[MODEL] LoRA support for Jamba model (#11209)

Signed-off-by: Erez Schwartz <[email protected]>

Author: ErezSC42

tests/lora/conftest.py

@@ -4,6 +4,7 @@
 from unittest.mock import MagicMock, patch
 
 import pytest
+import safetensors
 import torch
 import torch.nn as nn
 from huggingface_hub import snapshot_download
@@ -169,6 +170,29 @@ def mixtral_lora_files_all_target_modules():
     return snapshot_download(repo_id="dyang415/mixtral-lora-v0")
 
 
+@pytest.fixture(scope="session")
+def jamba_lora_files():
+    #   some of the adapters have unnecessary weights for serving,
+    #   hence we remove them
+    def remove_unnecessary_weights(path):
+        lora_path = f"{adapter_path}/adapter_model.safetensors"
+        tensors = safetensors.torch.load_file(lora_path)
+        nonlora_keys = []
+        for k in list(tensors.keys()):
+            if "lora" not in k:
+                nonlora_keys.append(k)
+        for k in nonlora_keys:
+            del tensors[k]
+        safetensors.torch.save_file(tensors, lora_path)
+
+    adapter_path = snapshot_download(
+        repo_id=
+        "hf-100/Jamba-1.5-mini-Spellbound-StoryWriter-0.1-6583896-ckpt53-lora")
+
+    remove_unnecessary_weights(adapter_path)
+    return adapter_path
+
+
 @pytest.fixture(scope="session")
 def gemma_lora_files():
     return snapshot_download(repo_id="wskwon/gemma-7b-test-lora")

tests/lora/test_jamba.py

@@ -0,0 +1,54 @@
+from typing import List
+
+import pytest
+import torch
+
+import vllm
+from vllm.lora.request import LoRARequest
+
+MODEL_PATH = "ai21labs/AI21-Jamba-1.5-Mini"
+
+MAX_TOKENS = 40
+
+
+def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int,
+              prompts: List[str]) -> List[str]:
+
+    sampling_params = vllm.SamplingParams(temperature=0, max_tokens=MAX_TOKENS)
+    outputs = llm.generate(
+        prompts,
+        sampling_params,
+        lora_request=LoRARequest(str(lora_id), lora_id, lora_path)
+        if lora_id else None)
+    # Print the outputs.
+    generated_texts: List[str] = []
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text.strip()
+        generated_texts.append(generated_text)
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    return generated_texts
+
+
+@pytest.mark.parametrize("tp_size", [4])
+def test_jamba_lora(jamba_lora_files, tp_size):
+    """Original test, the LoRA model has the common target modules, not all"""
+    if torch.cuda.device_count() < tp_size:
+        pytest.skip(f"Not enough GPUs for tensor parallelism {tp_size}")
+
+    prompts = ["Write a story about a sheep and a goat."]
+
+    llm = vllm.LLM(
+        MODEL_PATH,
+        enable_lora=True,
+        max_num_seqs=16,
+        max_loras=4,
+        distributed_executor_backend="ray",
+        tensor_parallel_size=tp_size,
+    )
+
+    expected_jamba_output = [
+        """Once upon a time, in a lush green meadow, there lived a sheep named Clara and a goat named Billy. Clara was a gentle creature, always nibbling on the soft grass and humming"""  # noqa: E501
+    ]
+    assert do_sample(llm, jamba_lora_files, lora_id=1,
+                     prompts=prompts) == expected_jamba_output

vllm/model_executor/layers/mamba/mamba_mixer.py

@@ -42,12 +42,14 @@ def __init__(self,
                  use_rms_norm: bool,
                  rms_norm_has_weight: bool = True,
                  rms_norm_eps: float = 1e-5,
-                 activation="silu"):
+                 activation="silu",
+                 is_lora_enabled: bool = False):
         super().__init__()
         self.time_step_rank = time_step_rank
         self.ssm_state_size = ssm_state_size
         self.use_rms_norm = use_rms_norm
         self.activation = activation
+        self.is_lora_enabled = is_lora_enabled
 
