feat: Add AML deployment

.dockerignore

@@ -1 +1,2 @@
+aml
 router/target

aml/README.md

@@ -0,0 +1,7 @@
+```shell
+docker build . -t db4c2190dd824d1f950f5d1555fbadf0.azurecr.io/text-generation:0.1
+docker push db4c2190dd824d1f950f5d1555fbadf0.azurecr.io/text-generation:0.1
+
+az ml online-endpoint create -f endpoint.yaml -g HuggingFace-BLOOM-ModelPage -w HuggingFace
+az ml online-deployment create -f deployment.yaml -g HuggingFace-BLOOM-ModelPage -w HuggingFace
+```

aml/deployment.yaml

@@ -0,0 +1,39 @@
+$schema: https://azuremlschemas.azureedge.net/latest/managedOnlineDeployment.schema.json
+name: bloom-deployment
+endpoint_name: bloom-inference
+model:
+  name: bloom
+  path: ./bloom
+model_mount_path: /var/azureml-model
+environment_variables:
+  MODEL_BASE_PATH: /var/azureml-model/bloom
+  MODEL_NAME: bigscience/bloom
+  NUM_GPUS: 8
+environment:
+  image: db4c2190dd824d1f950f5d1555fbadf0.azurecr.io/text-generation:0.1
+  inference_config:
+    liveness_route:
+      port: 3000
+      path: /health
+    readiness_route:
+      port: 3000
+      path: /health
+    scoring_route:
+      port: 3000
+      path: /generate
+instance_type: Standard_ND96amsr_A100_v4
+request_settings:
+  request_timeout_ms: 90000
+liveness_probe:
+  initial_delay: 300
+  timeout: 20
+  period: 60
+  success_threshold: 1
+  failure_threshold: 60
+readiness_probe:
+  initial_delay: 300
+  timeout: 20
+  period: 60
+  success_threshold: 1
+  failure_threshold: 60
+instance_count: 1

aml/endpoint.yaml

@@ -0,0 +1,3 @@
+$schema: https://azuremlsdk2.blob.core.windows.net/latest/managedOnlineEndpoint.schema.json
+name: bloom-inference
+auth_mode: aml_token

router/src/server.rs

@@ -1,7 +1,8 @@
-use crate::{Batcher, ShardedClient, Validation};
+use bloom_inference_client::ShardedClient;
+use crate::{Batcher, Validation};
 use axum::extract::Extension;
 use axum::http::StatusCode;
-use axum::routing::post;
+use axum::routing::{get, post};
 use axum::{Json, Router};
 use serde::Deserialize;
 use std::net::SocketAddr;
@@ -142,7 +143,7 @@ pub async fn run(client: ShardedClient, tokenizer: Tokenizer, addr: SocketAddr)
     let app = Router::new()
         .route("/generate", post(generate))
         .layer(Extension(shared_state.clone()))
-        .route("/health", post(liveness))
+        .route("/health", get(liveness))
         .layer(Extension(shared_state.clone()));
 
     axum::Server::bind(&addr)

server/bloom_inference/model.py

@@ -9,7 +9,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
 from transformers.modeling_utils import no_init_weights
 
 from bloom_inference.pb import generate_pb2
-from bloom_inference.shard_model import shard_model, match_suffix
+from bloom_inference.prepare_weights import prepare_weights, match_suffix
 from bloom_inference.utils import (
     StoppingCriteria,
     NextTokenChooser,
@@ -377,8 +377,8 @@ class BLOOMSharded(BLOOM):
         # shard state_dict
         if self.master:
             # TODO @thomasw21 do some caching
-            shard_state_dict_paths = shard_model(
-                model_name, shard_directory, tp_world_size=self.world_size, dtype=dtype
+            shard_state_dict_paths = prepare_weights(
+                model_name, shard_directory / "cache", shard_directory, tp_world_size=self.world_size
             )
             shard_state_dict_paths = [
                 str(path.absolute()) for path in shard_state_dict_paths

server/bloom_inference/prepare_weights.py

@@ -62,7 +62,7 @@ def prepare_weights(model_name: str, cache_path: Path, save_path: Path, tp_world
 
     if all(save_path.exists() for save_path in save_paths):
         print("Weights are already prepared")
-        return
+        return save_paths
 
     cache_path.mkdir(parents=True, exist_ok=True)
     if model_name == "bigscience/bloom-560m":