Merge branch 'main' into evals_5

.gitignore

@@ -13,6 +13,7 @@ xcuserdata/
 Package.resolved
 *.pte
 *.ipynb_checkpoints*
+.idea
 .venv/
 .idea
 _build

distributions/README.md

@@ -7,6 +7,7 @@ A Distribution is where APIs and Providers are assembled together to provide a c
 | **Distribution** 	|           **Llama Stack Docker**           	| Start This Distribution 	|    **Inference**   	|     **Agents**     	|     **Memory**     	|     **Safety**     	|    **Telemetry**   	|
 |:----------------:	|:------------------------------------------:	|:-----------------------:	|:------------------:	|:------------------:	|:------------------:	|:------------------:	|:------------------:	|
 |  Meta Reference  	| [llamastack/distribution-meta-reference-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-gpu/general) 	|       [Guide](./meta-reference-gpu/)       	| meta-reference 	| meta-reference 	| meta-reference; remote::pgvector; remote::chromadb	| meta-reference 	| meta-reference	|
+|  Meta Reference Quantized  	| [llamastack/distribution-meta-reference-quantized-gpu](https://hub.docker.com/repository/docker/llamastack/distribution-meta-reference-quantized-gpu/general) 	|       [Guide](./meta-reference-quantized-gpu/)       	| meta-reference-quantized 	| meta-reference 	| meta-reference; remote::pgvector; remote::chromadb	| meta-reference 	| meta-reference	|
 |      Ollama      	|       [llamastack/distribution-ollama](https://hub.docker.com/repository/docker/llamastack/distribution-ollama/general)       	|       [Guide](./ollama/)       	| remote::ollama	| meta-reference 	| remote::pgvector; remote::chromadb 	| remote::ollama 	| meta-reference 	|
 |        TGI       	|         [llamastack/distribution-tgi](https://hub.docker.com/repository/docker/llamastack/distribution-tgi/general)        	|       [Guide](./tgi/)       	| remote::tgi	| meta-reference 	| meta-reference; remote::pgvector; remote::chromadb 	| meta-reference 	| meta-reference 	|
 |        Together       	|         [llamastack/distribution-together](https://hub.docker.com/repository/docker/llamastack/distribution-together/general)        	|       [Guide](./together/)       	| remote::together 	| meta-reference | remote::weaviate | meta-reference 	| meta-reference  	|

distributions/meta-reference-gpu/build.yaml

@@ -1,5 +1,6 @@
 name: meta-reference-gpu
 distribution_spec:
+  docker_image: pytorch/pytorch
   description: Use code from `llama_stack` itself to serve all llama stack APIs
   providers:
     inference: meta-reference

distributions/meta-reference-quantized-gpu/README.md

@@ -0,0 +1,34 @@
+# Meta Reference Quantized Distribution
+
+The `llamastack/distribution-meta-reference-quantized-gpu` distribution consists of the following provider configurations.
+
+
+| **API**         	| **Inference**            	| **Agents**     	| **Memory**                                       	| **Safety**     	| **Telemetry**  	|
+|-----------------	|------------------------  	|----------------	|--------------------------------------------------	|----------------	|----------------	|
+| **Provider(s)** 	| meta-reference-quantized  | meta-reference 	| meta-reference, remote::pgvector, remote::chroma 	| meta-reference 	| meta-reference 	|
+
+The only difference vs. the `meta-reference-gpu` distribution is that it has support for more efficient inference -- with fp8, int4 quantization, etc.
+
+### Start the Distribution (Single Node GPU)
+
+> [!NOTE]
+> This assumes you have access to GPU to start a local server with access to your GPU.
+
+
+> [!NOTE]
+> `~/.llama` should be the path containing downloaded weights of Llama models.
+
+
+To download and start running a pre-built docker container, you may use the following commands:
+
+```
+docker run -it -p 5000:5000 -v ~/.llama:/root/.llama \
+  -v ./run.yaml:/root/my-run.yaml \
+  --gpus=all \
+  distribution-meta-reference-quantized-gpu \
+  --yaml_config /root/my-run.yaml
+```
+
+### Alternative (Build and start distribution locally via conda)
+
+- You may checkout the [Getting Started](../../docs/getting_started.md) for more details on building locally via conda and starting up the distribution.