         self.conv1d = ColumnParallelLinear(
             input_size=conv_kernel_size,
@@ -63,6 +65,7 @@ def __init__(self,
         self.in_proj = MergedColumnParallelLinear(hidden_size,
                                                   [intermediate_size] * 2,
                                                   bias=use_bias)
+
         # selective projection used to make dt, B and C input dependent
         self.x_proj = RowParallelLinear(
             intermediate_size,
@@ -170,7 +173,13 @@ def forward_cuda(self, hidden_states: torch.Tensor,
 
         # 3. State Space Model sequence transformation
         # 3.a. input varying initialization of time_step, B and C
-        ssm_parameters = self.x_proj(hidden_states.transpose(-2, -1))[0]
+
+        if self.is_lora_enabled:
+            #   lora kernel requires contiguous tensor
+            ssm_parameters = self.x_proj(
+                hidden_states.transpose(-2, -1).contiguous())[0]
+        else:
+            ssm_parameters = self.x_proj(hidden_states.transpose(-2, -1))[0]
 
         time_step, B, C = torch.split(
             ssm_parameters,
@@ -222,6 +231,11 @@ def forward_cuda(self, hidden_states: torch.Tensor,
             scan_outputs = scan_outputs.transpose(0, 1)
 
         # 4. Final linear projection
-        contextualized_states = self.out_proj(scan_outputs.transpose(-2,
-                                                                     -1))[0]
+        if self.is_lora_enabled:
+            #  lora kernel requires contiguous tensor
+            contextualized_states = self.out_proj(
+                scan_outputs.transpose(-2, -1).contiguous())[0]
+        else:
+            contextualized_states = self.out_proj(
+                scan_outputs.transpose(-2, -1))[0]
         return contextualized_states

vllm/model_executor/models/jamba.py

@@ -107,9 +107,11 @@ def __init__(self,
                  layer_idx: int,
                  cache_config: Optional[CacheConfig] = None,
                  quant_config: Optional[QuantizationConfig] = None,
-                 prefix: str = "") -> None:
+                 is_lora_enabled: Optional[bool] = False,
+                 **kwargs) -> None:
         super().__init__()
         self.config = config
+        self.is_lora_enabled = is_lora_enabled
         self.mamba = MambaMixer(hidden_size= config.hidden_size,
                                 ssm_state_size = config.mamba_d_state,
                                 conv_kernel_size = config.mamba_d_conv,
@@ -120,7 +122,9 @@ def __init__(self,
                                 use_bias = config.mamba_proj_bias,
                                 use_rms_norm=True,
                                 rms_norm_eps=config.rms_norm_eps,
-                                activation=config.hidden_act)
+                                activation=config.hidden_act,
+                                is_lora_enabled = self.is_lora_enabled
+                                )
 
         num_experts = config.layers_num_experts[layer_idx]
         ffn_layer_class = JambaMoE if num_experts > 1 else JambaMLP
@@ -156,14 +160,13 @@ def forward(
 
 class JambaAttentionDecoderLayer(nn.Module):
 
-    def __init__(
-        self,
-        config: JambaConfig,
-        layer_idx: int,
-        cache_config: Optional[CacheConfig] = None,
-        quant_config: Optional[QuantizationConfig] = None,
-        prefix: str = "",
-    ) -> None:
+    def __init__(self,
+                 config: JambaConfig,
+                 layer_idx: int,
+                 cache_config: Optional[CacheConfig] = None,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = "",
+                 **kwargs) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
         tp_size = get_tensor_model_parallel_world_size()
@@ -287,17 +290,18 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
             org_num_embeddings=config.vocab_size,
         )
 
