merge

.github/workflows/build.yaml

@@ -7,7 +7,7 @@ on:
       - 'main'
       - 'push-image-always'
     tags:
-      - 'v*'
+      - "v*"
 
 jobs:
   build-test-push-image:
@@ -49,7 +49,7 @@ jobs:
       - name: Install soci
         uses: lerentis/[email protected]
         with:
-          soci-release: 'v0.4.0'
+          soci-release: "v0.4.0"
 
       - name: Set up Docker Buildx
         uses: docker/[email protected]
@@ -100,7 +100,7 @@ jobs:
         uses: docker/build-push-action@v5
         with:
           context: .
-          file: ./Dockerfile  # Path to your Dockerfile
+          file: ./Dockerfile # Path to your Dockerfile
           push: false
           tags: ${{ steps.meta.outputs.tags }}
           load: true
@@ -168,6 +168,15 @@ jobs:
             sudo ctr i push --user "${{ github.repository_owner }}:${{ secrets.GHCR_PAT }}" $tag
           done
 
+      - name: Run integration tests
+        uses: ./.github/workflows/integration-tests
+        if: ${{ github.ref == 'refs/heads/main' }}
+        with:
+          test_image_tag: ${{ steps.meta.outputs.tags }}
+          use_local_image: true
+          github_token: ${{ secrets.GHCR_PAT }}
+          huggingface_token: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
+
       - name: Create and push soci index
         env:
           tags: ${{ steps.meta.outputs.tags }}

server/lorax_server/adapters/lora.py

@@ -152,11 +152,19 @@ def load(
         layer_type: str,
         unused_weight_names: Set[str],
     ) -> Optional[AdapterWeights]:
+        # for vlm models we need to return list of layers
+        # so nlayers is a list of ints in this case but in others its just an int
         nlayers = model.get_num_layers_for_type(layer_type)
-        lora_a_list = [None] * nlayers
-        lora_b_list = [None] * nlayers
+        if type(nlayers) is int:
+            lora_a_list = [None] * nlayers
+            lora_b_list = [None] * nlayers
+            layer_ids = list(range(nlayers))
+        else:
+            lora_a_list = [None] * len(nlayers)
+            lora_b_list = [None] * len(nlayers)
+            layer_ids = nlayers
 
-        for layer_id in range(nlayers):
+        for i, layer_id in enumerate(layer_ids):
             key = (layer_id, layer_type)
             weight_name, layer = model.target_to_layer[key]
 
@@ -184,8 +192,8 @@ def load(
 
             # Merge scaling factor into lora_b due to associativity of matrix multiplication:
             # (A * B) * C = A * (B * C)
-            lora_a_list[layer_id] = lora_a.transpose(0, 1)
-            lora_b_list[layer_id] = lora_b.transpose(0, 1) * scale
+            lora_a_list[i] = lora_a.transpose(0, 1)
+            lora_b_list[i] = lora_b.transpose(0, 1) * scale
 
         # pad lora ranks to be compatible with sgmv
         lora_a_list = [pad_rank(w, dim=1, world_size=model.world_size) for w in lora_a_list]

server/lorax_server/models/custom_modeling/mllama.py

@@ -24,17 +24,27 @@
 from transformers.activations import ACT2FN
 
 from lorax_server.adapters.weights import AdapterBatchData
-from lorax_server.layers import (
+from lorax_server.models.custom_modeling.flash_llama_modeling import (
+    FlashLlamaForCausalLM,
+    FlashLlamaLayer,
+)
+from lorax_server.utils.attention.common import Seqlen
+from lorax_server.utils.layers import (
     FastLinear,
+    TensorParallelAdapterRowLinear,
     TensorParallelColumnLinear,
     TensorParallelEmbedding,
+    TensorParallelMultiAdapterLinear,
     TensorParallelRowLinear,
 )
-from lorax_server.models.custom_modeling.flash_llama_modeling import (
-    FlashLlamaForCausalLM,
-    FlashLlamaLayer,
+from lorax_server.utils.lora import (
+    FC1,
+    FC2,
+    K_PROJ,
+    O_PROJ,
+    Q_PROJ,
+    V_PROJ,
 )
-from lorax_server.utils.attention.common import Seqlen
 
 
 # Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
@@ -200,27 +210,76 @@ def _prepare_cross_attention_mask(
 
 # Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->MllamaVision
 class MllamaVisionMLP(nn.Module):
-    def __init__(self, *, prefix, config, weights):
+    def __init__(self, *, prefix, config, weights, layer_id, model_type):
         super().__init__()
         self.config = config
         self.activation_fn = ACT2FN[config.hidden_act]
-        self.fc1 = TensorParallelColumnLinear.load(prefix=f"{prefix}.fc1", weights=weights, config=config, bias=True)
-        self.fc2 = TensorParallelRowLinear.load(prefix=f"{prefix}.fc2", weights=weights, config=config, bias=True)
 
