[Model] Cohere CommandR+ (#3829)

vllm/model_executor/models/commandr.py

@@ -25,6 +25,7 @@ from typing import List, Optional, Tuple
 import torch
 import torch.utils.checkpoint
 from torch import nn
+from torch.nn.parameter import Parameter
 from transformers import CohereConfig
 
 from vllm.attention import Attention, AttentionMetadata
@@ -39,8 +40,9 @@ from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
 from vllm.model_executor.parallel_utils.parallel_state import (
-    get_tensor_model_parallel_world_size)
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.utils import set_weight_attrs
 from vllm.model_executor.weight_utils import (default_weight_loader,
                                               hf_model_weights_iterator)
 from vllm.sequence import SamplerOutput
@@ -48,11 +50,11 @@ from vllm.sequence import SamplerOutput
 
 class LayerNorm(nn.Module):
 
-    def __init__(self, hidden_size, eps=1e-5, bias=False):
+    def __init__(self, param_shape=None, eps=1e-5):
         super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.bias = nn.Parameter(torch.zeros(hidden_size)) if bias else None
+        self.weight = nn.Parameter(torch.ones(param_shape))
         self.variance_epsilon = eps
+        set_weight_attrs(self.weight, {"weight_loader": self.weight_loader})
 
     def forward(self, hidden_states, residuals=None):
         input_dtype = hidden_states.dtype
@@ -62,10 +64,20 @@ class LayerNorm(nn.Module):
         hidden_states = (hidden_states -
                          mean) * torch.rsqrt(variance + self.variance_epsilon)
         hidden_states = self.weight.to(torch.float32) * hidden_states
-        if self.bias is not None:
-            hidden_states = hidden_states + self.bias.to(torch.float32)
         return hidden_states.to(input_dtype), residuals
 
+    def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
+        tp_rank = get_tensor_model_parallel_rank()
+        shard_dim = 0 if param.dim() != 1 else None
+        param_data = param.data
+        if shard_dim is not None:
+            shard_size = param_data.shape[shard_dim]
+            start_idx = tp_rank * shard_size
+            loaded_weight = loaded_weight.narrow(shard_dim, start_idx,
+                                                 shard_size)
+        assert param_data.shape == loaded_weight.shape
+        param_data.copy_(loaded_weight)
+
 
 # Copied from transformers.models.llama.modeling_llama.LlamaMLP Llama->Cohere
 class CohereMLP(nn.Module):
@@ -131,6 +143,7 @@ class CohereAttention(nn.Module):
         self.max_position_embeddings = config.max_position_embeddings
         self.rope_theta = config.rope_theta
         self.rope_scaling = getattr(config, "rope_scaling", None)
+        self.use_qk_norm = getattr(config, "use_qk_norm", False)
         self.qkv_proj = QKVParallelLinear(
             self.hidden_size,
             self.head_dim,
@@ -159,6 +172,22 @@ class CohereAttention(nn.Module):
             self.scaling,
             num_kv_heads=self.num_kv_heads,
         )
+        if self.use_qk_norm:
+            self.q_norm = LayerNorm(param_shape=(self.num_heads,
+                                                 self.head_dim),
+                                    eps=config.layer_norm_eps)
+            self.k_norm = LayerNorm(param_shape=(self.num_kv_heads,
+                                                 self.head_dim),
+                                    eps=config.layer_norm_eps)
+
+    def _apply_qk_norm(self, q, k):
+        q = q.view(*q.shape[:-1], -1, self.head_dim)
+        k = k.view(*k.shape[:-1], -1, self.head_dim)
+        q, _ = self.q_norm(q)
+        k, _ = self.k_norm(k)
+        q = q.view(*q.shape[:-2], -1)
+        k = k.view(*k.shape[:-2], -1)
+        return q, k
 
     def forward(
         self,
@@ -169,6 +198,8 @@ class CohereAttention(nn.Module):
     ) -> torch.Tensor:
         qkv, _ = self.qkv_proj(hidden_states)
         q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        if self.use_qk_norm:
+            q, k = self._apply_qk_norm(q, k)
         q, k = self.rotary_emb(positions, q, k)
         attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
         output, _ = self.o_proj(attn_output)
@@ -186,7 +217,7 @@ class CohereDecoderLayer(nn.Module):
         self.self_attn = CohereAttention(config, linear_method=linear_method)
 
         self.mlp = CohereMLP(config, linear_method=linear_method)
-        self.input_layernorm = LayerNorm(config.hidden_size,
+        self.input_layernorm = LayerNorm(param_shape=(config.hidden_size),
                                          eps=config.layer_norm_eps)
 
     def forward(
@@ -229,7 +260,8 @@ class CohereModel(nn.Module):
             CohereDecoderLayer(config, linear_method=linear_method)
             for _ in range(config.num_hidden_layers)
         ])
-        self.norm = LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.norm = LayerNorm(param_shape=(config.hidden_size),
+                              eps=config.layer_norm_eps)
 
     def forward(
         self,