate_up_proj(x)
@@ -126,13 +130,41 @@ def __init__(self, config: BaichuanConfig):
         self.input_layernorm = nn.RMSNorm(config.hidden_size, -1, norm_eps, bias=False)
         self.post_attention_layernorm = nn.RMSNorm(config.hidden_size, -1, norm_eps, bias=False)
 
+        def _set_tp():
+            def _set(layer, hint):
+                layer.attrs["shard_strategy"] = hint
+
+            hd = config.head_dim
+            q = self.self_attn.num_heads * hd
+            k = self.self_attn.num_heads * hd
+            v = self.self_attn.num_heads * hd
+            i = self.mlp.intermediate_size
+            _set(
+                self.self_attn.W_pack.weight,
+                tp.ShardSingleDim("_shard_qkv_weight", dim=0, segs=[q, k, v]),
+            )
+            _set(self.self_attn.o_proj.weight, tp.ShardSingleDim("_shard_o", dim=1))
+            _set(
+                self.mlp.gate_up_proj.weight,
+                tp.ShardSingleDim("_shard_mlp_gate_up", segs=[i, i], dim=0),
+            )
+            _set(self.mlp.down_proj.weight, tp.ShardSingleDim("_shard_mlp_down_proj", dim=1))
+
+        self.tensor_parallel_shards = config.tensor_parallel_shards
+        _set_tp()
+
     def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
         out = self.self_attn(self.input_layernorm(hidden_states), paged_kv_cache, layer_id)
-        hidden_states = out + hidden_states
+        hidden_states = self._apply_residual(out, residual=hidden_states)
         out = self.mlp(self.post_attention_layernorm(hidden_states))
-        hidden_states = out + hidden_states
+        hidden_states = self._apply_residual(out, residual=hidden_states)
         return hidden_states
 
+    def _apply_residual(self, out, residual):
+        if self.tensor_parallel_shards > 1:
+            return op.ccl_allreduce(out, "sum") + residual
+        return out + residual
+
 
 class BaichuanModel(nn.Module):
     def __init__(self, config: BaichuanConfig):
@@ -159,7 +191,7 @@ def __init__(self, config: BaichuanConfig):
         self.num_hidden_layers = config.num_hidden_layers
         self.hidden_size = config.hidden_size
         self.num_attention_heads = config.num_attention_heads
-        self.head_dim = self.hidden_size // self.num_attention_heads
+        self.head_dim = config.head_dim
         self.vocab_size = config.vocab_size
         self.rope_theta = 10000
         self.tensor_parallel_shards = config.tensor_parallel_shards
@@ -187,6 +219,8 @@ def batch_forward(
         return logits
 
     def embed(self, input_ids: Tensor):
+        if self.tensor_parallel_shards > 1:
+            input_ids = op.ccl_broadcast_from_worker0(input_ids)
         return self.model.embed_tokens(input_ids)
 
     def prefill(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
@@ -215,6 +249,8 @@ def decode(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
     def batch_prefill(
         self, input_embeds: Tensor, logit_positions: Tensor, paged_kv_cache: PagedKVCache
     ):
+        if self.tensor_parallel_shards > 1:
+            logit_positions = op.ccl_broadcast_from_worker0(logit_positions)
         logits = self.batch_forward(input_embeds, paged_kv_cache, logit_positions)
         return logits, paged_kv_cache