distributions/meta-reference-quantized-gpu/build.yaml

@@ -0,0 +1,14 @@
+name: meta-reference-quantized-gpu
+distribution_spec:
+  docker_image: pytorch/pytorch:2.5.0-cuda12.4-cudnn9-runtime
+  description: Use code from `llama_stack` itself to serve all llama stack APIs
+  providers:
+    inference: meta-reference-quantized
+    memory:
+    - meta-reference
+    - remote::chromadb
+    - remote::pgvector
+    safety: meta-reference
+    agents: meta-reference
+    telemetry: meta-reference
+image_type: docker

distributions/meta-reference-quantized-gpu/run.yaml

@@ -0,0 +1,51 @@
+version: '2'
+built_at: '2024-10-08T17:40:45.325529'
+image_name: local
+docker_image: null
+conda_env: local
+apis:
+- shields
+- agents
+- models
+- memory
+- memory_banks
+- inference
+- safety
+providers:
+  inference:
+  - provider_id: meta0
+    provider_type: meta-reference-quantized
+    config:
+      model: Llama3.2-3B-Instruct
+      quantization:
+        type: fp8
+      torch_seed: null
+      max_seq_len: 2048
+      max_batch_size: 1
+  safety:
+  - provider_id: meta0
+    provider_type: meta-reference
+    config:
+      llama_guard_shield:
+        model: Llama-Guard-3-1B
+        excluded_categories: []
+        disable_input_check: false
+        disable_output_check: false
+      prompt_guard_shield:
+        model: Prompt-Guard-86M
+  memory:
+  - provider_id: meta0
+    provider_type: meta-reference
+    config: {}
+  agents:
+  - provider_id: meta0
+    provider_type: meta-reference
+    config:
+      persistence_store:
+        namespace: null
+        type: sqlite
+        db_path: ~/.llama/runtime/kvstore.db
+  telemetry:
+  - provider_id: meta0
+    provider_type: meta-reference
+    config: {}

llama_stack/apis/inference/client.py

@@ -172,7 +172,7 @@ async def run_mm_main(
         ],
     )
     cprint(f"User>{message.content}", "green")
-    iterator = client.chat_completion(
+    iterator = await client.chat_completion(
         model=model,
         messages=[message],
         stream=stream,

llama_stack/apis/inference/inference.py

@@ -25,6 +25,7 @@ class LogProbConfig(BaseModel):
 class QuantizationType(Enum):
     bf16 = "bf16"
     fp8 = "fp8"
+    int4 = "int4"
 
 
 @json_schema_type
@@ -37,8 +38,14 @@ class Bf16QuantizationConfig(BaseModel):
     type: Literal[QuantizationType.bf16.value] = QuantizationType.bf16.value
 
 
+@json_schema_type
+class Int4QuantizationConfig(BaseModel):
+    type: Literal[QuantizationType.int4.value] = QuantizationType.int4.value
+    scheme: Optional[str] = None
+
+
 QuantizationConfig = Annotated[
-    Union[Bf16QuantizationConfig, Fp8QuantizationConfig],
+    Union[Bf16QuantizationConfig, Fp8QuantizationConfig, Int4QuantizationConfig],
     Field(discriminator="type"),
 ]
 
@@ -219,8 +226,6 @@ class Inference(Protocol):
         logprobs: Optional[LogProbConfig] = None,
     ) -> Union[CompletionResponse, CompletionResponseStreamChunk]: ...
 
-    # This method is not `async def` because it can result in either an
-    # `AsyncGenerator` or a `ChatCompletionResponse` depending on the value of `stream`.
     @webmethod(route="/inference/chat_completion")
     async def chat_completion(
         self,

llama_stack/distribution/build_container.sh

@@ -97,7 +97,7 @@ if [ -n "$pip_dependencies" ]; then
 fi
 
 if [ -n "$special_pip_deps" ]; then
-  IFS='#' read -ra parts <<< "$special_pip_deps"
+  IFS='#' read -ra parts <<<"$special_pip_deps"
   for part in "${parts[@]}"; do
     add_to_docker "RUN pip install $part"
   done
@@ -127,7 +127,7 @@ if [ -n "$LLAMA_MODELS_DIR" ]; then
   mounts="$mounts -v $(readlink -f $LLAMA_MODELS_DIR):$models_mount"
 fi
 
-if command -v selinuxenabled &> /dev/null && selinuxenabled; then
+if command -v selinuxenabled &>/dev/null && selinuxenabled; then
   # Disable SELinux labels -- we don't want to relabel the llama-stack source dir
   DOCKER_OPTS="$DOCKER_OPTS --security-opt label=disable"
 fi
@@ -139,4 +139,4 @@ $DOCKER_BINARY build $DOCKER_OPTS -t $image_name -f "$TEMP_DIR/Dockerfile" "$REP
 rm -rf $REPO_CONFIGS_DIR
 set +x
 
