Merge branch 'gptq-cuda-kernels' of https://github.com/fxmarty/text-generation-inference into gptq-cuda-kernels

Cargo.toml

@@ -8,7 +8,7 @@ members = [
 ]
 
 [workspace.package]
-version = "0.8.2"
+version = "0.9.0"
 edition = "2021"
 authors = ["Olivier Dehaene"]
 homepage = "https://github.com/huggingface/text-generation-inference"

Dockerfile

@@ -1,5 +1,5 @@
 # Rust builder
-FROM lukemathwalker/cargo-chef:latest-rust-1.69 AS chef
+FROM lukemathwalker/cargo-chef:latest-rust-1.70 AS chef
 WORKDIR /usr/src
 
 ARG CARGO_REGISTRIES_CRATES_IO_PROTOCOL=sparse
@@ -88,7 +88,6 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-ins
 RUN /opt/conda/bin/conda install -c "nvidia/label/cuda-11.8.0"  cuda==11.8 && \
     /opt/conda/bin/conda clean -ya
 
-
 # Build Flash Attention CUDA kernels
 FROM kernel-builder as flash-att-builder
 
@@ -109,6 +108,16 @@ COPY server/custom_kernels/ .
 # Build specific version of transformers
 RUN python setup.py build
 
+# Build vllm CUDA kernels
+FROM kernel-builder as vllm-builder
+
+WORKDIR /usr/src
+
+COPY server/Makefile-vllm Makefile
+
+# Build specific version of vllm
+RUN make build-vllm
+
 # Text Generation Inference base image
 FROM nvidia/cuda:11.8.0-base-ubuntu20.04 as base
 
@@ -137,9 +146,12 @@ COPY --from=flash-att-builder /usr/src/flash-attention/build/lib.linux-x86_64-cp
 COPY --from=flash-att-builder /usr/src/flash-attention/csrc/layer_norm/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages
 COPY --from=flash-att-builder /usr/src/flash-attention/csrc/rotary/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages
 
-# Copy build artifacts from transformers builder
+# Copy build artifacts from custom kernels builder
 COPY --from=custom-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-39/custom_kernels /usr/src/custom-kernels/src/custom_kernels
 
+# Copy builds artifacts from vllm builder
+COPY --from=vllm-builder /usr/src/vllm/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages
+
 # Install flash-attention dependencies
 RUN pip install einops --no-cache-dir
 

README.md

@@ -43,8 +43,8 @@ to power LLMs api-inference widgets.
 - Tensor Parallelism for faster inference on multiple GPUs
 - Token streaming using Server-Sent Events (SSE)
 - [Continuous batching of incoming requests](https://github.com/huggingface/text-generation-inference/tree/main/router) for increased total throughput
-- Optimized transformers code for inference using [flash-attention](https://github.com/HazyResearch/flash-attention) on the most popular architectures
-- Quantization with [bitsandbytes](https://github.com/TimDettmers/bitsandbytes)
+- Optimized transformers code for inference using [flash-attention](https://github.com/HazyResearch/flash-attention) and [Paged Attention](https://github.com/vllm-project/vllm) on the most popular architectures
+- Quantization with [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) and [GPT-Q](https://arxiv.org/abs/2210.17323)
 - [Safetensors](https://github.com/huggingface/safetensors) weight loading
 - Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
 - Logits warper (temperature scaling, top-p, top-k, repetition penalty, more details see [transformers.LogitsProcessor](https://huggingface.co/docs/transformers/internal/generation_utils#transformers.LogitsProcessor))
@@ -84,7 +84,7 @@ model=bigscience/bloom-560m
 num_shard=2
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
 
-docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:0.8 --model-id $model --num-shard $num_shard
+docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:0.9 --model-id $model --num-shard $num_shard
 ```
 **Note:** To use GPUs, you need to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). We also recommend using NVIDIA drivers with CUDA version 11.8 or higher.
 

aml/README.md

@@ -1,15 +0,0 @@
-# Azure ML endpoint
-
-## Create all resources
-
-```shell
-az ml model create -f model.yaml -g HuggingFace-BLOOM-ModelPage -w HuggingFace
-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
-```
-
-## Update deployment
-
-```shell
-az ml online-deployment update -f deployment.yaml -g HuggingFace-BLOOM-ModelPage -w HuggingFace
-```

aml/deployment.yaml

@@ -1,38 +0,0 @@
-$schema: https://azuremlschemas.azureedge.net/latest/managedOnlineDeployment.schema.json
-name: bloom-deployment
-endpoint_name: bloom-inference
-model: azureml:bloom-safetensors:1
-model_mount_path: /var/azureml-model
-environment_variables:
-  WEIGHTS_CACHE_OVERRIDE: /var/azureml-model/bloom-safetensors
-  MODEL_ID: bigscience/bloom
-  NUM_SHARD: 8
-environment:
-  image: db4c2190dd824d1f950f5d1555fbadf0.azurecr.io/text-generation-inference:0.2.0
-  inference_config:
-    liveness_route:
-      port: 80
-      path: /health
-    readiness_route:
-      port: 80
-      path: /health
-    scoring_route:
-      port: 80
-      path: /generate
-instance_type: Standard_ND96amsr_A100_v4
-request_settings:
-  request_timeout_ms: 90000
-  max_concurrent_requests_per_instance: 256
-liveness_probe:
-  initial_delay: 600
-  timeout: 90
-  period: 120
-  success_threshold: 1
-  failure_threshold: 5
-readiness_probe:
-  initial_delay: 600
-  timeout: 90
-  period: 120
-  success_threshold: 1
-  failure_threshold: 5
-instance_count: 1

aml/endpoint.yaml

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

aml/model.yaml

@@ -1,3 +0,0 @@
-$schema: https://azuremlschemas.azureedge.net/latest/model.schema.json
-name: bloom-safetensors
-path: /data/bloom-safetensors

benchmark/src/main.rs

@@ -14,36 +14,85 @@ use tracing_subscriber::EnvFilter;
 #[derive(Parser, Debug)]
 #[clap(author, version, about, long_about = None)]
 struct Args {
+    /// The name of the tokenizer (as in model_id on the huggingface hub, or local path).
     #[clap(short, long, env)]
     tokenizer_name: String,
+
+    /// The revision to use for the tokenizer if on the hub.
     #[clap(default_value = "main", long, env)]
     revision: String,
+
+    /// The various batch sizes to benchmark for, the idea is to get enough
+    /// batching to start seeing increased latency, this usually means you're
+    /// moving from memory bound (usual as BS=1) to compute bound, and this is
+    /// a sweet spot for the maximum batch size for the model under test
     #[clap(short, long)]
     batch_size: Option<Vec<u32>>,
+
+    /// This is the initial prompt sent to the text-generation-server length
+    /// in token. Longer prompt will slow down the benchmark. Usually the
+    /// latency grows somewhat linearly with this for the prefill step.
+    ///
+    /// Most importantly, the prefill step is usually not the one dominating
+    /// your runtime, so it's ok to keep it short.
     #[clap(default_value = "10", short, long, env)]
     sequence_length: u32,
+
+    /// This is how many tokens will be generated by the server and averaged out
+    /// to give the `decode` latency. This is the *critical* number you want to optimize for
+    /// LLM spend most of their time doing decoding.
+    ///
+    /// Decode latency is usually quite stable.
     #[clap(default_value = "8", short, long, env)]
     decode_length: u32,
+
+    ///How many runs should we average from
     #[clap(default_value = "10", short, long, env)]
     runs: usize,
+
+    /// Number of warmup cycles
     #[clap(default_value = "1", short, long, env)]
     warmups: usize,
-    #[clap(long, env)]
-    temperature: Option<f32>,
-    #[clap(long, env)]
-    top_k: Option<u32>,
-    #[clap(long, env)]
-    top_p: Option<f32>,
-    #[clap(long, env)]
-    typical_p: Option<f32>,
-    #[clap(long, env)]
-    repetition_penalty: Option<f32>,
-    #[clap(long, env)]
-    watermark: bool,
-    #[clap(long, env)]
-    do_sample: bool,
+
+    /// The location of the grpc socket. This benchmark tool bypasses the router
+    /// completely and directly talks to the gRPC processes
     #[clap(default_value = "/tmp/text-generation-server-0", short, long, env)]
     master_shard_uds_path: String,
+
+    /// Generation parameter in case you want to specifically test/debug particular
+    /// decoding strategies, for full doc refer to the `text-generation-server`
+    #[clap(long, env)]
+    temperature: Option<f32>,
+
+    /// Generation parameter in case you want to specifically test/debug particular
+    /// decoding strategies, for full doc refer to the `text-generation-server`
+    #[clap(long, env)]
+    top_k: Option<u32>,
+
+    /// Generation parameter in case you want to specifically test/debug particular
+    /// decoding strategies, for full doc refer to the `text-generation-server`
+    #[clap(long, env)]
+    top_p: Option<f32>,
+
+    /// Generation parameter in case you want to specifically test/debug particular
+    /// decoding strategies, for full doc refer to the `text-generation-server`
+    #[clap(long, env)]
+    typical_p: Option<f32>,
+
+    /// Generation parameter in case you want to specifically test/debug particular
+    /// decoding strategies, for full doc refer to the `text-generation-server`
+    #[clap(long, env)]
+    repetition_penalty: Option<f32>,
+
+    /// Generation parameter in case you want to specifically test/debug particular
+    /// decoding strategies, for full doc refer to the `text-generation-server`
+    #[clap(long, env)]
+    watermark: bool,
+
+    /// Generation parameter in case you want to specifically test/debug particular
+    /// decoding strategies, for full doc refer to the `text-generation-server`
+    #[clap(long, env)]
+    do_sample: bool,
 }
 
 fn main() -> Result<(), Box<dyn std::error::Error>> {

docs/openapi.json

@@ -10,7 +10,7 @@
       "name": "Apache 2.0",
       "url": "https://www.apache.org/licenses/LICENSE-2.0"
     },
-    "version": "0.8.2"
+    "version": "0.9.0"
   },
   "paths": {
     "/": {
@@ -270,6 +270,35 @@
         }
       }
     },
+    "/health": {
+      "get": {
+        "tags": [
+          "Text Generation Inference"
+        ],
+        "summary": "Health check method",
+        "description": "Health check method",
+        "operationId": "health",
+        "responses": {
+          "200": {
+            "description": "Everything is working fine"
+          },
+          "503": {
+            "description": "Text generation inference is down",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                },
+                "example": {
+                  "error": "unhealthy",
+                  "error_type": "healthcheck"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
     "/info": {
       "get": {
         "tags": [

integration-tests/models/__snapshots__/test_mpt/test_mpt.json

@@ -0,0 +1,140 @@
+{
+  "details": {
+    "best_of_sequences": null,
+    "finish_reason": "length",
+    "generated_tokens": 17,
+    "prefill": [
+      {
+        "id": 1276,
+        "logprob": null,
+        "text": "What"
+      },
+      {
+        "id": 310,
+        "logprob": -1.5117188,
+        "text": " is"
+      },
+      {
+        "id": 18147,
+        "logprob": -8.96875,
+        "text": " Deep"
+      },
+      {
+        "id": 20727,
+        "logprob": -1.953125,
+        "text": " Learning"
+      },
+      {
+        "id": 32,
+        "logprob": -0.94189453,
+        "text": "?"
+      }
+    ],
+    "seed": null,
+    "tokens": [
+      {
+        "id": 428,
+        "logprob": -1.5830078,
+        "special": false,
+        "text": " -"
+      },
+      {
+        "id": 18147,
+        "logprob": -3.3105469,
+        "special": false,
+        "text": " Deep"
+      },
+      {
+        "id": 20727,
+        "logprob": -0.3215332,
+        "special": false,
+        "text": " Learning"
+      },
+      {
+        "id": 187,
+        "logprob": -2.5566406,
+        "special": false,
+        "text": "\n"
+      },
+      {
+        "id": 30763,
+        "logprob": -1.6074219,
+        "special": false,
+        "text": "Deep"
+      },
+      {
+        "id": 20727,
+        "logprob": -0.69628906,
+        "special": false,
+        "text": " Learning"
+      },
+      {
+        "id": 310,
+        "logprob": -0.6923828,
+        "special": false,
+        "text": " is"
+      },
+      {
+        "id": 247,
+        "logprob": -0.5263672,
+        "special": false,
+        "text": " a"
+      },
+      {
+        "id": 749,
+        "logprob": -1.8544922,
+        "special": false,
+        "text": " sub"
+      },
+      {
+        "id": 3423,
+        "logprob": -0.6118164,
+        "special": false,
+        "text": "field"
+      },
+      {
+        "id": 273,
+        "logprob": -0.055877686,
+        "special": false,
+        "text": " of"
+      },
+      {
+        "id": 5145,
+        "logprob": -1.0537109,
+        "special": false,
+        "text": " machine"
+      },
+      {
+        "id": 4715,
+        "logprob": -0.0115737915,
+        "special": false,
+        "text": " learning"
+      },
+      {
+        "id": 326,
+        "logprob": -0.9111328,
+        "special": false,
+        "text": " that"
+      },
+      {
+        "id": 4648,
+        "logprob": -1.4589844,
+        "special": false,
+        "text": " uses"
+      },
+      {
+        "id": 13345,
+        "logprob": -1.4853516,
+        "special": false,
+        "text": " artificial"
+      },
+      {
+        "id": 11454,
+        "logprob": -0.021636963,
+        "special": false,
+        "text": " neural"
+      }
+    ]
+  },
+  "generated_text": " - Deep Learning\nDeep Learning is a subfield of machine learning that uses artificial neural"
+}

integration-tests/models/__snapshots__/test_mpt/test_mpt_load.json

@@ -0,0 +1,562 @@
+[
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 17,
+      "prefill": [
+        {
+          "id": 1276,
+          "logprob": null,
+          "text": "What"
+        },
+        {
+          "id": 310,
+          "logprob": -1.5117188,
+          "text": " is"
+        },
+        {
+          "id": 18147,
+          "logprob": -8.96875,
+          "text": " Deep"
+        },
+        {
+          "id": 20727,
+          "logprob": -1.953125,
+          "text": " Learning"
+        },
+        {
+          "id": 32,
+          "logprob": -0.94189453,
+          "text": "?"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 428,
+          "logprob": -1.5830078,
+          "special": false,
+          "text": " -"
+        },
+        {
+          "id": 18147,
+          "logprob": -3.3183594,
+          "special": false,
+          "text": " Deep"
+        },
+        {
+          "id": 20727,
+          "logprob": -0.32617188,
+          "special": false,
+          "text": " Learning"
+        },
+        {
+          "id": 187,
+          "logprob": -2.5742188,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 30763,
+          "logprob": -1.6015625,
+          "special": false,
+          "text": "Deep"
+        },
+        {
+          "id": 20727,
+          "logprob": -0.69628906,
+          "special": false,
+          "text": " Learning"
+        },
+        {
+          "id": 310,
+          "logprob": -0.67822266,
+          "special": false,
+          "text": " is"
+        },
+        {
+          "id": 247,
+          "logprob": -0.5395508,
+          "special": false,
+          "text": " a"
+        },
+        {
+          "id": 749,
+          "logprob": -1.8623047,
+          "special": false,
+          "text": " sub"
+        },
+        {
+          "id": 3423,
+          "logprob": -0.6020508,
+          "special": false,
+          "text": "field"
+        },
+        {
+          "id": 273,
+          "logprob": -0.0552063,
+          "special": false,
+          "text": " of"
+        },
+        {
+          "id": 5145,
+          "logprob": -1.0742188,
+          "special": false,
+          "text": " machine"
+        },
+        {
+          "id": 4715,
+          "logprob": -0.011405945,
+          "special": false,
+          "text": " learning"
+        },
+        {
+          "id": 326,
+          "logprob": -0.9165039,
+          "special": false,
+          "text": " that"
+        },
+        {
+          "id": 4648,
+          "logprob": -1.4501953,
+          "special": false,
+          "text": " uses"
+        },
+        {
+          "id": 13345,
+          "logprob": -1.4960938,
+          "special": false,
+          "text": " artificial"
+        },
+        {
+          "id": 11454,
+          "logprob": -0.02116394,
+          "special": false,
+          "text": " neural"
+        }
+      ]
+    },
+    "generated_text": " - Deep Learning\nDeep Learning is a subfield of machine learning that uses artificial neural"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 17,
+      "prefill": [
+        {
+          "id": 1276,
+          "logprob": null,
+          "text": "What"
+        },
+        {
+          "id": 310,
+          "logprob": -1.5,
+          "text": " is"
+        },
+        {
+          "id": 18147,
+          "logprob": -8.984375,
+          "text": " Deep"
+        },
+        {
+          "id": 20727,
+          "logprob": -1.96875,
+          "text": " Learning"
+        },
+        {
+          "id": 32,
+          "logprob": -0.93359375,
+          "text": "?"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 428,
+          "logprob": -1.5800781,
+          "special": false,
+          "text": " -"
+        },
+        {
+          "id": 18147,
+          "logprob": -3.3242188,
+          "special": false,
+          "text": " Deep"
+        },
+        {
+          "id": 20727,
+          "logprob": -0.31835938,
+          "special": false,
+          "text": " Learning"
+        },
+        {
+          "id": 187,
+          "logprob": -2.5644531,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 30763,
+          "logprob": -1.5957031,
+          "special": false,
+          "text": "Deep"
+        },
+        {
+          "id": 20727,
+          "logprob": -0.69628906,
+          "special": false,
+          "text": " Learning"
+        },
+        {
+          "id": 310,
+          "logprob": -0.68603516,
+          "special": false,
+          "text": " is"
+        },
+        {
+          "id": 247,
+          "logprob": -0.5258789,
+          "special": false,
+          "text": " a"
+        },
+        {
+          "id": 749,
+          "logprob": -1.859375,
+          "special": false,
+          "text": " sub"
+        },
+        {
+          "id": 3423,
+          "logprob": -0.6166992,
+          "special": false,
+          "text": "field"
+        },
+        {
+          "id": 273,
+          "logprob": -0.056762695,
+          "special": false,
+          "text": " of"
+        },
+        {
+          "id": 5145,
+          "logprob": -1.0703125,
+          "special": false,
+          "text": " machine"
+        },
+        {
+          "id": 4715,
+          "logprob": -0.011428833,
+          "special": false,
+          "text": " learning"
+        },
+        {
+          "id": 326,
+          "logprob": -0.9213867,
+          "special": false,
+          "text": " that"
+        },
+        {
+          "id": 4648,
+          "logprob": -1.4726562,
+          "special": false,
+          "text": " uses"
+        },
+        {
+          "id": 13345,
+          "logprob": -1.5039062,
+          "special": false,
+          "text": " artificial"
+        },
+        {
+          "id": 11454,
+          "logprob": -0.021652222,
+          "special": false,
+          "text": " neural"
+        }
+      ]
+    },
+    "generated_text": " - Deep Learning\nDeep Learning is a subfield of machine learning that uses artificial neural"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 17,
+      "prefill": [
+        {
+          "id": 1276,
+          "logprob": null,
+          "text": "What"
+        },
+        {
+          "id": 310,
+          "logprob": -1.5,
+          "text": " is"
+        },
+        {
+          "id": 18147,
+          "logprob": -8.984375,
+          "text": " Deep"
+        },
+        {
+          "id": 20727,
+          "logprob": -1.96875,
+          "text": " Learning"
+        },
+        {
+          "id": 32,
+          "logprob": -0.93359375,
+          "text": "?"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 428,
+          "logprob": -1.5800781,
+          "special": false,
+          "text": " -"
+        },
+        {
+          "id": 18147,
+          "logprob": -3.3242188,
+          "special": false,
+          "text": " Deep"
+        },
+        {
+          "id": 20727,
+          "logprob": -0.31835938,
+          "special": false,
+          "text": " Learning"
+        },
+        {
+          "id": 187,
+          "logprob": -2.5644531,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 30763,
+          "logprob": -1.5957031,
+          "special": false,
+          "text": "Deep"
+        },
+        {
+          "id": 20727,
+          "logprob": -0.69628906,
+          "special": false,
+          "text": " Learning"
+        },
+        {
+          "id": 310,
+          "logprob": -0.68603516,
+          "special": false,
+          "text": " is"
+        },
+        {
+          "id": 247,
+          "logprob": -0.5258789,
+          "special": false,
+          "text": " a"
+        },
+        {
+          "id": 749,
+          "logprob": -1.859375,
+          "special": false,
+          "text": " sub"
+        },
+        {
+          "id": 3423,
+          "logprob": -0.6166992,
+          "special": false,
+          "text": "field"
+        },
+        {
+          "id": 273,
+          "logprob": -0.056762695,
+          "special": false,
+          "text": " of"
+        },
+        {
+          "id": 5145,
+          "logprob": -1.0703125,
+          "special": false,
+          "text": " machine"
+        },
+        {
+          "id": 4715,
+          "logprob": -0.011428833,
+          "special": false,
+          "text": " learning"
+        },
+        {
+          "id": 326,
+          "logprob": -0.9213867,
+          "special": false,
+          "text": " that"
+        },
+        {
+          "id": 4648,
+          "logprob": -1.4726562,
+          "special": false,
+          "text": " uses"
+        },
+        {
+          "id": 13345,
+          "logprob": -1.5039062,
+          "special": false,
+          "text": " artificial"
+        },
+        {
+          "id": 11454,
+          "logprob": -0.021652222,
+          "special": false,
+          "text": " neural"
+        }
+      ]
+    },
+    "generated_text": " - Deep Learning\nDeep Learning is a subfield of machine learning that uses artificial neural"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 17,
+      "prefill": [
+        {
+          "id": 1276,
+          "logprob": null,
+          "text": "What"
+        },
+        {
+          "id": 310,
+          "logprob": -1.5,
+          "text": " is"
+        },