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.fc1(hidden_states)
+        fc1 = TensorParallelColumnLinear.load_multi(
+            config,
+            prefixes=[f"{prefix}.fc1"],
+            weights=weights,
+            dim=0,
+            bias=True,
+        )
+
+        out_size = fc1.linear.weight.shape[-1] * weights.process_group.size()
+        self.fc1 = TensorParallelMultiAdapterLinear.load(
+            fc1,
+            layer_id,
+            [f'{model_type}_{FC1}'],
+            sizes=[out_size],
+            process_group=weights.process_group
+        )
+        self.fc2 = TensorParallelAdapterRowLinear.load(
+            TensorParallelRowLinear.load(
+                config,
+                prefix=f"{prefix}.fc2",
+                weights=weights,
+                bias=True,
+            ),
+            layer_id,
+            f'{model_type}_{FC2}',
+            process_group=weights.process_group,
+        )
+
+    def forward(self, hidden_states: torch.Tensor, adapter_data: AdapterBatchData) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states, adapter_data)
         hidden_states = self.activation_fn(hidden_states)
-        hidden_states = self.fc2(hidden_states)
+        hidden_states = self.fc2(hidden_states, adapter_data)
         return hidden_states
 
 
+def load_attention(config, prefix, weights, layer_id, model_type, head_dim, n_head, n_head_kv):
+    base_layer = TensorParallelColumnLinear.load_multi(
+        config,
+        prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
+        dim=0,
+        weights=weights,
+        bias=False,
+    )
+    return TensorParallelMultiAdapterLinear.load(
+        base_layer,
+        layer_id,
+        [f'{model_type}_{Q_PROJ}', f'{model_type}_{K_PROJ}', f'{model_type}_{V_PROJ}'],
+        sizes=[
+            head_dim * n_head,
+            head_dim * n_head_kv,
+            head_dim * n_head_kv,
+        ],
+        process_group=weights.process_group,
+    )
+
+
 class MllamaVisionSdpaAttention(nn.Module):
-    def __init__(self, *, prefix, config, weights):
+    def __init__(self, *, prefix, config, weights, layer_id, model_type):
         super().__init__()
 
         self.embed_dim = config.hidden_size
         self.head_dim = config.hidden_size // config.attention_heads
         self.num_heads = config.attention_heads // weights.process_group.size()
+        self.head_size = config.hidden_size // self.num_heads
+        self.num_key_value_heads = getattr(config, "n_head_kv", None) or self.num_heads
 
         self.qkv_proj = TensorParallelColumnLinear.load_multi(
             config,
@@ -229,19 +288,35 @@ def __init__(self, *, prefix, config, weights):
             weights=weights,
             bias=False,
         )
-        self.o_proj = TensorParallelRowLinear.load(
+        self.qkv_proj = load_attention(
             config,
-            prefix=f"{prefix}.o_proj",
-            weights=weights,
-            bias=False,
+            prefix,
+            weights,
+            layer_id,
+            model_type,
+            self.head_size,
+            self.num_heads,
+            self.num_key_value_heads,
+        )
+        self.o_proj = TensorParallelAdapterRowLinear.load(
+            TensorParallelRowLinear.load(
+                config,
+                prefix=f"{prefix}.o_proj",
+                weights=weights,
+                bias=False,
+            ),
+            layer_id,
+            f'{model_type}_{O_PROJ}',
+            process_group=weights.process_group,
         )
 
     def forward(
         self,
         hidden_state: torch.Tensor,
         attention_mask: Optional[torch.Tensor] = None,
+        adapter_data: AdapterBatchData = None,
     ) -> torch.Tensor:
-        qkv = self.qkv_proj(hidden_state)
+        qkv = self.qkv_proj(hidden_state, adapter_data)
         query, key, value = qkv.split(
             [
                 self.head_dim * self.num_heads,
@@ -267,21 +342,33 @@ def forward(
         attn_output = attn_output.transpose(1, 2).contiguous()
         attn_output = attn_output.reshape(batch_size, q_seq_len, -1)
 
-        output = self.o_proj(attn_output)
+        output = self.o_proj(attn_output, adapter_data)
         return output
 
 
 class MllamaVisionEncoderLayer(nn.Module):
-    def __init__(self, *, prefix, config, weights, is_gated: bool):
+    def __init__(self, *, prefix, config, weights, is_gated: bool, layer_id: int, model_type: str):
         super().__init__()
 
         self.hidden_size = config.hidden_size
         self.num_attention_heads = config.attention_heads
         self.is_gated = is_gated
         self.intermediate_size = config.intermediate_size
 