-echo "Success! You can run it with: $DOCKER_BINARY $DOCKER_OPTS run -p 5000:5000 $image_name"
+echo "Success!"

llama_stack/providers/impls/meta_reference/inference/generation.py

@@ -30,7 +30,6 @@ from llama_models.llama3.reference_impl.multimodal.model import (
     CrossAttentionTransformer,
 )
 from llama_models.sku_list import resolve_model
-
 from pydantic import BaseModel
 from termcolor import cprint
 
@@ -43,7 +42,12 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
     chat_completion_request_to_messages,
 )
 
-from .config import MetaReferenceInferenceConfig, MetaReferenceQuantizedInferenceConfig
+from .config import (
+    Fp8QuantizationConfig,
+    Int4QuantizationConfig,
+    MetaReferenceInferenceConfig,
+    MetaReferenceQuantizedInferenceConfig,
+)
 
 
 def model_checkpoint_dir(model) -> str:
@@ -131,7 +135,9 @@ class Llama:
         ), f"model_args vocab = {model_args.vocab_size} but tokenizer vocab = {tokenizer.n_words}"
 
         if isinstance(config, MetaReferenceQuantizedInferenceConfig):
-            from .quantization.loader import convert_to_quantized_model
+
+            if isinstance(config.quantization, Fp8QuantizationConfig):
+                from .quantization.loader import convert_to_fp8_quantized_model
 
                 # load on CPU in bf16 so that fp8 conversion does not find an
                 # unexpected (fp32, e.g.) datatype
@@ -142,7 +148,21 @@ class Llama:
                 else:
                     model = Transformer(model_args)
                 model.load_state_dict(state_dict, strict=False)
-            model = convert_to_quantized_model(model, config, ckpt_dir)
+                model = convert_to_fp8_quantized_model(model, config, ckpt_dir)
+            elif isinstance(config.quantization, Int4QuantizationConfig):
+                from .quantization.loader import convert_to_int4_quantized_model
+
+                assert (
+                    config.quantization.scheme is not None
+                ), "Please specify a quantization scheme."
+
+                model = Transformer(model_args)
+                model = convert_to_int4_quantized_model(model, model_args, config)
+                model.load_state_dict(state_dict, strict=True)
+            else:
+                raise NotImplementedError(
+                    "Currently int4 and fp8 are the only supported quantization methods."
+                )
         else:
             if torch.cuda.is_bf16_supported():
                 torch.set_default_tensor_type(torch.cuda.BFloat16Tensor)

llama_stack/providers/impls/meta_reference/inference/quantization/loader.py

@@ -8,19 +8,25 @@
 # This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
 
 import os
-from typing import Optional
+from typing import Any, Dict, List, Optional
 
 import torch
 
+from fairscale.nn.model_parallel.layers import ColumnParallelLinear, RowParallelLinear
 from fairscale.nn.model_parallel.mappings import reduce_from_model_parallel_region
-from llama_models.datatypes import CheckpointQuantizationFormat
-from llama_models.llama3.reference_impl.model import Transformer, TransformerBlock
 
+from llama_models.datatypes import CheckpointQuantizationFormat
+
+from llama_models.llama3.api.args import ModelArgs
+from llama_models.llama3.reference_impl.model import Transformer, TransformerBlock
 from llama_models.sku_list import resolve_model
 from termcolor import cprint
-from torch import Tensor
+from torch import nn, Tensor
+
+from torchao.quantization.GPTQ import Int8DynActInt4WeightLinear
 
 from llama_stack.apis.inference import QuantizationType
+from llama_stack.apis.inference.inference import Int4QuantizationConfig
 
 from llama_stack.providers.impls.meta_reference.inference.config import (
     MetaReferenceQuantizedInferenceConfig,
@@ -37,7 +43,7 @@ def swiglu_wrapper(
     return reduce_from_model_parallel_region(out)
 