+        {
+          "id": 18147,
+          "logprob": -8.984375,
+          "text": " Deep"
+        },
+        {
+          "id": 20727,
+          "logprob": -1.96875,
+          "text": " Learning"
+        },
+        {
+          "id": 32,
+          "logprob": -0.93359375,
+          "text": "?"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 428,
+          "logprob": -1.5800781,
+          "special": false,
+          "text": " -"
+        },
+        {
+          "id": 18147,
+          "logprob": -3.3242188,
+          "special": false,
+          "text": " Deep"
+        },
+        {
+          "id": 20727,
+          "logprob": -0.31835938,
+          "special": false,
+          "text": " Learning"
+        },
+        {
+          "id": 187,
+          "logprob": -2.5644531,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 30763,
+          "logprob": -1.5957031,
+          "special": false,
+          "text": "Deep"
+        },
+        {
+          "id": 20727,
+          "logprob": -0.69628906,
+          "special": false,
+          "text": " Learning"
+        },
+        {
+          "id": 310,
+          "logprob": -0.68603516,
+          "special": false,
+          "text": " is"
+        },
+        {
+          "id": 247,
+          "logprob": -0.5258789,
+          "special": false,
+          "text": " a"
+        },
+        {
+          "id": 749,
+          "logprob": -1.859375,
+          "special": false,
+          "text": " sub"
+        },
+        {
+          "id": 3423,
+          "logprob": -0.6166992,
+          "special": false,
+          "text": "field"
+        },
+        {
+          "id": 273,
+          "logprob": -0.056762695,
+          "special": false,
+          "text": " of"
+        },
+        {
+          "id": 5145,
+          "logprob": -1.0703125,
+          "special": false,
+          "text": " machine"
+        },
+        {
+          "id": 4715,
+          "logprob": -0.011428833,
+          "special": false,
+          "text": " learning"
+        },
+        {
+          "id": 326,
+          "logprob": -0.9213867,
+          "special": false,
+          "text": " that"
+        },
+        {
+          "id": 4648,
+          "logprob": -1.4726562,
+          "special": false,
+          "text": " uses"
+        },
+        {
+          "id": 13345,
+          "logprob": -1.5039062,
+          "special": false,
+          "text": " artificial"
+        },
+        {
+          "id": 11454,
+          "logprob": -0.021652222,
+          "special": false,
+          "text": " neural"
+        }
+      ]
+    },
+    "generated_text": " - Deep Learning\nDeep Learning is a subfield of machine learning that uses artificial neural"
+  }
+]

integration-tests/models/test_flash_neox.py

@@ -13,6 +13,7 @@ async def flash_neox(flash_neox_handle):
     return flash_neox_handle.client
 
 
+@pytest.mark.skip
 @pytest.mark.asyncio
 async def test_flash_neox(flash_neox, response_snapshot):
     response = await flash_neox.generate(
@@ -25,6 +26,7 @@ async def test_flash_neox(flash_neox, response_snapshot):
     assert response == response_snapshot
 
 
+@pytest.mark.skip
 @pytest.mark.asyncio
 async def test_flash_neox_load(flash_neox, generate_load, response_snapshot):
     responses = await generate_load(

integration-tests/models/test_mpt.py

@@ -0,0 +1,48 @@
+import pytest
+
+
+@pytest.fixture(scope="module")
+def mpt_sharded_handle(launcher):
+    with launcher("mosaicml/mpt-7b", num_shard=2) as handle:
+        yield handle
+
+
+@pytest.fixture(scope="module")
+async def mpt_sharded(mpt_sharded_handle):
+    await mpt_sharded_handle.health(300)
+    return mpt_sharded_handle.client
+
+
+@pytest.mark.asyncio
+async def test_mpt(mpt_sharded, response_snapshot):
+    response = await mpt_sharded.generate(
+        "What is Deep Learning?",
+        max_new_tokens=17,
+        decoder_input_details=True,
+    )
+
+    assert response.details.generated_tokens == 17
+    assert (
+        response.generated_text
+        == " - Deep Learning\nDeep Learning is a subfield of machine learning that uses artificial neural"
+    )
+    assert response == response_snapshot
+
+
+@pytest.mark.asyncio
+async def test_mpt_load(mpt_sharded, generate_load, response_snapshot):
+    responses = await generate_load(
+        mpt_sharded,
+        "What is Deep Learning?",
+        max_new_tokens=17,
+        n=4,
+    )
+
+    assert len(responses) == 4
+    assert all([r.generated_text == responses[0].generated_text for r in responses])
+    assert (
+        responses[0].generated_text
+        == " - Deep Learning\nDeep Learning is a subfield of machine learning that uses artificial neural"
+    )
+
+    assert responses == response_snapshot

launcher/src/main.rs

@@ -6,8 +6,7 @@ use std::io::{BufRead, BufReader, Read};
 use std::path::Path;
 use std::sync::atomic::{AtomicBool, Ordering};
 use std::sync::mpsc::TryRecvError;
-use std::sync::Arc;
-use std::sync::{mpsc, Mutex};
+use std::sync::{mpsc, Arc};
 use std::thread;
 use std::thread::sleep;
 use std::time::{Duration, Instant};
@@ -40,6 +39,25 @@ impl std::fmt::Display for Quantization {
     }
 }
 
+#[derive(Clone, Copy, Debug, ValueEnum)]
+enum Dtype {
+    Float16,
+    BFloat16,
+}
+
+impl std::fmt::Display for Dtype {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        // To keep in track with `server`.
+        match self {
+            Dtype::Float16 => {
+                write!(f, "float16")
+            }
+            Dtype::BFloat16 => {
+                write!(f, "bfloat16")
+            }
+        }
+    }
+}
 
 /// App Configuration
 #[derive(Parser, Debug)]
@@ -76,6 +94,10 @@ struct Args {
     #[clap(long, env, value_enum)]
     quantize: Option<Quantization>,
 
+    /// The dtype to be forced upon the model. This option cannot be used with `--quantize`.
+    #[clap(long, env, value_enum)]
+    dtype: Option<Dtype>,
+
     /// Whether you want to execute hub modelling code. Explicitly passing a `revision` is
     /// encouraged when loading a model with custom code to ensure no malicious code has been
     /// contributed in a newer revision.
@@ -106,7 +128,7 @@ struct Args {
     /// for users. The larger this value, the longer prompt users can send which
     /// can impact the overall memory required to handle the load.
     /// Please note that some models have a finite range of sequence they can handle.
-    #[clap(default_value = "1000", long, env)]
+    #[clap(default_value = "1024", long, env)]
     max_input_length: usize,
 
     /// This is the most important value to set as it defines the "memory budget"
@@ -117,15 +139,9 @@ struct Args {
     /// `1511` max_new_tokens.
     /// The larger this value, the larger amount each request will be in your RAM
     /// and the less effective batching can be.
-    #[clap(default_value = "1512", long, env)]
+    #[clap(default_value = "2048", long, env)]
     max_total_tokens: usize,
 
-    /// The maximum allowed batch size during dynamic batching.
-    /// Using `max_batch_total_tokens` should be favored in general
-    /// as it's a finer way to control RAM usage.
-    #[clap(long, env)]
-    max_batch_size: Option<usize>,
-
     /// This represents the ratio of waiting queries vs running queries where
     /// you want to start considering pausing the running queries to include the waiting
     /// ones into the same batch.
@@ -139,6 +155,12 @@ struct Args {
     #[clap(default_value = "1.2", long, env)]
     waiting_served_ratio: f32,
 
+    /// Limits the number of tokens for the prefill operation.
+    /// Since this operation take the most memory and is compute bound, it is interesting
+    /// to limit the number of requests that can be sent.
+    #[clap(default_value = "4096", long, env)]
+    max_batch_prefill_tokens: u32,
+
     /// **IMPORTANT** This is one critical control to allow maximum usage
     /// of the available hardware.
     ///
@@ -151,19 +173,12 @@ struct Args {
     /// For `max_batch_total_tokens=1000`, you could fit `10` queries of `total_tokens=100`
     /// or a single query of `1000` tokens.
     ///
-    /// So you don't have to control that finely
-    /// `max_batch_size` or `max_total_tokens`. In fact you could mostly relax them if you
-    /// want maximum flexibility. However, for your users if they are asking for the full amount of
-    /// total tokens, they are likely to wait for a very long time to get a spot
-    /// in the batch (since they are going to be alone) so setting `max_batch_size`
-    /// and `max_total_tokens` can still be useful to prevent those long waiting times.
-    ///
     /// Overall this number should be the largest possible amount that fits the
     /// remaining memory (after the model is loaded). Since the actual memory overhead
     /// depends on other parameters like if you're using quantization, flash attention
     /// or the model implementation, text-generation-inference cannot infer this number
     /// automatically.
-    #[clap(default_value = "32000", long, env)]
+    #[clap(default_value = "16000", long, env)]
     max_batch_total_tokens: u32,
 
     /// This setting defines how many tokens can be passed before forcing the waiting
@@ -185,9 +200,9 @@ struct Args {
     /// for end users.
     #[clap(default_value = "20", long, env)]
     max_waiting_tokens: usize,
-    #[clap(default_value = "3000", long, short, env)]
 
     /// The port to listen on.
+    #[clap(default_value = "3000", long, short, env)]
     port: u16,
 
     /// The name of the socket for gRPC communication between the webserver
@@ -262,7 +277,7 @@ struct Args {
 #[derive(Debug)]
 enum ShardStatus {
     Ready,
-    Failed((usize, String)),
+    Failed((usize, Option<String>)),
 }
 
 #[allow(clippy::too_many_arguments)]
@@ -270,6 +285,7 @@ fn shard_manager(
     model_id: String,
     revision: Option<String>,
     quantize: Option<Quantization>,
+    dtype: Option<Dtype>,
     trust_remote_code: bool,
     uds_path: String,
     rank: usize,
@@ -283,7 +299,7 @@ fn shard_manager(
     watermark_delta: Option<f32>,
     otlp_endpoint: Option<String>,
     status_sender: mpsc::Sender<ShardStatus>,
-    shutdown: Arc<Mutex<bool>>,
+    shutdown: Arc<AtomicBool>,
     _shutdown_sender: mpsc::Sender<()>,
 ) {
     // Get UDS path
@@ -319,6 +335,11 @@ fn shard_manager(
         shard_argv.push(quantize.to_string())
     }
 
+    if let Some(dtype) = dtype {
+        shard_argv.push("--dtype".to_string());
+        shard_argv.push(dtype.to_string())
+    }
+
     // Model optional revision
     if let Some(revision) = revision {
         shard_argv.push("--revision".to_string());
@@ -334,6 +355,12 @@ fn shard_manager(
     // Copy current process env
     let mut env: Vec<(OsString, OsString)> = env::vars_os().collect();
 
+    // Use cuda allocator. It leads to less memory fragmentation
+    env.push((
+        "PYTORCH_CUDA_ALLOC_CONF".into(),
+        "backend:cudaMallocAsync".into(),
+    ));
+
     // Torch Distributed Env vars
     env.push(("RANK".into(), rank.to_string().into()));
     env.push(("WORLD_SIZE".into(), world_size.to_string().into()));
@@ -409,20 +436,20 @@ fn shard_manager(
                 }
             }
             status_sender
-                .send(ShardStatus::Failed((rank, err.to_string())))
+                .send(ShardStatus::Failed((rank, Some(err.to_string()))))
                 .unwrap();
             return;
         }
     };
 
     // Redirect STDOUT to the console
-    let shard_stdout = p.stdout.take().unwrap();
+    let shard_stdout_reader = BufReader::new(p.stdout.take().unwrap());
+    let mut shard_stderr_reader = BufReader::new(p.stderr.take().unwrap());
 
     thread::spawn(move || {
         // Enter shard-manager tracing span
-        let stdout = BufReader::new(shard_stdout);
         let _span = tracing::span!(tracing::Level::INFO, "shard-manager", rank = rank).entered();
-        for line in stdout.lines() {
+        for line in shard_stdout_reader.lines() {
             // Parse loguru logs
             if let Ok(log) = serde_json::from_str::<PythonLogMessage>(&line.unwrap()) {
                 log.trace();
@@ -436,8 +463,22 @@ fn shard_manager(
     loop {
         // Process exited
         if let Some(exit_status) = p.poll() {
-            let mut err = String::new();
-            p.stderr.take().unwrap().read_to_string(&mut err).unwrap();
+            // We read stderr in another thread as it seems that `read_to_string` can block
+            // indefinitely in some cases
+            let (err_sender, err_receiver) = mpsc::channel();
+            thread::spawn(move || {
+                let mut err = String::new();
+                shard_stderr_reader.read_to_string(&mut err).unwrap();
+                err_sender.send(err).unwrap_or(());
+            });
+
+            let err = err_receiver
+                .recv_timeout(Duration::from_millis(100))
+                .map_err(|err| {
+                    tracing::error!("Unable to read shard {rank} error from stderr");
+                    err
+                })
+                .ok();
 
             if let ExitStatus::Signaled(signal) = exit_status {
                 tracing::error!("Shard process was signaled to shutdown with signal {signal}");
@@ -450,8 +491,8 @@ fn shard_manager(
         }
 
         // We received a shutdown signal
-        if *shutdown.lock().unwrap() {
-            p.terminate().unwrap();
+        if shutdown.load(Ordering::SeqCst) {
+            p.kill().unwrap();
             let _ = p.wait_timeout(Duration::from_secs(90));
             tracing::info!("Shard {rank} terminated");
             return;
@@ -470,14 +511,11 @@ fn shard_manager(
     }
 }
 
-fn shutdown_shards(shutdown: Arc<Mutex<bool>>, shutdown_receiver: &mpsc::Receiver<()>) {
+fn shutdown_shards(shutdown: Arc<AtomicBool>, shutdown_receiver: &mpsc::Receiver<()>) {
     tracing::info!("Shutting down shards");
     // Update shutdown value to true
     // This will be picked up by the shard manager
-    {
-        let mut shutdown = shutdown.lock().unwrap();
-        *shutdown = true;
-    }
+    shutdown.store(true, Ordering::SeqCst);
 
     // Wait for shards to shutdown
     // This will block till all shutdown_sender are dropped
@@ -719,7 +757,7 @@ fn download_convert_model(args: &Args, running: Arc<AtomicBool>) -> Result<(), L
 fn spawn_shards(
     num_shard: usize,
     args: &Args,
-    shutdown: Arc<Mutex<bool>>,
+    shutdown: Arc<AtomicBool>,
     shutdown_receiver: &mpsc::Receiver<()>,
     shutdown_sender: mpsc::Sender<()>,
     status_receiver: &mpsc::Receiver<ShardStatus>,
@@ -749,6 +787,7 @@ fn spawn_shards(
         let shutdown_sender = shutdown_sender.clone();
         let otlp_endpoint = args.otlp_endpoint.clone();
         let quantize = args.quantize;
+        let dtype = args.dtype;
         let trust_remote_code = args.trust_remote_code;
         let master_port = args.master_port;
         let disable_custom_kernels = args.disable_custom_kernels;
@@ -759,6 +798,7 @@ fn spawn_shards(
                 model_id,
                 revision,
                 quantize,
+                dtype,
                 trust_remote_code,
                 uds_path,
                 rank,
@@ -793,7 +833,10 @@ fn spawn_shards(
                 sleep(Duration::from_millis(100));
             }
             Ok(ShardStatus::Failed((rank, err))) => {
-                tracing::error!("Shard {} failed to start:\n{}", rank, err);
+                tracing::error!("Shard {rank} failed to start");
+                if let Some(err) = err {
+                    tracing::error!("{err}");
+                }
                 shutdown_shards(shutdown, shutdown_receiver);
                 return Err(LauncherError::ShardCannotStart);
             }
@@ -809,7 +852,7 @@ fn spawn_shards(
 
 fn spawn_webserver(
     args: Args,
-    shutdown: Arc<Mutex<bool>>,
+    shutdown: Arc<AtomicBool>,
     shutdown_receiver: &mpsc::Receiver<()>,
 ) -> Result<Popen, LauncherError> {
     // All shard started
@@ -827,6 +870,10 @@ fn spawn_webserver(
         args.max_input_length.to_string(),
         "--max-total-tokens".to_string(),
         args.max_total_tokens.to_string(),
+        "--max-batch-prefill-tokens".to_string(),
+        args.max_batch_prefill_tokens.to_string(),
+        "--max-batch-total-tokens".to_string(),
+        args.max_batch_total_tokens.to_string(),
         "--waiting-served-ratio".to_string(),
         args.waiting_served_ratio.to_string(),
         "--max-waiting-tokens".to_string(),
@@ -839,15 +886,6 @@ fn spawn_webserver(
         args.model_id,
     ];
 
-    // Deprecate max_batch_size
-    if let Some(max_batch_size) = args.max_batch_size {
-        argv.push("--max-batch-size".to_string());
-        argv.push(max_batch_size.to_string())
-    } else {
-        argv.push("--max-batch-total-tokens".to_string());
-        argv.push(args.max_batch_total_tokens.to_string())
-    }
-
     // Model optional revision
     if let Some(ref revision) = args.revision {
         argv.push("--revision".to_string());
@@ -981,7 +1019,7 @@ fn main() -> Result<(), LauncherError> {
     download_convert_model(&args, running.clone())?;
 
     // Shared shutdown bool
-    let shutdown = Arc::new(Mutex::new(false));
+    let shutdown = Arc::new(AtomicBool::new(false));
     // Shared shutdown channel
     // When shutting down, the main thread will wait for all senders to be dropped
     let (shutdown_sender, shutdown_receiver) = mpsc::channel();
@@ -1006,14 +1044,21 @@ fn main() -> Result<(), LauncherError> {
         return Ok(());
     }
 
-    let mut webserver = spawn_webserver(args, shutdown.clone(), &shutdown_receiver)?;
+    let mut webserver =
+        spawn_webserver(args, shutdown.clone(), &shutdown_receiver).map_err(|err| {
+            shutdown_shards(shutdown.clone(), &shutdown_receiver);
+            err
+        })?;
 
     // Default exit code
     let mut exit_code = Ok(());
 
     while running.load(Ordering::SeqCst) {
         if let Ok(ShardStatus::Failed((rank, err))) = status_receiver.try_recv() {
-            tracing::error!("Shard {rank} failed:\n{err}");
+            tracing::error!("Shard {rank} crashed");
+            if let Some(err) = err {
+                tracing::error!("{err}");
+            }
             exit_code = Err(LauncherError::ShardFailed);
             break;
         };

proto/generate.proto

@@ -11,6 +11,8 @@ service TextGenerationService {
     rpc ClearCache (ClearCacheRequest) returns (ClearCacheResponse);
     /// Remove requests from a cached batch
     rpc FilterBatch (FilterBatchRequest) returns (FilterBatchResponse);
+    /// Warmup the model and compute max cache size
+    rpc Warmup (WarmupRequest) returns (WarmupResponse);
     /// Prefill batch and decode first token
     rpc Prefill (PrefillRequest) returns (PrefillResponse);
     /// Decode token for a list of prefilled batches
@@ -192,3 +194,13 @@ message DecodeResponse {
     /// Next batch (cached)
     optional CachedBatch batch = 2;
 }
+
+message WarmupRequest {
+    /// Batch to warmup on
+    Batch batch = 1;
+    /// Maximum number of tokens that the client will send
+    uint32 max_total_tokens = 2;
+}
+
+/// Empty response
+message WarmupResponse {}

router/Cargo.toml

@@ -22,11 +22,11 @@ text-generation-client = { path = "client" }
 clap = { version = "4.1.4", features = ["derive", "env"] }
 flume = "0.10.14"
 futures = "0.3.26"
-metrics = "0.20.1"
-metrics-exporter-prometheus = { version = "0.11.0", features = [] }
