[SLM] Qwen2 Multi-GPU support (#1985)

* Update qwen2_model.py

* fix lint issue

* fix lint issue

* fix lint issue

python/mlc_llm/model/qwen2/qwen2_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
 
@@ -35,6 +36,7 @@ class QWen2Config(ConfigBase):  # pylint: disable=too-many-instance-attributes
     context_window_size: int = 0
     prefill_chunk_size: int = 0
     tensor_parallel_shards: int = 1
+    head_dim: int = 0
     dtype: str = "float32"
     kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict)
 
@@ -56,6 +58,9 @@ 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)",
@@ -80,29 +85,19 @@ def __post_init__(self):
 
 class QWen2Attention(nn.Module):  # pylint: disable=too-many-instance-attributes
     def __init__(self, config: QWen2Config):
-        head_dim = config.hidden_size // config.num_attention_heads
+        self.head_dim = config.head_dim
+        self.num_attention_heads = config.num_attention_heads // config.tensor_parallel_shards
+        self.num_key_value_heads = config.num_key_value_heads // config.tensor_parallel_shards
+        self.rope_theta = config.rope_theta
 
         self.c_attn = nn.Linear(
             in_features=config.hidden_size,
-            out_features=(2 * config.num_key_value_heads + config.num_attention_heads) * head_dim,
+            out_features=(2 * self.num_key_value_heads + self.num_attention_heads) * self.head_dim,
             bias=True,
         )
         self.o_proj = nn.Linear(
-            config.num_attention_heads * head_dim, config.hidden_size, bias=False
-        )
-        # KV cache for single sequence
-        self.k_cache = nn.KVCache(
-            config.context_window_size, [config.num_key_value_heads, head_dim]
+            self.num_attention_heads * self.head_dim, config.hidden_size, bias=False
         )
-        self.v_cache = nn.KVCache(
-            config.context_window_size, [config.num_attention_heads, head_dim]
-        )
-
-        self.hidden_size = config.hidden_size
-        self.head_dim = head_dim
-        self.num_attention_heads = config.num_attention_heads
-        self.num_key_value_heads = config.num_key_value_heads
-        self.rope_theta = config.rope_theta
 
     def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
         d, h_q, h_kv = self.head_dim, self.num_attention_heads, self.num_key_value_heads
@@ -128,8 +123,9 @@ def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id:
 
 class QWen2MLP(nn.Module):
     def __init__(self, config: QWen2Config):
-        self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False)
-        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+        self.intermediate_size = config.intermediate_size // config.tensor_parallel_shards
+        self.gate_up_proj = nn.Linear(config.hidden_size, 2 * self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, config.hidden_size, bias=False)
         self.act_fn = ACT2FN[config.hidden_act]
 
     def forward(self, x: Tensor):
@@ -147,15 +143,46 @@ def __init__(self, config: QWen2Config):
             config.hidden_size, -1, config.rms_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_attention_heads * hd
+            k = self.self_attn.num_key_value_heads * hd
+            v = self.self_attn.num_key_value_heads * hd
+            i = self.mlp.intermediate_size
+            _set(
+                self.self_attn.c_attn.weight,
+                tp.ShardSingleDim("_shard_qkv_weight", dim=0, segs=[q, k, v]),
+            )
+            _set(
+                self.self_attn.c_attn.bias,
+                tp.ShardSingleDim("_shard_qkv_bias", 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_up", segs=[i, i], dim=0)
+            )
+            _set(self.mlp.down_proj.weight, tp.ShardSingleDim("_shard_mlp_down", 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.input_layernorm(hidden_states)
         out = self.self_attn(out, paged_kv_cache, layer_id)
-        hidden_states = out + hidden_states
+        hidden_states = self._apply_residual(out, residual=hidden_states)
         out = self.post_attention_layernorm(hidden_states)
         out = self.mlp(out)
-        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 QWen2Model(nn.Module):
     def __init__(self, config: QWen2Config):
@@ -187,7 +214,7 @@ def __init__(self, config: QWen2Config):
         self.rope_theta = config.rope_theta
         self.vocab_size = config.vocab_size
         self.tensor_parallel_shards = config.tensor_parallel_shards
-        self.head_dim = config.hidden_size // config.num_attention_heads
+        self.head_dim = config.head_dim
 
     def to(self, dtype: Optional[str] = None):
         super().to(dtype=dtype)
@@ -211,6 +238,8 @@ 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):