 
-def convert_to_quantized_model(
+def convert_to_fp8_quantized_model(
     model: Transformer,
     config: MetaReferenceQuantizedInferenceConfig,
     checkpoint_dir: str,
@@ -99,3 +105,241 @@ def convert_to_quantized_model(
         if not isinstance(parameter, Fp8ScaledWeights):
             parameter.data = parameter.to(device="cuda")
     return model
+
+
+class Int8DynActInt4WeightLinearLoRA(Int8DynActInt4WeightLinear):
+    """
+    Int8DynActInt4WeightLinear with LoRA adaptor.
+
+    Args:
+        in_features: Number of input features.
+        out_features: Number of output features.
+        bias: Whether to use bias.
+        device: Device to use.
+        group_size: Group size for quantization.
+        precision: Precision of quantization.
+        scales_precision: Precision of scales.
+        lora_rank: Rank of LoRA adaptor.
+        lora_scale: Scale of LoRA adaptor.
+    """
+
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        bias=False,
+        device=None,
+        # quantization parameters
+        group_size: int = 256,
+        precision: torch.dtype = torch.float32,
+        scales_precision: torch.dtype = torch.float32,
+        # LoRA parameters
+        lora_rank: Optional[int] = None,
+        lora_scale: Optional[float] = None,
+    ) -> None:
+        super().__init__(
+            in_features,
+            out_features,
+            bias=bias,
+            device=device,
+            groupsize=group_size,
+            precision=precision,
+            scales_precision=scales_precision,
+        )
+        if lora_rank is not None:
+            assert lora_scale is not None, "Please specify lora scale for LoRA."
+            # Low-rank adaptation. See paper for more details: https://arxiv.org/abs/2106.09685
+            self.adaptor = nn.Sequential()
+            self.adaptor.add_module("A", nn.Linear(in_features, lora_rank, bias=False))
+            self.adaptor.add_module("B", nn.Linear(lora_rank, out_features, bias=False))
+            self.lora_scale = lora_scale
+        else:
+            self.adaptor = None
+            self.lora_scale = None
+        self._register_load_state_dict_pre_hook(self.load_hook)
+
+    def load_hook(
+        self,
+        state_dict: Dict[str, Any],
+        prefix: str,
+        local_metadata: Dict[str, Any],
+        strict: bool,
+        missing_keys: List[str],
+        unexpected_keys: List[str],
+        error_msgs: List[str],
+    ) -> None:
+        """A hook to load the quantized weights from the state dict."""
+        if prefix + "zeros" not in state_dict:
+            # Zero-point may not be saved in the state dict. In this case, we assume it's zero.
+            assert prefix + "scales" in state_dict
+            state_dict[prefix + "zeros"] = torch.zeros_like(
+                state_dict[prefix + "scales"]
+            )
+
+    def forward(self, input_: torch.Tensor) -> torch.Tensor:
+        module_out = super().forward(input_)
+        if self.adaptor is not None:
+            adaptor_out = self.adaptor(input_) * self.lora_scale
+            return module_out + adaptor_out
+        return module_out
+
+
+class Int8WeightEmbedding(torch.nn.Embedding):
+    """An embedding layer to load int8 weights.
+
+    Args:
+        num_embeddings: Number of embeddings.
+        embedding_dim: Embedding dimension.
+        padding_idx: Padding index.
+    """
+
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        padding_idx: int,
+        device=None,
+    ) -> None:
+        super().__init__(num_embeddings, embedding_dim, padding_idx, device=device)
+
+        self._register_load_state_dict_pre_hook(self.load_hook)
+
+    def load_hook(
+        self,
+        state_dict: Dict[str, Any],
+        prefix: str,
+        local_metadata: Dict[str, Any],
+        strict: bool,
+        missing_keys: List[str],
+        unexpected_keys: List[str],
+        error_msgs: List[str],
+    ) -> None:
+        """A hook to load the quantized embedding weight and scales from the state dict."""
+        weights = state_dict.pop(prefix + "weight")
+        scales = state_dict.pop(prefix + "scales")
+        state_dict[prefix + "weight"] = weights * scales
+
+
+class Int8WeightLinear(torch.nn.Linear):
+    """A linear layer to load int8 weights.
+
+    Args:
+        in_features: Number of input features.
+        out_features: Number of output features.
+        bias: Whether to use bias.
+    """
+
+    def __init__(
+        self, in_features: int, out_features: int, bias: bool = True, device=None
+    ) -> None:
+        super().__init__(in_features, out_features, bias, device=device)
+
+        self._register_load_state_dict_pre_hook(self.load_hook)
+
+    def load_hook(