+metrics = "0.21.0"
+metrics-exporter-prometheus = { version = "0.12.1", features = [] }
 nohash-hasher = "0.2.0"
-opentelemetry = { version = "0.18.0", features = ["rt-tokio"] }
-opentelemetry-otlp = "0.11.0"
+opentelemetry = { version = "0.19.0", features = ["rt-tokio"] }
+opentelemetry-otlp = "0.12.0"
 rand = "0.8.5"
 reqwest = { version = "0.11.14", features = [] }
 serde = "1.0.152"
@@ -36,7 +36,7 @@ tokenizers = "0.13.3"
 tokio = { version = "1.25.0", features = ["rt", "rt-multi-thread", "parking_lot", "signal", "sync"] }
 tower-http = { version = "0.4.0", features = ["cors"] }
 tracing = "0.1.37"
-tracing-opentelemetry = "0.18.0"
+tracing-opentelemetry = "0.19.0"
 tracing-subscriber = { version = "0.3.16", features = ["json", "env-filter"] }
 utoipa = { version = "3.0.1", features = ["axum_extras"] }
 utoipa-swagger-ui = { version = "3.0.2", features = ["axum"] }

router/client/Cargo.toml

@@ -11,10 +11,10 @@ grpc-metadata = { path = "../grpc-metadata" }
 prost = "^0.11"
 thiserror = "^1.0"
 tokio = { version = "^1.25", features = ["sync"] }
-tonic = "^0.8"
+tonic = "^0.9"
 tower = "^0.4"
 tracing = "^0.1"
 
 [build-dependencies]
-tonic-build = "0.8.4"
+tonic-build = "0.9.2"
 prost-build = "0.11.6"

router/client/src/client.rs

@@ -3,6 +3,7 @@ use crate::pb::generate::v1::text_generation_service_client::TextGenerationServi
 use crate::pb::generate::v1::*;
 use crate::Result;
 use grpc_metadata::InjectTelemetryContext;
+use std::cmp::min;
 use tonic::transport::{Channel, Uri};
 use tracing::instrument;
 
@@ -94,6 +95,63 @@ impl Client {
         Ok(filtered_batch.batch)
     }
 
+    /// Warmup on a max size batch
+    ///
+    /// Returns the maximum amount of tokens supported by the hardware
+    #[instrument(skip(self))]
+    pub async fn warmup(
+        &mut self,
+        max_input_length: u32,
+        max_prefill_tokens: u32,
+        max_total_tokens: u32,
+    ) -> Result<()> {
+        let mut n_tokens = 0;
+        let mut requests = Vec::new();
+
+        // Create requests
+        while n_tokens < max_prefill_tokens {
+            requests.push(Request {
+                id: 0,
+                // We truncate the input on the server side to be sure that it has the correct size
+                inputs: "_test ".to_string().repeat(max_input_length as usize),
+                truncate: min(max_input_length, max_prefill_tokens - n_tokens),
+                // Set sampling parameters to also take these ops into account in the max memory
+                parameters: Some(NextTokenChooserParameters {
+                    temperature: 0.9,
+                    top_k: 10,
+                    top_p: 0.9,
+                    typical_p: 0.9,
+                    do_sample: false,
+                    seed: 0,
+                    repetition_penalty: 1.2,
+                    watermark: true,
+                }),
+                stopping_parameters: Some(StoppingCriteriaParameters {
+                    max_new_tokens: 2,
+                    stop_sequences: vec![],
+                    ignore_eos_token: false,
+                }),
+                prefill_logprobs: true,
+            });
+            n_tokens += max_input_length;
+        }
+
+        let batch = Batch {
+            id: 0,
+            size: requests.len() as u32,
+            requests,
+            max_tokens: 0,
+        };
+
+        let request = tonic::Request::new(WarmupRequest {
+            batch: Some(batch),
+            max_total_tokens,
+        })
+        .inject_context();
+        self.stub.warmup(request).await?.into_inner();
+        Ok(())
+    }
+
     /// Generate one token for each request in the given batch
     ///
     /// Returns Generation for each request in batch

router/client/src/sharded_client.rs

@@ -87,6 +87,27 @@ impl ShardedClient {
         join_all(futures).await.pop().unwrap()
     }
 
+    /// Warmup on a max size batch
+    ///
+    /// Returns the maximum amount of tokens supported by the hardware
+    #[instrument(skip(self))]
+    pub async fn warmup(
+        &mut self,
+        max_input_length: u32,
+        max_prefill_tokens: u32,
+        max_total_tokens: u32,
+    ) -> Result<()> {
+        let futures: Vec<_> = self
+            .clients
+            .iter_mut()
+            .map(|client| {
+                Box::pin(client.warmup(max_input_length, max_prefill_tokens, max_total_tokens))
+            })
+            .collect();
+        // all shards return the same message
+        join_all(futures).await.pop().unwrap()
+    }
+
     /// Generate one token for each request in the given batch
     ///
     /// Returns Generation for each request in batch

router/grpc-metadata/Cargo.toml

@@ -4,7 +4,7 @@ version = "0.1.0"
 edition = "2021"
 
 [dependencies]
-opentelemetry = "0.18.0"
-tonic = "^0.8"
+opentelemetry = "^0.19"
+tonic = "^0.9"
 tracing = "^0.1"
-tracing-opentelemetry = "0.18.0"
+tracing-opentelemetry = "^0.19"

router/src/infer.rs

@@ -45,6 +45,7 @@ impl Infer {
         client: ShardedClient,
         validation: Validation,
         waiting_served_ratio: f32,
+        max_batch_prefill_tokens: u32,
         max_batch_total_tokens: u32,
         max_waiting_tokens: usize,
         max_concurrent_requests: usize,
@@ -61,6 +62,7 @@ impl Infer {
         tokio::spawn(batching_task(
             client,
             waiting_served_ratio,
+            max_batch_prefill_tokens,
             max_batch_total_tokens,
             max_waiting_tokens,
             queue.clone(),
@@ -240,9 +242,11 @@ impl Infer {
 /// Will be launched in a background Tokio task
 ///
 /// Batches requests and sends them to the inference server
+#[allow(clippy::too_many_arguments)]
 async fn batching_task(
     mut client: ShardedClient,
     waiting_served_ratio: f32,
+    max_batch_prefill_tokens: u32,
     max_batch_total_tokens: u32,
     max_waiting_tokens: usize,
     queue: Queue,
@@ -257,8 +261,9 @@ async fn batching_task(
         // Get the next batch from the queue
         // This batch might be smaller than the maximum batch size if there are not enough requests
         // waiting in the queue
-        while let Some((mut entries, batch, span)) =
-            queue.next_batch(None, max_batch_total_tokens).await
+        while let Some((mut entries, batch, span)) = queue
+            .next_batch(None, max_batch_prefill_tokens, max_batch_total_tokens)
+            .await
         {
             let mut cached_batch = prefill(&mut client, batch, &mut entries, &generation_health)
                 .instrument(span)
@@ -284,11 +289,12 @@ async fn batching_task(
                     Some((batch_size as f32 * waiting_served_ratio).floor() as usize)
                 };
 
-                let token_budget = max_batch_total_tokens - batch_max_tokens;
+                let token_budget = max_batch_total_tokens.saturating_sub(batch_max_tokens);
 
                 // Try to get a new batch
-                if let Some((mut new_entries, new_batch, span)) =
-                    queue.next_batch(min_size, token_budget).await
+                if let Some((mut new_entries, new_batch, span)) = queue
+                    .next_batch(min_size, max_batch_prefill_tokens, token_budget)
+                    .await
                 {
                     // Tracking metrics
                     if min_size.is_some() {

router/src/main.rs

@@ -28,15 +28,15 @@ struct Args {
     max_best_of: usize,
     #[clap(default_value = "4", long, env)]
     max_stop_sequences: usize,
-    #[clap(default_value = "1000", long, env)]
+    #[clap(default_value = "1024", long, env)]
     max_input_length: usize,
-    #[clap(default_value = "1512", long, env)]
+    #[clap(default_value = "2048", long, env)]
     max_total_tokens: usize,
-    #[clap(long, env)]
-    max_batch_size: Option<usize>,
     #[clap(default_value = "1.2", long, env)]
     waiting_served_ratio: f32,
-    #[clap(default_value = "32000", long, env)]
+    #[clap(default_value = "4096", long, env)]
+    max_batch_prefill_tokens: u32,
+    #[clap(default_value = "16000", long, env)]
     max_batch_total_tokens: u32,
     #[clap(default_value = "20", long, env)]
     max_waiting_tokens: usize,
@@ -78,9 +78,9 @@ fn main() -> Result<(), std::io::Error> {
         max_stop_sequences,
         max_input_length,
         max_total_tokens,
-        max_batch_size,
         waiting_served_ratio,
-        mut max_batch_total_tokens,
+        max_batch_prefill_tokens,
+        max_batch_total_tokens,
         max_waiting_tokens,
         port,
         master_shard_uds_path,
@@ -97,8 +97,18 @@ fn main() -> Result<(), std::io::Error> {
         ngrok_password,
     } = args;
 
+    // Validate args
+    if max_input_length as u32 > max_batch_prefill_tokens {
+        panic!("{}", format!("`max_batch_prefill_tokens` must be >= `max_input_length`. Given: {max_batch_prefill_tokens} and {max_input_length}"));
+    }
+    if max_batch_prefill_tokens > max_batch_total_tokens {
+        panic!("{}", format!("`max_batch_prefill_tokens` must be <= `max_batch_total_tokens`. Given: {max_batch_prefill_tokens} and {max_batch_total_tokens}"));
+    }
+    if max_total_tokens as u32 > max_batch_total_tokens {
+        panic!("{}", format!("`max_total_tokens` must be <= `max_batch_total_tokens`. Given: {max_total_tokens} and {max_batch_total_tokens}"));
+    }
     if validation_workers == 0 {
-        panic!("validation_workers must be > 0");
+        panic!("`validation_workers` must be > 0");
     }
 
     // CORS allowed origins
@@ -141,12 +151,6 @@ fn main() -> Result<(), std::io::Error> {
         .block_on(async {
             init_logging(otlp_endpoint, json_output);
 
-            if let Some(max_batch_size) = max_batch_size {
-                tracing::warn!("`max-batch-size` is deprecated. Use `max-batch-total-tokens` instead");
-                max_batch_total_tokens = (max_batch_size * max_total_tokens) as u32;
-                tracing::warn!("Overriding `max-batch-total-tokens` value with `max-batch-size` * `max-total-tokens` = {max_batch_total_tokens}");
-            }
-
             if tokenizer.is_none() {
                 tracing::warn!(
                     "Could not find a fast tokenizer implementation for {tokenizer_name}"
@@ -161,10 +165,16 @@ fn main() -> Result<(), std::io::Error> {
                     sha: None,
                     pipeline_tag: None,
                 },
-                false => get_model_info(&tokenizer_name, &revision, authorization_token).await.unwrap_or_else(|| {
-                    tracing::warn!("Could not retrieve model info from the Hugging Face hub.");
-                    HubModelInfo { model_id: tokenizer_name.to_string(), sha: None, pipeline_tag: None }
-                }),
+                false => get_model_info(&tokenizer_name, &revision, authorization_token)
+                    .await
+                    .unwrap_or_else(|| {
+                        tracing::warn!("Could not retrieve model info from the Hugging Face hub.");
+                        HubModelInfo {
+                            model_id: tokenizer_name.to_string(),
+                            sha: None,
+                            pipeline_tag: None,
+                        }
+                    }),
             };
 
             // if pipeline-tag == text-generation we default to return_full_text = true
@@ -190,6 +200,17 @@ fn main() -> Result<(), std::io::Error> {
                 .info()
                 .await
                 .expect("Unable to get shard info");
+
+            // Warmup model
+            tracing::info!("Warming up model");
+            sharded_client
+                .warmup(
+                    max_input_length as u32,
+                    max_batch_prefill_tokens,
+                    max_batch_total_tokens,
+                )
+                .await
+                .expect("Unable to warmup model");
             tracing::info!("Connected");
 
             // Binds on localhost
@@ -206,6 +227,7 @@ fn main() -> Result<(), std::io::Error> {
                 max_input_length,
                 max_total_tokens,
                 waiting_served_ratio,
+                max_batch_prefill_tokens,
                 max_batch_total_tokens,
                 max_waiting_tokens,
                 sharded_client,
@@ -219,7 +241,7 @@ fn main() -> Result<(), std::io::Error> {
                 ngrok_username,
                 ngrok_password,
             )
-                .await;
+            .await;
             Ok(())
         })
 }

router/src/queue.rs

@@ -58,6 +58,7 @@ impl Queue {
     pub(crate) async fn next_batch(
         &self,
         min_size: Option<usize>,
+        prefill_token_budget: u32,
         token_budget: u32,
     ) -> Option<NextBatch> {
         // Create response channel
@@ -67,6 +68,7 @@ impl Queue {
         self.queue_sender
             .send(QueueCommand::NextBatch {
                 min_size,
+                prefill_token_budget,
                 token_budget,
                 response_sender,
                 span: Span::current(),
@@ -90,11 +92,12 @@ async fn queue_task(requires_padding: bool, receiver: flume::Receiver<QueueComma
             }
             QueueCommand::NextBatch {
                 min_size,
+                prefill_token_budget,
                 token_budget,
                 response_sender,
                 span,
             } => span.in_scope(|| {
-                let next_batch = state.next_batch(min_size, token_budget);
+                let next_batch = state.next_batch(min_size, prefill_token_budget, token_budget);
                 response_sender.send(next_batch).unwrap();
                 metrics::gauge!("tgi_queue_size", state.entries.len() as f64);
             }),
@@ -140,7 +143,12 @@ impl State {
     }
 
     // Get the next batch
-    fn next_batch(&mut self, min_size: Option<usize>, token_budget: u32) -> Option<NextBatch> {
+    fn next_batch(
+        &mut self,
+        min_size: Option<usize>,
+        prefill_token_budget: u32,
+        token_budget: u32,
+    ) -> Option<NextBatch> {
         if self.entries.is_empty() {
             return None;
         }
@@ -184,7 +192,9 @@ impl State {
 
             decode_tokens += entry.request.stopping_parameters.max_new_tokens;
 
-            if (prefill_tokens + decode_tokens) > token_budget {
+            if prefill_tokens > prefill_token_budget
+                || (prefill_tokens + decode_tokens) > token_budget
+            {
                 // Entry is over budget
                 // Add it back to the front
                 self.entries.push_front((id, entry));
@@ -259,6 +269,7 @@ enum QueueCommand {
     Append(Box<Entry>, Span),
     NextBatch {
         min_size: Option<usize>,
+        prefill_token_budget: u32,
         token_budget: u32,
         response_sender: oneshot::Sender<Option<NextBatch>>,
         span: Span,
@@ -328,8 +339,8 @@ mod tests {
     fn test_next_batch_empty() {
         let mut state = State::new(false);
 
-        assert!(state.next_batch(None, 1).is_none());
-        assert!(state.next_batch(Some(1), 1).is_none());
+        assert!(state.next_batch(None, 1, 1).is_none());
+        assert!(state.next_batch(Some(1), 1, 1).is_none());
     }
 
     #[test]
@@ -340,7 +351,7 @@ mod tests {
         state.append(entry1);
         state.append(entry2);
 
-        let (entries, batch, _) = state.next_batch(None, 2).unwrap();
+        let (entries, batch, _) = state.next_batch(None, 2, 2).unwrap();
         assert_eq!(entries.len(), 2);
         assert!(entries.contains_key(&0));
         assert!(entries.contains_key(&1));
@@ -356,7 +367,7 @@ mod tests {
         let (entry3, _guard3) = default_entry();
         state.append(entry3);
 
-        assert!(state.next_batch(Some(2), 2).is_none());
+        assert!(state.next_batch(Some(2), 2, 2).is_none());
 
         assert_eq!(state.next_id, 3);
         assert_eq!(state.entries.len(), 1);
@@ -372,7 +383,7 @@ mod tests {
         state.append(entry1);
         state.append(entry2);
 
-        let (entries, batch, _) = state.next_batch(None, 1).unwrap();
+        let (entries, batch, _) = state.next_batch(None, 1, 1).unwrap();
         assert_eq!(entries.len(), 1);
         assert!(entries.contains_key(&0));
         assert_eq!(batch.id, 0);
@@ -385,7 +396,7 @@ mod tests {
         let (entry3, _guard3) = default_entry();
         state.append(entry3);
 
-        let (entries, batch, _) = state.next_batch(None, 3).unwrap();
+        let (entries, batch, _) = state.next_batch(None, 3, 3).unwrap();
         assert_eq!(entries.len(), 2);
         assert!(entries.contains_key(&1));
         assert!(entries.contains_key(&2));
@@ -408,8 +419,8 @@ mod tests {
     async fn test_queue_next_batch_empty() {
         let queue = Queue::new(false);
 
-        assert!(queue.next_batch(None, 1).await.is_none());
-        assert!(queue.next_batch(Some(1), 1).await.is_none());
+        assert!(queue.next_batch(None, 1, 1).await.is_none());
+        assert!(queue.next_batch(Some(1), 1, 1).await.is_none());
     }
 
     #[tokio::test]
@@ -420,7 +431,7 @@ mod tests {
         queue.append(entry1);
         queue.append(entry2);
 
-        let (entries, batch, _) = queue.next_batch(None, 2).await.unwrap();
+        let (entries, batch, _) = queue.next_batch(None, 2, 2).await.unwrap();
         assert_eq!(entries.len(), 2);
         assert!(entries.contains_key(&0));
         assert!(entries.contains_key(&1));
@@ -433,11 +444,11 @@ mod tests {
         queue.append(entry3);
 
         // Not enough requests pending
-        assert!(queue.next_batch(Some(2), 2).await.is_none());
+        assert!(queue.next_batch(Some(2), 2, 2).await.is_none());
         // Not enough token budget
-        assert!(queue.next_batch(Some(1), 0).await.is_none());
+        assert!(queue.next_batch(Some(1), 0, 0).await.is_none());
         // Ok
-        let (entries2, batch2, _) = queue.next_batch(Some(1), 2).await.unwrap();
+        let (entries2, batch2, _) = queue.next_batch(Some(1), 2, 2).await.unwrap();
         assert_eq!(entries2.len(), 1);
         assert!(entries2.contains_key(&2));
         assert!(entries2.get(&2).unwrap().batch_time.is_some());
@@ -453,7 +464,7 @@ mod tests {
         queue.append(entry1);
         queue.append(entry2);
 
-        let (entries, batch, _) = queue.next_batch(None, 1).await.unwrap();
+        let (entries, batch, _) = queue.next_batch(None, 1, 1).await.unwrap();
         assert_eq!(entries.len(), 1);
         assert!(entries.contains_key(&0));
         assert_eq!(batch.id, 0);
@@ -462,7 +473,7 @@ mod tests {
         let (entry3, _guard3) = default_entry();
         queue.append(entry3);
 
-        let (entries, batch, _) = queue.next_batch(None, 3).await.unwrap();
+        let (entries, batch, _) = queue.next_batch(None, 3, 3).await.unwrap();
         assert_eq!(entries.len(), 2);
         assert!(entries.contains_key(&1));
         assert!(entries.contains_key(&2));
@@ -476,6 +487,6 @@ mod tests {
         let (entry, _) = default_entry();
         queue.append(entry);
 
-        assert!(queue.next_batch(None, 1).await.is_none());
+        assert!(queue.next_batch(None, 1, 1).await.is_none());
     }
 }

router/src/server.rs

@@ -514,6 +514,7 @@ pub async fn run(
     max_input_length: usize,
     max_total_tokens: usize,
     waiting_served_ratio: f32,
+    max_batch_prefill_tokens: u32,
     max_batch_total_tokens: u32,
     max_waiting_tokens: usize,
     client: ShardedClient,
@@ -531,6 +532,7 @@ pub async fn run(