+        extra_kwargs = {"is_lora_enabled": bool(vllm_config.lora_config)}
+
         def get_layer(prefix: str):
             layer_idx = int(prefix.rsplit(".", 1)[1])
             layer_class = ALL_DECODER_LAYER_TYPES[
                 config.layers_block_type[layer_idx]]
-            return layer_class(
-                config,
-                layer_idx,
-                cache_config,
-                quant_config=quant_config,
-                prefix=prefix,
-            )
+            return layer_class(config,
+                               layer_idx,
+                               cache_config,
+                               quant_config=quant_config,
+                               prefix=prefix,
+                               **extra_kwargs)
 
         self.start_layer, self.end_layer, self.layers = make_layers(
             config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers")
@@ -371,14 +375,13 @@ class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
             "k_proj",
             "v_proj",
         ],
+        "in_proj": ["in_proj"],
     }
 
     # LoRA specific attributes
     supported_lora_modules = [
-        "qkv_proj",
-        "o_proj",
-        "embed_tokens",
-        "lm_head",
+        "qkv_proj", "o_proj", "embed_tokens", "lm_head", "up_proj",
+        "down_proj", "gate_proj", "out_proj", "in_proj", "x_proj"
     ]
     embedding_modules = {
         "embed_tokens": "input_embeddings",
@@ -423,9 +426,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.make_empty_intermediate_tensors = (
             self.model.make_empty_intermediate_tensors)
         if self.scheduler_config is not None and \
-            not self.model_config.enforce_eager:
+                not self.model_config.enforce_eager:
             if self.scheduler_config.max_num_seqs > \
-                vllm_config.compilation_config.max_capture_size:
+                    vllm_config.compilation_config.max_capture_size:
                 self.max_batch_size = \
                     vllm_config.compilation_config.max_capture_size
             else:
@@ -446,7 +449,6 @@ def forward(self,
                 inputs_embeds: Optional[torch.Tensor] = None,
                 **kwargs):
         if self.mamba_cache is None:
-
             num_mamba_layers = self.model_config.get_num_layers_by_block_type(
                 self.vllm_config.parallel_config, LayerBlockType.mamba)
             self.mamba_cache = MambaCacheManager(

vllm/model_executor/models/mamba.py

@@ -38,10 +38,12 @@ class MambaDecoderLayer(nn.Module):
     def __init__(self,
                  config: MambaConfig,
                  cache_config: Optional[CacheConfig] = None,
-                 quant_config: Optional[QuantizationConfig] = None) -> None:
+                 quant_config: Optional[QuantizationConfig] = None,
+                 is_lora_enabled: Optional[bool] = False) -> None:
         super().__init__()
         self.config = config
         self.is_falcon_mamba = config.model_type == "falcon_mamba"
+        self.is_lora_enabled = is_lora_enabled
         mixer_rms_eps = config.mixer_rms_eps if self.is_falcon_mamba else None
         self.mixer = MambaMixer(hidden_size=config.hidden_size,
                                 ssm_state_size=config.state_size,
@@ -53,7 +55,8 @@ def __init__(self,
                                 use_rms_norm=self.is_falcon_mamba,
                                 rms_norm_has_weight=not self.is_falcon_mamba,
                                 rms_norm_eps=mixer_rms_eps,
-                                activation=config.hidden_act)
+                                activation=config.hidden_act,
+                                is_lora_enabled=self.is_lora_enabled)
 
         self.norm = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
 
@@ -85,6 +88,7 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
+        is_lora_enabled = bool(lora_config)
 
         self.config = config
         self.padding_idx = config.pad_token_id
@@ -101,8 +105,10 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
 
         self.start_layer, self.end_layer, self.layers = make_layers(
             config.num_hidden_layers,
-            lambda prefix: MambaDecoderLayer(
-                config, cache_config=cache_config, quant_config=quant_config),
+            lambda prefix: MambaDecoderLayer(config,
+                                             cache_config=cache_config,
+                                             quant_config=quant_config,
+                                             is_lora_enabled=is_lora_enabled),
             prefix=f"{prefix}.layers")
 
         self.norm_f = RMSNorm(config.hidden_size,