-        self.self_attn = MllamaVisionSdpaAttention(prefix=f"{prefix}.self_attn", config=config, weights=weights)
-        self.mlp = MllamaVisionMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
+        self.self_attn = MllamaVisionSdpaAttention(
+            prefix=f"{prefix}.self_attn",
+            config=config,
+            weights=weights,
+            layer_id=layer_id,
+            model_type=model_type,
+        )
+        self.mlp = MllamaVisionMLP(
+            prefix=f"{prefix}.mlp",
+            config=config,
+            weights=weights,
+            layer_id=layer_id,
+            model_type=model_type,
+        )
 
         self.input_layernorm = nn.LayerNorm.load(prefix=f"{prefix}.input_layernorm", weights=weights, eps=1e-05)
         self.post_attention_layernorm = nn.LayerNorm.load(
@@ -297,47 +384,52 @@ def forward(
         self,
         hidden_state: torch.Tensor,
         attention_mask: Optional[torch.Tensor] = None,
+        adapter_data: AdapterBatchData = None,
     ):
         # Self Attention
         residual = hidden_state
         hidden_state = self.input_layernorm(hidden_state)
-        hidden_state = self.self_attn(hidden_state, attention_mask=attention_mask)
+        hidden_state = self.self_attn(hidden_state, attention_mask, adapter_data)
         gate_attn = 1 if not self.is_gated else self.gate_attn.tanh()
         hidden_state = residual + gate_attn * hidden_state
 
         # Feed forward
         residual = hidden_state
         hidden_state = self.post_attention_layernorm(hidden_state)
-        hidden_state = self.mlp(hidden_state)
+        hidden_state = self.mlp(hidden_state, adapter_data)
         gate_ffn = 1 if not self.is_gated else self.gate_ffn.tanh()
         hidden_state = residual + gate_ffn * hidden_state
         return hidden_state
 
 
 class MllamaVisionEncoder(nn.Module):
-    def __init__(self, *, prefix, config, weights, is_gated: bool, num_layers: int):
+    def __init__(self, *, prefix, config, weights, is_gated: bool, num_layers: int, model_type: str):
         super().__init__()
         self.config = config
         self.layers = [
             MllamaVisionEncoderLayer(
-                prefix=f"{prefix}.layers.{i}",
+                prefix=f"{prefix}.layers.{layer_id}",
                 config=config,
                 weights=weights,
                 is_gated=is_gated,
+                layer_id=layer_id,
+                model_type=model_type,
             )
-            for i in range(num_layers)
+            for layer_id in range(num_layers)
         ]
 
     def forward(
         self,
         hidden_states: torch.Tensor,
         attention_mask: Optional[torch.Tensor] = None,
+        adapter_data: AdapterBatchData = None,
     ):
         encoder_states = [hidden_states]
         for encoder_layer in self.layers:
             layer_outputs = encoder_layer(
                 hidden_states,
                 attention_mask,
+                adapter_data,
             )
 
             hidden_states = layer_outputs
@@ -465,13 +557,15 @@ def __init__(self, *, prefix, config, weights):
             weights=weights,
             is_gated=False,
             num_layers=config.num_hidden_layers,
+            model_type='VISION_TRANSFORMER',
         )
         self.global_transformer = MllamaVisionEncoder(
             prefix=f"{prefix}.global_transformer",
             config=config,
             weights=weights,
             is_gated=True,
             num_layers=config.num_global_layers,
+            model_type='VISION_GLOBAL_TRANSFORMER',
         )
 
     def apply_class_embedding(self, hidden_state: torch.Tensor) -> torch.Tensor:
@@ -485,6 +579,7 @@ def forward(
         pixel_values: torch.Tensor,
         aspect_ratio_ids: torch.Tensor,
         attention_mask: torch.Tensor,
+        adapter_data: AdapterBatchData,
     ) -> torch.Tensor:
         batch_size, num_concurrent_media, num_tiles, num_channels, height, width = pixel_values.shape
 
@@ -538,6 +633,7 @@ def forward(
         hidden_state, all_intermediate_hidden_states = self.transformer(
             hidden_state,
             attention_mask=attention_mask,
+            adapter_data=adapter_data,
         )
         intermediate_hidden_states = [
             hidden_state
@@ -560,7 +656,11 @@ def forward(
             num_tiles * (num_patches + num_padding_patches),
             dim,
         )
-        hidden_state, _ = self.global_transformer(hidden_state, attention_mask=attention_mask)
+        hidden_state, _ = self.global_transformer(
+            hidden_state,
+            attention_mask=attention_mask,
+            adapter_data=adapter_data,
+        )
         hidden_state = hidden_state.reshape(
             batch_size * num_concurrent_media,
             num_tiles,
@@ -854,12 +954,12 @@ def create_layer(layer_id, prefix, config, weights):
         self.dtype = weights.dtype
         self.device = weights.device
 
-    def vision_forward(self, pixel_values, aspect_ratio_ids, aspect_ratio_mask):
+    def vision_forward(self, pixel_values, aspect_ratio_ids, aspect_ratio_mask, adapter_data):
         if aspect_ratio_ids is None:
             raise ValueError("`aspect_ratio_ids` must be provided if `pixel_values` is provided")
         # logger.info(f"PIxel values {pixel_values.shape}")
         batch_size = pixel_values.shape[0]
-        vision_states = self.vision_model(pixel_values, aspect_ratio_ids, aspect_ratio_mask)
+        vision_states = self.vision_model(pixel_values, aspect_ratio_ids, aspect_ratio_mask, adapter_data)
         cross_attention_states = self.multi_modal_projector(vision_states).reshape(
             -1, vision_states.shape[-2], self.hidden_size
         )