     #[derive(OpenApi)]
     #[openapi(
         paths(
+            health,
             get_model_info,
             compat_generate,
             generate,
@@ -582,6 +584,7 @@ pub async fn run(
         client,
         validation,
         waiting_served_ratio,
+        max_batch_prefill_tokens,
         max_batch_total_tokens,
         max_waiting_tokens,
         max_concurrent_requests,

rust-toolchain.toml

@@ -1,3 +1,3 @@
 [toolchain]
-channel = "1.69.0"
+channel = "1.70.0"
 components = ["rustfmt", "clippy"]

server/Makefile

@@ -1,4 +1,5 @@
 include Makefile-flash-att
+include Makefile-vllm
 
 unit-tests:
 	pytest -s -vv -m "not private" tests

server/Makefile-flash-att

@@ -1,9 +1,9 @@
-flash_att_commit := 06ece1a1525ebcf4e183ac76b1e5108d2872f57f
+flash_att_commit := 3a9bfd076f98746c73362328958dbc68d145fbec
 
 flash-attention:
     # Clone flash attention
 	pip install packaging
-	git clone https://github.com/OlivierDehaene/flash-attention.git
+	git clone https://github.com/HazyResearch/flash-attention.git
 
 build-flash-attention: flash-attention
 	cd flash-attention && git fetch && git checkout $(flash_att_commit)

server/Makefile-vllm

@@ -0,0 +1,13 @@
+vllm_commit := d284b831c17f42a8ea63369a06138325f73c4cf9
+
+vllm:
+    # Clone vllm
+	git clone https://github.com/OlivierDehaene/vllm.git
+
+build-vllm: vllm
+	cd vllm && git fetch && git checkout $(vllm_commit)
+	cd vllm && python setup.py build
+
+install-vllm: build-vllm
+	pip uninstall vllm -y || true
+	cd vllm && python setup.py install

server/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "text-generation-server"
-version = "0.8.2"
+version = "0.9.0"
 description = "Text Generation Inference Python gRPC Server"
 authors = ["Olivier Dehaene <olivier@huggingface.co>"]
 
@@ -26,7 +26,8 @@ hf-transfer = "^0.1.2"
 sentencepiece = "^0.1.97"
 tokenizers = "0.13.3"
 huggingface-hub = "^0.14.1"
-transformers = "^4.29.2"
+transformers = "4.29.2"
+einops = "^0.6.1"
 
 [tool.poetry.extras]
 accelerate = ["accelerate"]

server/requirements.txt

@@ -1,21 +1,23 @@
 backoff==2.2.1 ; python_version >= "3.9" and python_version < "4.0"
+bitsandbytes==0.38.1 ; python_version >= "3.9" and python_version < "4.0"
 certifi==2023.5.7 ; python_version >= "3.9" and python_version < "4.0"
 charset-normalizer==3.1.0 ; python_version >= "3.9" and python_version < "4.0"
 click==8.1.3 ; python_version >= "3.9" and python_version < "4.0"
-colorama==0.4.6 ; python_version >= "3.9" and python_version < "4.0" and (sys_platform == "win32" or platform_system == "Windows")
+colorama==0.4.6 ; python_version >= "3.9" and python_version < "4.0" and sys_platform == "win32" or python_version >= "3.9" and python_version < "4.0" and platform_system == "Windows"
 deprecated==1.2.14 ; python_version >= "3.9" and python_version < "4.0"
+einops==0.6.1 ; python_version >= "3.9" and python_version < "4.0"
 filelock==3.12.2 ; python_version >= "3.9" and python_version < "4.0"
 fsspec==2023.6.0 ; python_version >= "3.9" and python_version < "4.0"
 googleapis-common-protos==1.59.1 ; python_version >= "3.9" and python_version < "4.0"
 grpc-interceptor==0.15.2 ; python_version >= "3.9" and python_version < "4.0"
-grpcio-reflection==1.54.2 ; python_version >= "3.9" and python_version < "4.0"
-grpcio-status==1.54.2 ; python_version >= "3.9" and python_version < "4.0"
-grpcio==1.54.2 ; python_version >= "3.9" and python_version < "4.0"
+grpcio-reflection==1.56.0 ; python_version >= "3.9" and python_version < "4.0"
+grpcio-status==1.56.0 ; python_version >= "3.9" and python_version < "4.0"
+grpcio==1.56.0 ; python_version >= "3.9" and python_version < "4.0"
 hf-transfer==0.1.3 ; python_version >= "3.9" and python_version < "4.0"
 huggingface-hub==0.14.1 ; python_version >= "3.9" and python_version < "4.0"
 idna==3.4 ; python_version >= "3.9" and python_version < "4.0"
 loguru==0.6.0 ; python_version >= "3.9" and python_version < "4.0"
-numpy==1.24.3 ; python_version >= "3.9" and python_version < "4.0"
+numpy==1.25.0 ; python_version >= "3.9" and python_version < "4.0"
 opentelemetry-api==1.15.0 ; python_version >= "3.9" and python_version < "4.0"
 opentelemetry-exporter-otlp-proto-grpc==1.15.0 ; python_version >= "3.9" and python_version < "4.0"
 opentelemetry-exporter-otlp-proto-http==1.15.0 ; python_version >= "3.9" and python_version < "4.0"
@@ -26,18 +28,18 @@ opentelemetry-proto==1.15.0 ; python_version >= "3.9" and python_version < "4.0"
 opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "4.0"
 opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "4.0"
 packaging==23.1 ; python_version >= "3.9" and python_version < "4.0"
-protobuf==4.23.2 ; python_version >= "3.9" and python_version < "4.0"
+protobuf==4.23.3 ; python_version >= "3.9" and python_version < "4.0"
 pyyaml==6.0 ; python_version >= "3.9" and python_version < "4.0"
 regex==2023.6.3 ; python_version >= "3.9" and python_version < "4.0"
 requests==2.31.0 ; python_version >= "3.9" and python_version < "4.0"
 safetensors==0.3.1 ; python_version >= "3.9" and python_version < "4.0"
 sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "4.0"
-setuptools==67.8.0 ; python_version >= "3.9" and python_version < "4.0"
+setuptools==68.0.0 ; python_version >= "3.9" and python_version < "4.0"
 tokenizers==0.13.3 ; python_version >= "3.9" and python_version < "4.0"
 tqdm==4.65.0 ; python_version >= "3.9" and python_version < "4.0"
-transformers==4.30.2 ; python_version >= "3.9" and python_version < "4.0"
+transformers==4.29.2 ; python_version >= "3.9" and python_version < "4.0"
 typer==0.6.1 ; python_version >= "3.9" and python_version < "4.0"
-typing-extensions==4.6.3 ; python_version >= "3.9" and python_version < "4.0"
+typing-extensions==4.7.1 ; python_version >= "3.9" and python_version < "4.0"
 urllib3==2.0.3 ; python_version >= "3.9" and python_version < "4.0"
 win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "4.0" and sys_platform == "win32"
 wrapt==1.15.0 ; python_version >= "3.9" and python_version < "4.0"

server/text_generation_server/cache.py

@@ -22,7 +22,9 @@ class Cache:
             del batch
 
     def clear(self):
-        self.cache.clear()
+        keys = list(self.cache.keys())
+        for k in keys:
+            self.delete(k)
 
     def __len__(self):
         return len(self.cache.keys())

server/text_generation_server/cli.py

@@ -17,12 +17,18 @@ class Quantization(str, Enum):
     gptq_cuda = "gptq-cuda"
 
 
+class Dtype(str, Enum):
+    float16 = "float16"
+    bloat16 = "bfloat16"
+
+
 @app.command()
 def serve(
     model_id: str,
     revision: Optional[str] = None,
     sharded: bool = False,
     quantize: Optional[Quantization] = None,
+    dtype: Optional[Dtype] = None,
     trust_remote_code: bool = False,
     uds_path: Path = "/tmp/text-generation-server",
     logger_level: str = "INFO",
@@ -65,7 +71,14 @@ def serve(
 
     # Downgrade enum into str for easier management later on
     quantize = None if quantize is None else quantize.value
-    server.serve(model_id, revision, sharded, quantize, trust_remote_code, uds_path)
+    dtype = None if dtype is None else dtype.value
+    if dtype is not None and quantize is not None:
+        raise RuntimeError(
+            "Only 1 can be set between `dtype` and `quantize`, as they both decide how goes the final model."
+        )
+    server.serve(
+        model_id, revision, sharded, quantize, dtype, trust_remote_code, uds_path
+    )
 
 
 @app.command()

server/text_generation_server/models/__init__.py

@@ -10,6 +10,7 @@ from text_generation_server.models.model import Model
 from text_generation_server.models.causal_lm import CausalLM
 from text_generation_server.models.flash_causal_lm import FlashCausalLM
 from text_generation_server.models.bloom import BLOOMSharded
+from text_generation_server.models.mpt import MPTSharded
 from text_generation_server.models.seq2seq_lm import Seq2SeqLM
 from text_generation_server.models.rw import RW
 from text_generation_server.models.opt import OPTSharded
@@ -100,11 +101,25 @@ def get_model(
     revision: Optional[str],
     sharded: bool,
     quantize: Optional[str],
+    dtype: Optional[str],
     trust_remote_code: bool,
 ) -> Model:
+    if dtype is None:
+        dtype = torch.float16
+    elif dtype == "float16":
+        dtype = torch.float16
+    elif dtype == "bfloat16":
+        dtype = torch.bfloat16
+    else:
+        raise RuntimeError(f"Unknown dtype {dtype}")
+
     if "facebook/galactica" in model_id:
         return GalacticaSharded(
-            model_id, revision, quantize=quantize, trust_remote_code=trust_remote_code
+            model_id,
+            revision,
+            quantize=quantize,
+            dtype=dtype,
+            dtypetrust_remote_code=trust_remote_code,
         )
 
     if model_id.startswith("bigcode/"):
@@ -113,6 +128,7 @@ def get_model(
                 model_id,
                 revision,
                 quantize=quantize,
+                dtype=dtype,
                 trust_remote_code=trust_remote_code,
             )
         elif sharded:
@@ -124,6 +140,7 @@ def get_model(
                 model_id,
                 revision,
                 quantize=quantize,
+                dtype=dtype,
                 trust_remote_code=trust_remote_code,
             )
 
@@ -138,6 +155,7 @@ def get_model(
                 model_id,
                 revision,
                 quantize=quantize,
+                dtype=dtype,
                 trust_remote_code=trust_remote_code,
             )
         elif sharded:
@@ -149,11 +167,20 @@ def get_model(
                 model_id,
                 revision,
                 quantize=quantize,
+                dtype=dtype,
                 trust_remote_code=trust_remote_code,
             )
 
     if model_type == "bloom":
         return BLOOMSharded(
+            model_id,
+            revision,
+            quantize=quantize,
+            dtype=dtype,
+            trust_remote_code=trust_remote_code,
+        )
+    elif model_type == "mpt":
+        return MPTSharded(
             model_id, revision, quantize=quantize, trust_remote_code=trust_remote_code
         )
 
@@ -163,6 +190,7 @@ def get_model(
                 model_id,
                 revision,
                 quantize=quantize,
+                dtype=dtype,
                 trust_remote_code=trust_remote_code,
             )
         elif sharded:
@@ -170,6 +198,7 @@ def get_model(
                 model_id,
                 revision,
                 quantize=quantize,
+                dtype=dtype,
                 trust_remote_code=trust_remote_code,
             )
         else:
@@ -177,6 +206,7 @@ def get_model(
                 model_id,
                 revision,
                 quantize=quantize,
+                dtype=dtype,
                 trust_remote_code=trust_remote_code,
             )
 
@@ -186,6 +216,7 @@ def get_model(
                 model_id,
                 revision,
                 quantize=quantize,
+                dtype=dtype,
                 trust_remote_code=trust_remote_code,
             )
         elif sharded:
@@ -195,6 +226,7 @@ def get_model(
                 model_id,
                 revision,
                 quantize=quantize,
+                dtype=dtype,
                 trust_remote_code=trust_remote_code,
             )
 
@@ -210,6 +242,7 @@ def get_model(
                     model_id,
                     revision,
                     quantize=quantize,
+                    dtype=dtype,
                     trust_remote_code=trust_remote_code,
                 )
             raise NotImplementedError(
@@ -221,6 +254,7 @@ def get_model(
                     model_id,
                     revision,
                     quantize=quantize,
+                    dtype=dtype,
                     trust_remote_code=trust_remote_code,
                 )
             else:
@@ -228,12 +262,17 @@ def get_model(
                     model_id,
                     revision,
                     quantize=quantize,
+                    dtype=dtype,
                     trust_remote_code=trust_remote_code,
                 )
 
     elif model_type == "opt":
         return OPTSharded(
-            model_id, revision, quantize=quantize, trust_remote_code=trust_remote_code
+            model_id,
+            revision,
+            quantize=quantize,
+            dtype=dtype,
+            trust_remote_code=trust_remote_code,
         )
 
     elif model_type == "t5":
@@ -241,6 +280,7 @@ def get_model(
             model_id,
             revision,
             quantize=quantize,
+            dtype=dtype,
             trust_remote_code=trust_remote_code,
         )
 
@@ -253,11 +293,19 @@ def get_model(
 
     if model_type in modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
         return CausalLM(
-            model_id, revision, quantize=quantize, trust_remote_code=trust_remote_code
+            model_id,
+            revision,
+            quantize=quantize,
+            dtype=dtype,
+            trust_remote_code=trust_remote_code,
         )
     if model_type in modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES:
         return Seq2SeqLM(
-            model_id, revision, quantize=quantize, trust_remote_code=trust_remote_code
+            model_id,
+            revision,
+            quantize=quantize,
+            dtype=dtype,
+            trust_remote_code=trust_remote_code,
         )
 
     auto_map = config_dict.get("auto_map", None)
@@ -267,6 +315,7 @@ def get_model(
                 model_id,
                 revision,
                 quantize=quantize,
+                dtype=dtype,
                 trust_remote_code=trust_remote_code,
             )
         if "AutoModelForSeq2SeqLM" in auto_map.keys():
@@ -274,6 +323,7 @@ def get_model(
                 model_id,
                 revision,
                 quantize=quantize,
+                dtype=dtype,
                 trust_remote_code=trust_remote_code,
             )
 

server/text_generation_server/models/bloom.py

@@ -42,12 +42,13 @@ class BLOOMSharded(CausalLM):
         model_id: str,
         revision: Optional[str] = None,
         quantize: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
-            dtype = torch.float16
+            dtype = torch.float16 if dtype is None else dtype
         else:
             device = torch.device("cpu")
             dtype = torch.float32

server/text_generation_server/models/causal_lm.py

@@ -122,7 +122,7 @@ class CausalLMBatch(Batch):
         position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
         all_input_ids = tokenized_inputs["input_ids"].T.split(1, dim=1)
 
-        max_tokens = len(inputs) * max_input_length + max_decode_tokens
+        max_tokens = len(inputs) * (max_input_length + max_decode_tokens)
 
         return cls(
             batch_id=pb.id,
@@ -454,11 +454,12 @@ class CausalLM(Model):
         model_id: str,
         revision: Optional[str] = None,
         quantize: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
         if torch.cuda.is_available():
             device = torch.device("cuda")
-            dtype = torch.float16
+            dtype = torch.float16 if dtype is None else dtype
         else:
             if quantize:
                 raise ValueError("quantization is not available on CPU")

server/text_generation_server/models/custom_modeling/flash_llama_modeling.py

@@ -23,12 +23,16 @@ import torch.distributed
 
 from torch import nn
 from transformers.activations import ACT2FN
-from typing import Optional
+from typing import Optional, List, Tuple
 
 # Flash attention imports
 import flash_attn_cuda
 import dropout_layer_norm
 
+# vllm imports
+import vllm_cache_ops
+import vllm_attention_ops
+
 from text_generation_server.utils.layers import (
     TensorParallelRowLinear,
     TensorParallelColumnLinear,
@@ -106,7 +110,7 @@ class FlashLlamaAttention(torch.nn.Module):
             prefix=f"{prefix}.rotary_emb", weights=weights
         )
 
-        self.softmax_scale = self.head_size ** (-0.5)
+        self.softmax_scale = self.head_size**-0.5
 
         self.num_heads = self.num_heads // weights.process_group.size()
         self.query_key_value = TensorParallelColumnLinear.load_multi(
@@ -122,20 +126,21 @@ class FlashLlamaAttention(torch.nn.Module):
             weights=weights,
             bias=False,
         )
+        self.kv_head_mapping = torch.arange(
+            0, self.num_heads, dtype=torch.int32, device=weights.device
+        )
 
     def forward(
         self,
         hidden_states,
         cos,
         sin,
-        start_seq,
-        end_seq,
-        start_seq_q,
-        end_seq_q,
+        cu_seqlen_prefill,
+        kv_cache,
+        block_tables,
+        slots,
+        input_lengths,
         max_s,
-        layer_past,
-        past_present_indices,
-        prefill,
     ):
         qkv = self.query_key_value(hidden_states)
         qkv = qkv.view(-1, 3, self.num_heads, self.head_size)
@@ -144,23 +149,23 @@ class FlashLlamaAttention(torch.nn.Module):
         self.rotary_emb(qkv[:, 0], cos, sin)
         self.rotary_emb(qkv[:, 1], cos, sin)
 
-        # Prefill
-        if prefill:
-            # Copy to layer past
-            layer_past[...] = qkv[:, 1:]
+        vllm_cache_ops.reshape_and_cache(
+            qkv[:, 1], qkv[:, 2], kv_cache[0], kv_cache[1], slots
+        )
 
-            # output
-            attn_output = torch.empty_like(qkv[:, 0])
+        # output tensor
+        attn_output = torch.empty_like(qkv[:, 0])
+
+        # Prefill
+        if cu_seqlen_prefill is not None:
             # flash attention
             flash_attn_cuda.fwd(
                 qkv[:, 0],
                 qkv[:, 1],
                 qkv[:, 2],
                 attn_output,
-                start_seq,
-                end_seq,
-                start_seq,
-                end_seq,
+                cu_seqlen_prefill,
+                cu_seqlen_prefill,
                 max_s,
                 max_s,
                 0.0,
@@ -173,31 +178,19 @@ class FlashLlamaAttention(torch.nn.Module):
             )
         # Decode
         else:
-            query = qkv[:, 0]
-            # Add present to the layer_past tensor at the correct indices
-            layer_past[past_present_indices] = qkv[:, 1:]
-
-            # output
-            attn_output = torch.empty_like(query)
-            # flash attention
-            flash_attn_cuda.fwd(
-                query,
-                layer_past[:, 0],
-                layer_past[:, 1],
+            # kv_cache[1] => [num_blocks, num_heads, head_size, block_size]
+            block_size = kv_cache[1].shape[3]
+            vllm_attention_ops.single_query_cached_kv_attention(
                 attn_output,
-                start_seq_q,
-                end_seq_q,
-                start_seq,
-                end_seq,
-                1,
-                max_s,
-                0.0,
+                qkv[:, 0],
+                kv_cache[0],
+                kv_cache[1],
+                self.kv_head_mapping,
                 self.softmax_scale,
-                False,
-                False,
-                False,
-                0,
-                None,
+                block_tables,
+                input_lengths,
+                block_size,
+                max_s,
             )
 
         return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
@@ -265,14 +258,12 @@ class FlashLlamaLayer(nn.Module):
         residual,
         cos,
         sin,
-        start_seq,