+        self,
+        state_dict: Dict[str, Any],
+        prefix: str,
+        local_metadata: Dict[str, Any],
+        strict: bool,
+        missing_keys: List[str],
+        unexpected_keys: List[str],
+        error_msgs: List[str],
+    ) -> None:
+        """A hook to load the quantized linear weight and scales from the state dict."""
+        weights = state_dict.pop(prefix + "weight")
+        scales = state_dict.pop(prefix + "scales")
+        state_dict[prefix + "weight"] = weights * scales
+
+
+def _prepare_model_int4_weight_int8_dynamic_activation(
+    model: torch.nn.Module,
+    group_size: int,
+    lora_rank: Optional[int],
+    lora_scale: Optional[float],
+):
+    """Prepare the model for int4 weight and int8 dynamic activation quantization.
+
+    Note that the weights of embedding and output layers are quantized to int8.
+    """
+    device = None
+    for module_name, module in model.named_children():
+        if module_name == "output":
+            quantized_module = Int8WeightLinear(
+                in_features=module.in_features,
+                out_features=module.out_features,
+                bias=module.bias,
+                device=device,
+            )
+            del module
+            setattr(model, module_name, quantized_module)
+        elif module_name == "tok_embeddings":
+            quantized_module = Int8WeightEmbedding(
+                num_embeddings=module.num_embeddings,
+                embedding_dim=module.embedding_dim,
+                padding_idx=module.padding_idx,
+                device=device,
+            )
+            del module
+            setattr(model, module_name, quantized_module)
+        elif isinstance(module, (ColumnParallelLinear, RowParallelLinear, nn.Linear)):
+            quantized_module = Int8DynActInt4WeightLinearLoRA(
+                in_features=module.in_features,
+                out_features=module.out_features,
+                bias=False,
+                group_size=group_size,
+                lora_rank=lora_rank,
+                lora_scale=lora_scale,
+                device=device,
+            )
+            del module
+            setattr(model, module_name, quantized_module)
+        else:
+            _prepare_model_int4_weight_int8_dynamic_activation(
+                module, group_size, lora_rank, lora_scale
+            )
+
+    return model
+
+
+def convert_to_int4_quantized_model(
+    model: Transformer,
+    model_args: ModelArgs,
+    config: MetaReferenceQuantizedInferenceConfig,
+) -> Transformer:
+    """Convert the model to int4 quantized model."""
+
+    quant_config = config.quantization
+    if not isinstance(quant_config, Int4QuantizationConfig):
+        raise ValueError("Only int4 quantization is supported")
+
+    if quant_config.type != QuantizationType.int4.value:
+        raise ValueError("Only int4 quantization is supported")
+
+    if quant_config.scheme != "int4_weight_int8_dynamic_activation":
+        raise NotImplementedError(
+            "Only int4 quantization with 'int4_weight_int8_dynamic_activation' scheme is supported."
+        )
+
+    if model_args.quantization_args is None:
+        raise ValueError("'quantization_args' cannot be None. Please specify it.")
+
+    group_size = model_args.quantization_args.group_size
+    if group_size is None:
+        raise ValueError(
+            "'group_size' cannot be None in 'quantization_args'. Please specify it."
+        )
+
+    if model_args.lora_args is None:
+        # Certain quantized models (e.g., SpinQuant) may not have LoRA.
+        lora_rank = None
+        lora_scale = None
+    else:
+        lora_rank = model_args.lora_args.rank
+        lora_scale = model_args.lora_args.scale
+
+    _prepare_model_int4_weight_int8_dynamic_activation(
+        model, group_size, lora_rank, lora_scale
+    )
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    return model.to(device)

llama_stack/providers/registry/inference.py

@@ -36,7 +36,8 @@ def available_providers() -> List[ProviderSpec]:
             pip_packages=(
                 META_REFERENCE_DEPS
                 + [
-                    "fbgemm-gpu==0.8.0",
+                    "fbgemm-gpu",
+                    "torchao==0.5.0",
                 ]
             ),
             module="llama_stack.providers.impls.meta_reference.inference",

requirements.txt

@@ -2,7 +2,7 @@ blobfile
 fire
 httpx
 huggingface-hub
-llama-models>=0.0.43
+llama-models>=0.0.44
 prompt-toolkit
 python-dotenv
 pydantic>=2

setup.py

@@ -16,7 +16,7 @@ def read_requirements():
 
 setup(
     name="llama_stack",
-    version="0.0.43",
+    version="0.0.44",
     author="Meta Llama",
     author_email="llama-oss@meta.com",
     description="Llama Stack",