-        end_seq,
-        start_seq_q,
-        end_seq_q,
+        cu_seqlen_prefill,
+        kv_cache,
+        block_tables,
+        slots,
+        input_lengths,
         max_s,
-        layer_past,
-        past_present_indices,
-        prefill,
     ):
         normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
 
@@ -281,14 +272,12 @@ class FlashLlamaLayer(nn.Module):
             normed_hidden_states,
             cos,
             sin,
-            start_seq,
-            end_seq,
-            start_seq_q,
-            end_seq_q,
+            cu_seqlen_prefill,
+            kv_cache,
+            block_tables,
+            slots,
+            input_lengths,
             max_s,
-            layer_past,
-            past_present_indices,
-            prefill,
         )
 
         # faster post attention rms norm
@@ -333,40 +322,17 @@ class FlashLlamaModel(torch.nn.Module):
 
     def forward(
         self,
-        input_ids,
-        position_ids,
-        start_seq,
-        end_seq,
-        start_seq_q,
-        end_seq_q,
-        max_s,
-        past_present_indices,
-        past_key_values=None,
-        pre_allocate_past_size: Optional[int] = None,
-    ):
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        cu_seqlen_prefill: Optional[torch.Tensor],
+        kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
+        block_tables: torch.Tensor,
+        slots: torch.Tensor,
+        input_lengths: torch.Tensor,
+        max_s: int,
+    ) -> torch.Tensor:
         hidden_states = self.embed_tokens(input_ids)
 
-        # Prefill
-        if past_key_values is None:
-            assert pre_allocate_past_size is not None
-
-            prefill = True
-
-            # Create past tensor
-            # We create a tensor of the same size as input_ids as we don't want to slice at every layer
-            past_key_values = hidden_states.new_empty(
-                (
-                    len(input_ids),
-                    len(self.layers),
-                    2,
-                    self.num_heads,
-                    self.head_size,
-                )
-            )
-        # Decode
-        else:
-            prefill = False
-
         # Get rotary cos and sin for this forward
         # Avoid to index in each layer
         cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
@@ -380,34 +346,17 @@ class FlashLlamaModel(torch.nn.Module):
                 residual,
                 cos,
                 sin,
-                start_seq,
-                end_seq,
-                start_seq_q,
-                end_seq_q,
+                cu_seqlen_prefill,
+                kv_cache[i],
+                block_tables,
+                slots,
+                input_lengths,
                 max_s,
-                past_key_values[:, i],
-                past_present_indices,
-                prefill,
             )
 
-        if prefill:
-            present = past_key_values
-            # Create padded past tensor
-            past_key_values = hidden_states.new_empty(
-                (
-                    pre_allocate_past_size,
-                    len(self.layers),
-                    2,
-                    self.num_heads,
-                    self.head_size,
-                )
-            )
-            # We slice only once instead of at every layer
-            past_key_values[past_present_indices] = present
-
         hidden_states, _ = self.norm(hidden_states, residual)
 
-        return hidden_states, past_key_values
+        return hidden_states
 
 
 class FlashLlamaForCausalLM(torch.nn.Module):
@@ -423,31 +372,27 @@ class FlashLlamaForCausalLM(torch.nn.Module):
 
     def forward(
         self,
-        input_ids,
-        position_ids,
-        start_seq,
-        end_seq,
-        start_seq_q,
-        end_seq_q,
-        max_s,
-        past_present_indices,
-        past_key_values: Optional[torch.Tensor] = None,
-        pre_allocate_past_size: Optional[int] = None,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        cu_seqlen_prefill: Optional[torch.Tensor],
+        kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
+        block_tables: torch.Tensor,
+        slots: torch.Tensor,
+        input_lengths: torch.Tensor,
+        max_s: int,
         lm_head_indices: Optional[torch.Tensor] = None,
-    ):
-        hidden_states, present = self.model(
+    ) -> torch.Tensor:
+        hidden_states = self.model(
             input_ids,
             position_ids,
-            start_seq,
-            end_seq,
-            start_seq_q,
-            end_seq_q,
+            cu_seqlen_prefill,
+            kv_cache,
+            block_tables,
+            slots,
+            input_lengths,
             max_s,
-            past_present_indices,
-            past_key_values,
-            pre_allocate_past_size,
         )
         if lm_head_indices is not None:
             hidden_states = hidden_states[lm_head_indices]
         logits = self.lm_head(hidden_states)
-        return logits, present
+        return logits

server/text_generation_server/models/custom_modeling/flash_neox_modeling.py

@@ -25,11 +25,15 @@ from torch import nn
 from transformers.activations import ACT2FN
 from transformers.modeling_utils import PreTrainedModel
 from transformers.models.gpt_neox import GPTNeoXConfig
-from typing import Optional
+from typing import Optional, List, Tuple
 
 # Flash attention imports
 import flash_attn_cuda
 
+# vllm imports
+import vllm_cache_ops
+import vllm_attention_ops
+
 from text_generation_server.utils.layers import (
     TensorParallelRowLinear,
     TensorParallelColumnLinear,
@@ -110,20 +114,21 @@ class FlashNeoxAttention(torch.nn.Module):
         self.dense = load_row(
             config, prefix=f"{prefix}.dense", weights=weights, bias=True
         )
+        self.kv_head_mapping = torch.arange(
+            0, self.num_heads, dtype=torch.int32, device=weights.device
+        )
 
     def forward(
         self,
         hidden_states,
         cos,
         sin,
-        start_seq,
-        end_seq,
-        start_seq_q,
-        end_seq_q,
+        cu_seqlen_prefill,
+        kv_cache,
+        block_tables,
+        slots,
+        input_lengths,
         max_s,
-        layer_past,
-        past_present_indices,
-        prefill,
     ):
         qkv = self.query_key_value(hidden_states)
         qkv = qkv.view(-1, 3, self.num_heads, self.head_size)
@@ -132,23 +137,23 @@ class FlashNeoxAttention(torch.nn.Module):
         self.rotary_emb(qkv[:, 0], cos, sin)
         self.rotary_emb(qkv[:, 1], cos, sin)
 
-        # Prefill
-        if prefill:
-            # Copy to layer past
-            layer_past[...] = qkv[:, 1:]
+        vllm_cache_ops.reshape_and_cache(
+            qkv[:, 1], qkv[:, 2], kv_cache[0], kv_cache[1], slots
+        )
 
-            # output
-            attn_output = torch.empty_like(qkv[:, 0])
+        # output tensor
+        attn_output = torch.empty_like(qkv[:, 0])
+
+        # Prefill
+        if cu_seqlen_prefill is not None:
             # flash attention
             flash_attn_cuda.fwd(
                 qkv[:, 0],
                 qkv[:, 1],
                 qkv[:, 2],
                 attn_output,
-                start_seq,
-                end_seq,
-                start_seq,
-                end_seq,
+                cu_seqlen_prefill,
+                cu_seqlen_prefill,
                 max_s,
                 max_s,
                 0.0,
@@ -161,31 +166,19 @@ class FlashNeoxAttention(torch.nn.Module):
             )
         # Decode
         else:
-            query = qkv[:, 0]
-            # Add present to the layer_past tensor at the correct indices
-            layer_past[past_present_indices] = qkv[:, 1:]
-
-            # output
-            attn_output = torch.empty_like(query)
-            # flash attention
-            flash_attn_cuda.fwd(
-                query,
-                layer_past[:, 0],
-                layer_past[:, 1],
+            # kv_cache[1] => [num_blocks, num_heads, head_size, block_size]
+            block_size = kv_cache[1].shape[3]
+            vllm_attention_ops.single_query_cached_kv_attention(
                 attn_output,
-                start_seq_q,
-                end_seq_q,
-                start_seq,
-                end_seq,
-                1,
-                max_s,
-                0.0,
+                qkv[:, 0],
+                kv_cache[0],
+                kv_cache[1],
+                self.kv_head_mapping,
                 self.softmax_scale,
-                False,
-                False,
-                False,
-                0,
-                None,
+                block_tables,
+                input_lengths,
+                block_size,
+                max_s,
             )
 
         return self.dense(attn_output.view(-1, self.num_heads * self.head_size))
@@ -250,14 +243,12 @@ class FlashNeoXLayer(nn.Module):
         residual,
         cos,
         sin,
-        start_seq,
-        end_seq,
-        start_seq_q,
-        end_seq_q,
+        cu_seqlen_prefill,
+        kv_cache,
+        block_tables,
+        slots,
+        input_lengths,
         max_s,
-        layer_past,
-        past_present_indices,
-        prefill,
     ):
         if self.use_parallel_residual:
             ln1_hidden_states, _ = self.input_layernorm(hidden_states)
@@ -266,14 +257,12 @@ class FlashNeoXLayer(nn.Module):
                 ln1_hidden_states,
                 cos,
                 sin,
-                start_seq,
-                end_seq,
-                start_seq_q,
-                end_seq_q,
+                cu_seqlen_prefill,
+                kv_cache,
+                block_tables,
+                slots,
+                input_lengths,
                 max_s,
-                layer_past,
-                past_present_indices,
-                prefill,
             )
 
             ln2_hidden_states, _ = self.post_attention_layernorm(hidden_states)
@@ -292,14 +281,12 @@ class FlashNeoXLayer(nn.Module):
                 hidden_states,
                 cos,
                 sin,
-                start_seq,
-                end_seq,
-                start_seq_q,
-                end_seq_q,
+                cu_seqlen_prefill,
+                kv_cache,
+                block_tables,
+                slots,
+                input_lengths,
                 max_s,
-                layer_past,
-                past_present_indices,
-                prefill,
             )
 
             hidden_states, residual = self.post_attention_layernorm(
@@ -346,40 +333,17 @@ class FlashGPTNeoXModel(FlashGPTNeoXPreTrainedModel):
 
     def forward(
         self,
-        input_ids,
-        position_ids,
-        start_seq,
-        end_seq,
-        start_seq_q,
-        end_seq_q,
-        max_s,
-        past_present_indices,
-        past_key_values=None,
-        pre_allocate_past_size: Optional[int] = None,
-    ):
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        cu_seqlen_prefill: Optional[torch.Tensor],
+        kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
+        block_tables: torch.Tensor,
+        slots: torch.Tensor,
+        input_lengths: torch.Tensor,
+        max_s: int,
+    ) -> torch.Tensor:
         hidden_states = self.embed_in(input_ids)
 
-        # Prefill
-        if past_key_values is None:
-            assert pre_allocate_past_size is not None
-
-            prefill = True
-
-            # Create past tensor
-            # We create a tensor of the same size as input_ids as we don't want to slice at every layer
-            past_key_values = hidden_states.new_empty(
-                (
-                    len(input_ids),
-                    len(self.layers),
-                    2,
-                    self.num_heads,
-                    self.head_size,
-                )
-            )
-        # Decode
-        else:
-            prefill = False
-
         # Get rotary cos and sin for this forward
         # Avoid to index in each layer
         cos, sin = self.layers[0].attention.rotary_emb.get_cos_sin(
@@ -393,34 +357,17 @@ class FlashGPTNeoXModel(FlashGPTNeoXPreTrainedModel):
                 residual,
                 cos,
                 sin,
-                start_seq,
-                end_seq,
-                start_seq_q,
-                end_seq_q,
+                cu_seqlen_prefill,
+                kv_cache[i],
+                block_tables,
+                slots,
+                input_lengths,
                 max_s,
-                past_key_values[:, i],
-                past_present_indices,
-                prefill,
             )
 
-        if prefill:
-            present = past_key_values
-            # Create padded past tensor
-            past_key_values = hidden_states.new_empty(
-                (
-                    pre_allocate_past_size,
-                    len(self.layers),
-                    2,
-                    self.num_heads,
-                    self.head_size,
-                )
-            )
-            # We slice only once instead of at every layer
-            past_key_values[past_present_indices] = present
-
         hidden_states, _ = self.final_layer_norm(hidden_states, residual)
 
-        return hidden_states, past_key_values
+        return hidden_states
 
 
 class FlashGPTNeoXForCausalLM(FlashGPTNeoXPreTrainedModel):
@@ -434,31 +381,27 @@ class FlashGPTNeoXForCausalLM(FlashGPTNeoXPreTrainedModel):
 
     def forward(
         self,
-        input_ids,
-        position_ids,
-        start_seq,
-        end_seq,
-        start_seq_q,
-        end_seq_q,
-        max_s,
-        past_present_indices,
-        past_key_values: Optional[torch.Tensor] = None,
-        pre_allocate_past_size: Optional[int] = None,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        cu_seqlen_prefill: Optional[torch.Tensor],
+        kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
+        block_tables: torch.Tensor,
+        slots: torch.Tensor,
+        input_lengths: torch.Tensor,
+        max_s: int,
         lm_head_indices: Optional[torch.Tensor] = None,
-    ):
-        hidden_states, present = self.gpt_neox(
+    ) -> torch.Tensor:
+        hidden_states = self.gpt_neox(
             input_ids,
             position_ids,
-            start_seq,
-            end_seq,
-            start_seq_q,
-            end_seq_q,
+            cu_seqlen_prefill,
+            kv_cache,
+            block_tables,
+            slots,
+            input_lengths,
             max_s,
-            past_present_indices,
-            past_key_values,
-            pre_allocate_past_size,
         )
         if lm_head_indices is not None:
             hidden_states = hidden_states[lm_head_indices]
         logits = self.embed_out(hidden_states)
-        return logits, present
+        return logits

server/text_generation_server/models/custom_modeling/flash_rw_modeling.py

@@ -4,11 +4,15 @@ import torch.distributed
 from torch import nn
 from transformers.modeling_utils import PreTrainedModel
 from transformers.configuration_utils import PretrainedConfig
-from typing import Optional
+from typing import Optional, List, Tuple
 
 # Flash attention imports
 import flash_attn_cuda
 
+# vllm imports
+import vllm_cache_ops
+import vllm_attention_ops
+
 from text_generation_server.utils.layers import (
     TensorParallelRowLinear,
     TensorParallelColumnLinear,
@@ -126,19 +130,26 @@ class FlashRWAttention(torch.nn.Module):
             config, prefix=f"{prefix}.dense", weights=weights, bias=config.bias
         )
 
+        if self.num_heads_kv == 1:
+            self.kv_head_mapping = torch.zeros(
+                self.num_heads, dtype=torch.int32, device=weights.device
+            )
+        else:
+            self.kv_head_mapping = torch.arange(
+                0, self.num_heads, dtype=torch.int32, device=weights.device
+            )
+
     def forward(
         self,
         hidden_states,
         cos,
         sin,
-        start_seq,
-        end_seq,
-        start_seq_q,
-        end_seq_q,
+        cu_seqlen_prefill,
+        kv_cache,
+        block_tables,
+        slots,
+        input_lengths,
         max_s,
-        layer_past,
-        past_present_indices,
-        prefill,
     ):
         qkv = self.query_key_value(hidden_states)
 
@@ -156,25 +167,27 @@ class FlashRWAttention(torch.nn.Module):
         self.rotary_emb(query, cos, sin)
         self.rotary_emb(torch.select(kv, dim=1, index=0), cos, sin)
 
-        # Prefill
-        if prefill:
-            # Copy to layer past
-            layer_past[...] = kv
-            # Expand to query shape
-            kv = kv.expand(-1, 2, self.num_heads, self.head_size)
+        vllm_cache_ops.reshape_and_cache(
+            kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots
+        )
+
+        # output
+        attn_output = torch.empty_like(query)
+
+        # Prefill
+        if cu_seqlen_prefill is not None:
+            if self.num_heads_kv == 1:
+                # Expand to query shape
+                kv = kv.expand(-1, 2, self.num_heads, self.head_size)
 
-            # output
-            attn_output = torch.empty_like(query)
             # flash attention
             flash_attn_cuda.fwd(
                 query,
                 torch.select(kv, dim=1, index=0),
                 torch.select(kv, dim=1, index=1),
                 attn_output,
-                start_seq,
-                end_seq,
-                start_seq,
-                end_seq,
+                cu_seqlen_prefill,
+                cu_seqlen_prefill,
                 max_s,
                 max_s,
                 0.0,
@@ -187,32 +200,19 @@ class FlashRWAttention(torch.nn.Module):
             )
         # Decode
         else:
-            # Add present to the layer_past tensor at the correct indices
-            layer_past[past_present_indices] = kv
-            # Expand to query shape
-            kv = layer_past.expand(-1, 2, self.num_heads, self.head_size)
-
-            # output
-            attn_output = torch.empty_like(query)
-            # flash attention
-            flash_attn_cuda.fwd(
-                query,
-                torch.select(kv, dim=1, index=0),
-                torch.select(kv, dim=1, index=1),
+            # kv_cache[1] => [num_blocks, num_heads_kv, head_size, block_size]
+            block_size = kv_cache[1].shape[3]
+            vllm_attention_ops.single_query_cached_kv_attention(
                 attn_output,
-                start_seq_q,
-                end_seq_q,
-                start_seq,
-                end_seq,
-                1,
-                max_s,
-                0.0,
+                query,
+                kv_cache[0],
+                kv_cache[1],
+                self.kv_head_mapping,
                 self.softmax_scale,
-                False,
-                False,
-                False,
-                0,
-                None,
+                block_tables,
+                input_lengths,
+                block_size,
+                max_s,
             )
 
         return self.dense(attn_output.view(-1, self.num_heads * self.head_size))
@@ -264,19 +264,21 @@ class FlashRWLargeAttention(torch.nn.Module):
             config, prefix=f"{prefix}.dense", weights=weights, bias=config.bias
         )
 
+        self.kv_head_mapping = torch.arange(
+            0, self.num_groups, dtype=torch.int32, device=weights.device
+        ).repeat_interleave(self.num_heads)
+
     def forward(
         self,
         hidden_states,
         cos,
         sin,
-        start_seq,
-        end_seq,
-        start_seq_q,
-        end_seq_q,
+        cu_seqlen_prefill,
+        kv_cache,
+        block_tables,
+        slots,
+        input_lengths,
         max_s,
-        layer_past,
-        past_present_indices,
-        prefill,
     ):
         qkv = self.query_key_value(hidden_states)
         qkv = qkv.view(-1, self.num_groups, self.num_heads + 2, self.head_size)
@@ -293,10 +295,19 @@ class FlashRWLargeAttention(torch.nn.Module):
         self.rotary_emb(query, cos, sin)
         self.rotary_emb(torch.select(kv, dim=2, index=0), cos, sin)
 
+        vllm_cache_ops.reshape_and_cache(
+            kv[:, :, 0].contiguous(),
+            kv[:, :, 1].contiguous(),
+            kv_cache[0],
+            kv_cache[1],
+            slots,
+        )
+
+        # output
+        attn_output = torch.empty_like(query)
+
         # Prefill
-        if prefill:
-            # Copy to layer past
-            layer_past[...] = kv
+        if cu_seqlen_prefill is not None:
             # Expand to query shape
             kv = (
                 kv.unsqueeze(2)
@@ -304,18 +315,14 @@ class FlashRWLargeAttention(torch.nn.Module):
                 .reshape(-1, self.num_groups * self.num_heads, 2, self.head_size)
             )
 
-            # output
-            attn_output = torch.empty_like(query)
             # flash attention
             flash_attn_cuda.fwd(
                 query,
                 torch.select(kv, dim=2, index=0),
                 torch.select(kv, dim=2, index=1),
                 attn_output,
-                start_seq,
-                end_seq,
-                start_seq,
-                end_seq,
+                cu_seqlen_prefill,
+                cu_seqlen_prefill,
                 max_s,
                 max_s,
                 0.0,
@@ -328,36 +335,19 @@ class FlashRWLargeAttention(torch.nn.Module):
             )
         # Decode
         else:
-            # Add present to the layer_past tensor at the correct indices
-            layer_past[past_present_indices] = kv
-            # Expand to query shape
-            kv = (
-                layer_past.unsqueeze(2)
-                .expand(-1, self.num_groups, self.num_heads, 2, self.head_size)
-                .reshape(-1, self.num_groups * self.num_heads, 2, self.head_size)
-            )
-
-            # output
-            attn_output = torch.empty_like(query)
-            # flash attention
-            flash_attn_cuda.fwd(
-                query,
-                torch.select(kv, dim=2, index=0),
-                torch.select(kv, dim=2, index=1),
+            # kv_cache[1] => [num_blocks, num_groups, head_size, block_size]
+            block_size = kv_cache[1].shape[3]
+            vllm_attention_ops.single_query_cached_kv_attention(
                 attn_output,
-                start_seq_q,
-                end_seq_q,
-                start_seq,
-                end_seq,
-                1,
-                max_s,
-                0.0,
+                query,
+                kv_cache[0],
+                kv_cache[1],
+                self.kv_head_mapping,
                 self.softmax_scale,
-                False,
-                False,
-                False,
-                0,
-                None,
+                block_tables,
+                input_lengths,
+                block_size,
+                max_s,
             )
 
         return self.dense(
@@ -432,14 +422,12 @@ class FlashRWLayer(nn.Module):
         residual,
         cos,
         sin,
-        start_seq,
-        end_seq,
-        start_seq_q,
-        end_seq_q,
+        cu_seqlen_prefill,
+        kv_cache,
+        block_tables,
+        slots,
+        input_lengths,
         max_s,
-        layer_past,
-        past_present_indices,
-        prefill,
     ):
         if self.parallel_attn:
             ln_hidden_states, residual = self.input_layernorm(hidden_states, residual)
@@ -448,14 +436,12 @@ class FlashRWLayer(nn.Module):
                 ln_hidden_states,
                 cos,
                 sin,
-                start_seq,
-                end_seq,
-                start_seq_q,
-                end_seq_q,
+                cu_seqlen_prefill,
+                kv_cache,
+                block_tables,
+                slots,
+                input_lengths,
                 max_s,
-                layer_past,
-                past_present_indices,
-                prefill,
             )
 
             mlp_output = self.mlp(ln_hidden_states)
@@ -472,14 +458,12 @@ class FlashRWLayer(nn.Module):
                 hidden_states,
                 cos,
                 sin,
-                start_seq,
-                end_seq,
-                start_seq_q,
-                end_seq_q,
+                cu_seqlen_prefill,
+                kv_cache,
+                block_tables,
+                slots,
+                input_lengths,
                 max_s,
-                layer_past,
-                past_present_indices,
-                prefill,
             )
 
             hidden_states, residual = self.post_attention_layernorm(
@@ -523,14 +507,12 @@ class FlashRWLargeLayer(nn.Module):
         residual,
         cos,
         sin,
-        start_seq,
-        end_seq,
-        start_seq_q,
-        end_seq_q,
+        cu_seqlen_prefill,
+        kv_cache,
+        block_tables,
+        slots,
+        input_lengths,
         max_s,
-        layer_past,
-        past_present_indices,
-        prefill,
     ):
         ln_attn, residual = self.ln_attn(hidden_states, residual)
         ln_mlp, _ = self.ln_mlp(residual)
@@ -540,14 +522,12 @@ class FlashRWLargeLayer(nn.Module):
             ln_attn,
             cos,
             sin,
-            start_seq,
-            end_seq,
-            start_seq_q,
-            end_seq_q,
+            cu_seqlen_prefill,
+            kv_cache,
+            block_tables,
+            slots,
+            input_lengths,
             max_s,
-            layer_past,
-            past_present_indices,
-            prefill,
         )
 
         # MLP.
@@ -580,11 +560,7 @@ class FlashRWModel(FlashRWPreTrainedModel):
                     for layer_id in range(config.num_hidden_layers)
                 ]
             )
-            self.cache_size = (
-                2,
-                self.h[0].self_attention.num_heads_kv,
-                self.h[0].self_attention.head_size,
-            )
+            self.cache_size = self.h[0].self_attention.num_heads_kv
         elif config.model_type == "RefinedWeb":
             self.h = nn.ModuleList(
                 [
@@ -592,11 +568,7 @@ class FlashRWModel(FlashRWPreTrainedModel):
                     for layer_id in range(config.num_hidden_layers)
                 ]
             )
-            self.cache_size = (
-                self.h[0].self_attention.num_groups,
-                2,
-                self.h[0].self_attention.head_size,
-            )
+            self.cache_size = self.h[0].self_attention.num_groups
         else:
             raise NotImplementedError(
                 f"model_type {config.model_type} is not supported."
@@ -612,38 +584,17 @@ class FlashRWModel(FlashRWPreTrainedModel):
 
     def forward(
         self,
-        input_ids,
-        position_ids,
-        start_seq,
-        end_seq,
-        start_seq_q,
-        end_seq_q,
-        max_s,
-        past_present_indices,
-        past_key_values=None,
-        pre_allocate_past_size: Optional[int] = None,
-    ):
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        cu_seqlen_prefill: Optional[torch.Tensor],
+        kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
+        block_tables: torch.Tensor,
+        slots: torch.Tensor,
+        input_lengths: torch.Tensor,
+        max_s: int,
+    ) -> torch.Tensor:
         hidden_states = self.word_embeddings(input_ids)
 
-        # Prefill
-        if past_key_values is None:
-            assert pre_allocate_past_size is not None
-
-            prefill = True
-
-            # Create past tensor
-            # We create a tensor of the same size as input_ids as we don't want to slice at every layer
-            past_key_values = hidden_states.new_empty(
-                (
-                    len(input_ids),
-                    len(self.h),
-                    *self.cache_size,
-                )
-            )
-        # Decode
-        else:
-            prefill = False
-
         # Get rotary cos and sin for this forward
         # Avoid to index in each layer
         cos, sin = self.h[0].self_attention.rotary_emb.get_cos_sin(
@@ -657,32 +608,17 @@ class FlashRWModel(FlashRWPreTrainedModel):
                 residual,
                 cos,
                 sin,
-                start_seq,
-                end_seq,
-                start_seq_q,
-                end_seq_q,
+                cu_seqlen_prefill,
+                kv_cache[i],
+                block_tables,
+                slots,
+                input_lengths,
                 max_s,
-                torch.select(past_key_values, dim=1, index=i),
-                past_present_indices,
-                prefill,
             )
 
-        if prefill:
-            present = past_key_values
-            # Create padded past tensor
-            past_key_values = hidden_states.new_empty(
-                (
-                    pre_allocate_past_size,
-                    len(self.h),
-                    *self.cache_size,
-                )
-            )
-            # We slice only once instead of at every layer
-            past_key_values[past_present_indices] = present
-
         hidden_states, _ = self.ln_f(hidden_states, residual)
 
-        return hidden_states, past_key_values
+        return hidden_states
 
 
 class FlashRWForCausalLM(FlashRWPreTrainedModel):
@@ -697,31 +633,27 @@ class FlashRWForCausalLM(FlashRWPreTrainedModel):
 
     def forward(
         self,
-        input_ids,
-        position_ids,
-        start_seq,
-        end_seq,
-        start_seq_q,
-        end_seq_q,
-        max_s,
-        past_present_indices,
-        past_key_values: Optional[torch.Tensor] = None,
-        pre_allocate_past_size: Optional[int] = None,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        cu_seqlen_prefill: Optional[torch.Tensor],
+        kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
+        block_tables: torch.Tensor,
+        slots: torch.Tensor,
+        input_lengths: torch.Tensor,
+        max_s: int,
         lm_head_indices: Optional[torch.Tensor] = None,
-    ):
-        hidden_states, present = self.transformer(
+    ) -> torch.Tensor:
+        hidden_states = self.transformer(
             input_ids,
             position_ids,
-            start_seq,
-            end_seq,
-            start_seq_q,
-            end_seq_q,
+            cu_seqlen_prefill,
+            kv_cache,
+            block_tables,
+            slots,
+            input_lengths,
             max_s,
-            past_present_indices,
-            past_key_values,
-            pre_allocate_past_size,
         )
         if lm_head_indices is not None:
             hidden_states = hidden_states[lm_head_indices]
         logits = self.lm_head(hidden_states)
-        return logits, present
+        return logits

server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py

@@ -3,10 +3,15 @@ import torch.distributed
 
 from torch import nn
 from transformers.activations import ACT2FN
-from typing import Optional
+from typing import Optional, List, Tuple
 
 # Flash attention imports
 import flash_attn_cuda
+
+# vllm imports
+import vllm_cache_ops
+import vllm_attention_ops
+
 from text_generation_server.utils.layers import (
     TensorParallelRowLinear,
     TensorParallelColumnLinear,
@@ -96,6 +101,7 @@ def _load_multi_mqa_gptq(
 def _load_multi_mqa(
     config, prefix: str, weights, bias: bool, head_size, num_heads, hidden_size
 ):
+
     if any("c_attn" in k for k in weights.routing.keys()):
         slice_ = weights._get_slice(f"{prefix}.c_attn.weight")
         shape = slice_.get_shape()
@@ -239,18 +245,19 @@ class FlashMQAttention(torch.nn.Module):
         self.c_proj = load_row(
             config, prefix=f"{prefix}.c_proj", weights=weights, bias=True
         )
+        self.kv_head_mapping = torch.zeros(
+            self.num_heads, dtype=torch.int32, device=weights.device
+        )
 
     def forward(
         self,
         hidden_states,
-        start_seq,
-        end_seq,
-        start_seq_q,
-        end_seq_q,
+        cu_seqlen_prefill,
+        kv_cache,
+        block_tables,
+        slots,
+        input_lengths,
         max_s,
-        layer_past,
-        past_present_indices,
-        prefill,
     ):
         qkv = self.c_attn(hidden_states)
 
@@ -263,10 +270,15 @@ class FlashMQAttention(torch.nn.Module):
         query = query.view(-1, self.num_heads, self.head_size)
         key_value = key_value.view(-1, 2, 1, self.head_size)
 
+        vllm_cache_ops.reshape_and_cache(
+            key_value[:, 0], key_value[:, 1], kv_cache[0], kv_cache[1], slots
+        )
+
+        # output
+        attn_output = torch.empty_like(query)
+
         # Prefill
-        if prefill:
-            # Copy to layer past
-            layer_past[...] = key_value
+        if cu_seqlen_prefill is not None:
             # Expand from 1 to num_heads
             key_value = key_value.expand(-1, 2, self.num_heads, self.head_size)
 
@@ -278,10 +290,8 @@ class FlashMQAttention(torch.nn.Module):
                 torch.select(key_value, dim=1, index=0),
                 torch.select(key_value, dim=1, index=1),
                 attn_output,
-                start_seq,
-                end_seq,
-                start_seq,
-                end_seq,
+                cu_seqlen_prefill,
+                cu_seqlen_prefill,
                 max_s,
                 max_s,
                 0.0,
@@ -294,32 +304,19 @@ class FlashMQAttention(torch.nn.Module):
             )
         # Decode
         else:
-            # Add present to the layer_past tensor at the correct indices
-            layer_past[past_present_indices] = key_value
-            # Expand from 1 to num_heads
-            key_value = layer_past.expand(-1, 2, self.num_heads, self.head_size)
-
-            # output
-            attn_output = torch.empty_like(query)
-            # flash attention
-            flash_attn_cuda.fwd(
-                query,
-                torch.select(key_value, dim=1, index=0),
-                torch.select(key_value, dim=1, index=1),
+            # kv_cache[1] => [num_blocks, 1, head_size, block_size]
+            block_size = kv_cache[1].shape[3]
+            vllm_attention_ops.single_query_cached_kv_attention(
                 attn_output,
-                start_seq_q,
-                end_seq_q,
-                start_seq,
-                end_seq,
-                1,
-                max_s,
-                0.0,
+                query,
+                kv_cache[0],
+                kv_cache[1],
+                self.kv_head_mapping,
                 self.softmax_scale,
-                False,
-                False,
-                False,
-                0,
-                None,
+                block_tables,
+                input_lengths,
+                block_size,
+                max_s,
             )
 
         return self.c_proj(attn_output.view(-1, self.num_heads * self.head_size))
@@ -379,27 +376,23 @@ class Block(nn.Module):
         self,
         hidden_states,
         residual,
-        start_seq,
-        end_seq,
-        start_seq_q,
-        end_seq_q,
+        cu_seqlen_prefill,
+        kv_cache,
+        block_tables,
+        slots,
+        input_lengths,
         max_s,
-        layer_past,
-        past_present_indices,
-        prefill,
     ):
         hidden_states, residual = self.ln_1(hidden_states, residual)
 
         hidden_states = self.attn(
             hidden_states,
-            start_seq,
-            end_seq,
-            start_seq_q,
-            end_seq_q,
+            cu_seqlen_prefill,
+            kv_cache,
+            block_tables,
+            slots,
+            input_lengths,
             max_s,
-            layer_past,
-            past_present_indices,
-            prefill,
         )
 
         hidden_states, residual = self.ln_2(hidden_states, residual)
@@ -445,64 +438,36 @@ class FlashSantacoderModel(nn.Module):
 
     def forward(
         self,
-        input_ids,
-        position_ids,
-        start_seq,
-        end_seq,
-        start_seq_q,
-        end_seq_q,
-        max_s,
-        past_present_indices,
-        past_key_values=None,
-        pre_allocate_past_size: Optional[int] = None,
-    ):
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        cu_seqlen_prefill: Optional[torch.Tensor],
+        kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
+        block_tables: torch.Tensor,
+        slots: torch.Tensor,
+        input_lengths: torch.Tensor,
+        max_s: int,
+    ) -> torch.Tensor:
         hidden_states = self.wte(input_ids) + self.wpe(position_ids)
 
         if self.process_group.size() > 1:
             torch.distributed.all_reduce(hidden_states, group=self.process_group)
 
-        # Prefill
-        if past_key_values is None:
-            assert pre_allocate_past_size is not None
-
-            prefill = True
-
-            # Create past tensor
-            # We create a tensor of the same size as input_ids as we don't want to slice at every layer
-            past_key_values = hidden_states.new_zeros(
-                (len(input_ids), len(self.h), 2, 1, self.head_size)
-            )
-        # Decode
-        else:
-            prefill = False
-
         residual = None
         for i, layer in enumerate(self.h):
             hidden_states, residual = layer(
                 hidden_states,
                 residual,
-                start_seq,
-                end_seq,
-                start_seq_q,
-                end_seq_q,
+                cu_seqlen_prefill,
+                kv_cache[i],
+                block_tables,
+                slots,
+                input_lengths,
                 max_s,
-                torch.select(past_key_values, dim=1, index=i),
-                past_present_indices,
-                prefill,
             )
 
-        if prefill:
-            present = past_key_values
-            # Create padded past tensor
-            past_key_values = hidden_states.new_empty(
-                (pre_allocate_past_size, len(self.h), 2, 1, self.head_size)
-            )
-            # We slice only once instead of at every layer
-            past_key_values[past_present_indices] = present
-
         hidden_states, _ = self.ln_f(hidden_states, residual)
 
-        return hidden_states, past_key_values
+        return hidden_states
 
 
 class FlashSantacoderForCausalLM(nn.Module):
@@ -539,31 +504,27 @@ class FlashSantacoderForCausalLM(nn.Module):
             
     def forward(
         self,
-        input_ids,
-        position_ids,
-        start_seq,
-        end_seq,
-        start_seq_q,
-        end_seq_q,
-        max_s,
-        past_present_indices,
-        past_key_values: Optional[torch.Tensor] = None,
-        pre_allocate_past_size: Optional[int] = None,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        cu_seqlen_prefill: Optional[torch.Tensor],
+        kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
+        block_tables: torch.Tensor,
+        slots: torch.Tensor,
+        input_lengths: torch.Tensor,
+        max_s: int,
         lm_head_indices: Optional[torch.Tensor] = None,
-    ):
-        hidden_states, present = self.transformer(
+    ) -> torch.Tensor:
+        hidden_states = self.transformer(
             input_ids,
             position_ids,
-            start_seq,
-            end_seq,
-            start_seq_q,
-            end_seq_q,
+            cu_seqlen_prefill,
+            kv_cache,
+            block_tables,
+            slots,
+            input_lengths,
             max_s,
-            past_present_indices,
-            past_key_values,
-            pre_allocate_past_size,
         )
         if lm_head_indices is not None:
             hidden_states = hidden_states[lm_head_indices]
         logits = self.lm_head(hidden_states)
-        return logits, present
+        return logits

server/text_generation_server/models/custom_modeling/t5_modeling.py

@@ -1004,7 +1004,9 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
         try:
             self.shared = TensorParallelEmbedding(prefix="shared", weights=weights)
         except RuntimeError:
-            self.shared = TensorParallelEmbedding(prefix="encoder.embed_tokens", weights=weights)
+            self.shared = TensorParallelEmbedding(
+                prefix="encoder.embed_tokens", weights=weights
+            )
 
         encoder_config = copy.deepcopy(config)
         encoder_config.is_decoder = False

server/text_generation_server/models/flash_causal_lm.py

@@ -1,11 +1,14 @@
+import math
+import itertools
 import torch
 import torch.distributed
 
 import numpy as np
 
 from dataclasses import dataclass
+from loguru import logger
 from opentelemetry import trace
-from transformers import AutoTokenizer, PreTrainedTokenizerBase, PreTrainedModel
+from transformers import PreTrainedTokenizerBase
 from typing import Optional, Tuple, List, Type, Union, Dict
 
 from text_generation_server.models import Model
@@ -20,6 +23,92 @@ from text_generation_server.utils import StoppingCriteria, HeterogeneousNextToke
 
 tracer = trace.get_tracer(__name__)
 
+BLOCK_SIZE = 16
+# Will be set in warmup
+CACHE_MANAGER: Optional["CacheManager"] = None
+
+
+class CacheManager:
+    def __init__(
+        self,
+        num_blocks: int,
+        num_layers: int,
+        num_heads: int,
+        head_size: int,
+        dtype: torch.dtype,
+        device: torch.device,
+    ):
+        self.block_size = BLOCK_SIZE
+
+        element_size = torch.tensor([], dtype=dtype).element_size()
+        x = self.block_size // element_size
+
+        self.kv_cache = [
+            (
+                torch.empty(
+                    (num_blocks, num_heads, head_size // x, self.block_size, x),
+                    dtype=dtype,
+                    device=device,
+                ),
+                torch.empty(
+                    (num_blocks, num_heads, head_size, self.block_size),
+                    dtype=dtype,
+                    device=device,
+                ),
+            )
+            for _ in range(num_layers)
+        ]
+        self.free_block_mask = torch.ones(num_blocks, dtype=torch.int32, device="cpu")
+        self.slots = torch.arange(
+            0, num_blocks * self.block_size, dtype=torch.int32
+        ).view(num_blocks, self.block_size)
+
+    def allocate(self, batch: "FlashCausalLMBatch"):
+        # Get free blocks indices by finding values in mask that are not set to 0
+        free_block_indices = self.free_block_mask.nonzero()
+        assert (
+            len(free_block_indices) >= batch.blocks
+        ), f"Out of available cache blocks: asked {batch.blocks}, only {len(free_block_indices)} free blocks"
+
+        # Slice by the number of required blocks
+        block_indices = free_block_indices[: batch.blocks]
+        block_indices = block_indices.flatten()
+
+        # Padded block tables
+        block_tables_tensor = torch.zeros(
+            (len(batch), batch.max_blocks), dtype=torch.int32
+        )
+
+        # Allocate paged attention blocks
+        cumulative_blocks = 0
+        slots = []
+        block_tables = []
+        for i, (needed_blocks, needed_slots) in enumerate(batch.needed_blocks_slots):
+            # Get allocated blocks for this sequence
+            allocated_blocks = block_indices[
+                cumulative_blocks : cumulative_blocks + needed_blocks
+            ]
+            # Get slots for the allocated blocks
+            allocated_slots = self.slots[allocated_blocks].flatten()[:needed_slots]
+
+            slots.append(allocated_slots)
+            block_tables.append(allocated_blocks.tolist())
+            block_tables_tensor[i, :needed_blocks] = allocated_blocks
+            cumulative_blocks += needed_blocks
+
+        batch.needed_blocks_slots = None
+        batch.block_tables = block_tables
+        batch.block_tables_tensor = block_tables_tensor.to(batch.input_ids.device)
+        batch.slots = torch.concat(slots).to(batch.input_ids.device)
+
+        # Allocate the required number of blocks by setting the mask to 0
+        self.free_block_mask[block_indices] = 0
+
+    def free(self, block_indices: Optional[List[int]]):
+        if block_indices is not None and block_indices:
+            # Reset mask
+            self.free_block_mask[block_indices] = 1
+
 
 @dataclass
 class FlashCausalLMBatch(Batch):
@@ -32,23 +121,29 @@ class FlashCausalLMBatch(Batch):
     input_ids: torch.Tensor
     position_ids: torch.Tensor
 
-    # Indices to copy present to the correct indices is the pre-allocated past key values
-    past_present_indices: torch.Tensor
+    # Flash Attention values
+
+    # tensor of length b containing the cumulative sequence lengths of the sequences in the batch, only used in prefill
+    cu_seqlen_prefill: Optional[torch.Tensor]
+
+    # Paged Attention values
+
+    # Set when creating the batch
+    # CPU tensor of length b indicating the start of each sequence in slots
+    start_slots: torch.Tensor
+    # tensor of indices of the currently used slots, length = \sum_{i=0}^{b} s_i in prefill, length = b in decode
+    slot_indices: torch.Tensor
+    # List of tuple of ints representing the number of blocks and slots needed by each sequence
+    needed_blocks_slots: Optional[List[Tuple[int, int]]]
+
+    # Set in prefill by the CacheManager
+    # list of length b of list of length s_i // block_size
+    block_tables: Optional[List[List[int]]]
+    # tensor of size [b, max_seqlen // block_size] holding the paged attention block tables for all sequences
+    block_tables_tensor: Optional[torch.Tensor]
+    # tensor of length \sum_{i=0}^{b} max_s_i  holding the paged attention slots for all sequences
+    slots: Optional[torch.Tensor]
 
-    # tensor of length b holding starting offset of each sequence
-    start_seq: torch.Tensor
-    # tensor of length b holding ending offset of each sequence
-    end_seq: torch.Tensor
-    # tensor of length b holding starting offset of each sequence, only used in prefill
-    start_seq_prefill: Optional[torch.Tensor]
-    # tensor of length b holding ending offset of each sequence, only used in prefill
-    end_seq_prefill: Optional[torch.Tensor]
-    # tensor of length b holding starting offset of each query sequence, only used in decode
-    start_seq_q: Optional[torch.Tensor]
-    # tensor of length b holding ending offset of each query sequence, only used in decode
-    end_seq_q: Optional[torch.Tensor]
-    # past key values, only used in decode
-    past_key_values: Optional[torch.Tensor]
     max_seqlen: int
 
     # Prefill metadata tensors to efficiently compute logprobs
@@ -62,6 +157,7 @@ class FlashCausalLMBatch(Batch):
 
     # Lengths of all generations present in the batch
     input_lengths: List[int]
+    input_lengths_tensor: torch.Tensor
     prefix_offsets: List[Optional[int]]
     read_offsets: List[Optional[int]]
 
@@ -69,15 +165,17 @@ class FlashCausalLMBatch(Batch):
     next_token_chooser: HeterogeneousNextTokenChooser
     stopping_criterias: List[StoppingCriteria]
 
-    # Maximum number of tokens this batch will grow to
-    max_tokens: int
+    # Number of blocks in this batch
+    blocks: int
+    # Maximum number of blocks
+    max_blocks: int
 
     def to_pb(self) -> generate_pb2.CachedBatch:
         return generate_pb2.CachedBatch(
             id=self.batch_id,
             request_ids=[r.id for r in self.requests],
             size=len(self),
-            max_tokens=self.max_tokens,
+            max_tokens=self.blocks * BLOCK_SIZE,
         )
 
     @classmethod
@@ -99,12 +197,10 @@ class FlashCausalLMBatch(Batch):
         )["input_ids"]
 
         position_ids = []
-        past_present_indices = []
-        start_seq = []
-        end_seq = []
-        start_seq_prefill = []
-        end_seq_prefill = []
-        max_seqlen = 0
+        cu_seqlen_prefill = [0]
+        needed_blocks_slots = []
+        start_slots = []
+        slot_indices = []
 
         input_lengths = []
         prefix_offsets = []
@@ -126,7 +222,10 @@ class FlashCausalLMBatch(Batch):
         cumulative_max_length = 0
         prefill_out_cumulative_length = 0
 
+        blocks = 0
+        max_seqlen = 0
         max_length = 0
+        max_blocks = 0
 
         # Parse batch
         for i, (r, tokenized_input) in enumerate(
@@ -138,7 +237,6 @@ class FlashCausalLMBatch(Batch):
             tokenized_input = tokenized_input[-r.truncate :]
 
             input_length = len(tokenized_input)
-            max_seqlen = max(max_seqlen, input_length)
             input_lengths.append(input_length)
 
             prefix_offsets.append(input_length - 5)
@@ -151,10 +249,7 @@ class FlashCausalLMBatch(Batch):
             position_ids.append(request_position_ids)
 
             # Add cumulative lengths of all previous inputs
-            start_seq_prefill.append(cumulative_length)
-            end_seq_prefill.append(cumulative_length + input_length)
-            start_seq.append(cumulative_max_length)
-            end_seq.append(cumulative_max_length + input_length)
+            cu_seqlen_prefill.append(cumulative_length + input_length)
 
             next_token_chooser_parameters.append(r.parameters)
 
@@ -164,6 +259,21 @@ class FlashCausalLMBatch(Batch):
             max_new_tokens = stopping_criteria.max_new_tokens
             stopping_criterias.append(stopping_criteria)
 
+            # Paged attention
+            # Remove one as the first token des not have a past
+            total_tokens = input_length + max_new_tokens - 1
+            needed_blocks = math.ceil(total_tokens / BLOCK_SIZE)
+            blocks += needed_blocks
+            needed_blocks_slots.append((needed_blocks, total_tokens))
+            start_slots.append(cumulative_max_length)
+
+            request_slot_indices = torch.arange(
+                cumulative_max_length,
+                cumulative_max_length + input_length,
+                dtype=torch.int64,
+            )
+            slot_indices.append(request_slot_indices)
+
             all_prefill_logprobs = all_prefill_logprobs and r.prefill_logprobs
             no_prefill_logprobs = no_prefill_logprobs and not r.prefill_logprobs
 
@@ -184,22 +294,17 @@ class FlashCausalLMBatch(Batch):
                 prefill_cu_outlens.append(prefill_out_cumulative_length + 1)
                 prefill_out_cumulative_length += 1
 
-            request_past_present_indices = torch.arange(
-                cumulative_max_length,
-                cumulative_max_length + input_length,
-                dtype=torch.int64,
-            )
-            past_present_indices.append(request_past_present_indices)
-
             # Update
-            # Remove one as the first token des not have a past
             cumulative_length += input_length
-            cumulative_max_length += input_length + max_new_tokens - 1
+            cumulative_max_length += total_tokens
+            max_seqlen = max(max_seqlen, input_length)
+            max_blocks = max(max_blocks, needed_blocks)
             max_length = max(max_length, input_length + max_new_tokens)
 
         next_token_chooser = HeterogeneousNextTokenChooser.from_pb(
             next_token_chooser_parameters, dtype, device
         )
+        start_slots = torch.tensor(start_slots, dtype=torch.int64)
 
         # Padded all_input_ids_tensor
         all_input_ids_tensor = np.zeros(
@@ -212,41 +317,32 @@ class FlashCausalLMBatch(Batch):
         all_input_ids_tensor = torch.tensor(
             all_input_ids_tensor, dtype=torch.int64, device=device
         )
-        start_seq = torch.tensor(start_seq, device=device, dtype=torch.int32)
-        end_seq = torch.tensor(end_seq, device=device, dtype=torch.int32)
 
         if len(pb.requests) > 1:
             input_ids = np.concatenate(all_input_ids, dtype=np.int64)
             position_ids = torch.cat(position_ids)
-
-            past_present_indices = np.concatenate(past_present_indices, dtype=np.int64)
-
-            start_seq_prefill = torch.tensor(
-                start_seq_prefill, device=device, dtype=torch.int32
-            )
-            end_seq_prefill = torch.tensor(
-                end_seq_prefill, device=device, dtype=torch.int32
-            )
+            slot_indices = torch.cat(slot_indices)
         else:
             input_ids = all_input_ids[0]
             position_ids = position_ids[0]
+            slot_indices = slot_indices[0]
 
-            past_present_indices = past_present_indices[0]
-
-            start_seq_prefill = start_seq
-            end_seq_prefill = end_seq
+        cu_seqlen_prefill = torch.tensor(
+            cu_seqlen_prefill, device=device, dtype=torch.int32
+        )
 
+        position_ids = position_ids.to(device)
+        slot_indices = slot_indices.to(device)
         input_ids = torch.tensor(input_ids, dtype=torch.int64, device=device)
-        position_ids = torch.tensor(position_ids, dtype=torch.int32, device=device)
-        past_present_indices = torch.tensor(
-            past_present_indices, device=device, dtype=torch.int64
+        input_lengths_tensor = torch.tensor(
+            input_lengths, dtype=torch.int32, device=device
         )
 
         if all_prefill_logprobs:
             prefill_head_indices = None
-            prefill_next_token_indices = end_seq_prefill - 1
+            prefill_next_token_indices = cu_seqlen_prefill[1:] - 1
         elif no_prefill_logprobs:
-            prefill_head_indices = end_seq_prefill - 1
+            prefill_head_indices = cu_seqlen_prefill[1:] - 1
             prefill_next_token_indices = None
         else:
             prefill_head_indices = torch.tensor(
@@ -262,26 +358,27 @@ class FlashCausalLMBatch(Batch):
             requests_idx_mapping=requests_idx_mapping,
             input_ids=input_ids,
             position_ids=position_ids,
-            past_present_indices=past_present_indices,
-            start_seq=start_seq,
-            end_seq=end_seq,
-            start_seq_prefill=start_seq_prefill,
-            end_seq_prefill=end_seq_prefill,
-            start_seq_q=None,
-            end_seq_q=None,
+            cu_seqlen_prefill=cu_seqlen_prefill,
+            start_slots=start_slots,
+            slot_indices=slot_indices,
+            needed_blocks_slots=needed_blocks_slots,
+            block_tables=None,
+            block_tables_tensor=None,
+            slots=None,
             max_seqlen=max_seqlen,
             prefill_head_indices=prefill_head_indices,
             prefill_next_token_indices=prefill_next_token_indices,
             prefill_cu_outlens=prefill_cu_outlens,
-            past_key_values=None,
             input_lengths=input_lengths,
+            input_lengths_tensor=input_lengths_tensor,
             prefix_offsets=prefix_offsets,
             read_offsets=read_offsets,
             all_input_ids=all_input_ids,
             all_input_ids_tensor=all_input_ids_tensor,
             next_token_chooser=next_token_chooser,
             stopping_criterias=stopping_criterias,
-            max_tokens=cumulative_max_length,
+            blocks=blocks,
+            max_blocks=max_blocks,
         )
 
     @tracer.start_as_current_span("filter")
@@ -294,28 +391,24 @@ class FlashCausalLMBatch(Batch):
 
         device = self.input_ids.device
 
-        # Cumulative length
-        cumulative_max_length = 0
-
         # New values after filtering
         requests_idx_mapping = {}
 
         # Used to index into tensors
         indices = []
 
-        # past indices to keep
-        past_indices = torch.zeros(
-            self.past_key_values.shape[0], dtype=torch.bool, device=device
+        # slots to keep after filtering
+        slot_filtering_indices = torch.zeros(
+            self.slots.shape[0], dtype=torch.bool, device=device
         )
 
         # Create on CPU to only move to GPU once instead of at every copy
-        start_seq = torch.empty(len(request_ids), dtype=torch.int32)
-        end_seq = torch.empty(len(request_ids), dtype=torch.int32)
-        start_seq_q = self.start_seq_q[: len(request_ids)]
-        end_seq_q = self.end_seq_q[: len(request_ids)]
+        slot_indices = torch.empty(len(request_ids), dtype=torch.int64)
         max_seqlen = 0
 
         requests = []
+        start_slots = []
+        block_tables = []
         all_input_ids = []
 
         input_lengths = []
@@ -324,6 +417,11 @@ class FlashCausalLMBatch(Batch):
 
         stopping_criterias = []
 
+        blocks = 0
+        max_blocks = 0
+        # Cumulative length
+        cumulative_max_length = 0
+
         for i, request_id in enumerate(request_ids):
             idx = self.requests_idx_mapping[request_id]
             indices.append(idx)
@@ -348,28 +446,51 @@ class FlashCausalLMBatch(Batch):
                 stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
             )
 
+            request_block_table = self.block_tables[idx]
+            blocks += len(request_block_table)
+            block_tables.append(request_block_table)
+            start_slots.append(cumulative_max_length)
+
             # Copy to tensor (CPU)
-            start_seq[i] = cumulative_max_length
-            end_seq[i] = cumulative_max_length + request_input_length
+            slot_indices[i] = cumulative_max_length + request_input_length - 1
 
             # Set slice
-            past_indices[
-                self.start_seq[idx] : self.end_seq[idx] + remaining_tokens - 1
+            slot_filtering_indices[
+                self.start_slots[idx] : self.start_slots[idx]
+                + request_input_length
+                + remaining_tokens
+                - 1
             ] = True
 
             cumulative_max_length += request_input_length + remaining_tokens - 1
 
+            max_blocks = max(max_blocks, len(request_block_table))
+
+        global CACHE_MANAGER
+        block_indices_to_free = []
+        # Iterate on all requests
+        for i, r in enumerate(self.requests):
+            # Filter requests that are not part of the new batch
+            if r.id not in requests_idx_mapping.keys():
+                block_indices_to_free.extend(self.block_tables[i])
+        # Free blocks
+        CACHE_MANAGER.free(block_indices_to_free)
+        # Needed to avoid dropping blocks when the batches will go out of scope
+        self.block_tables = None
+
         # Index into tensors
         input_ids = self.input_ids[indices]
         position_ids = self.position_ids[indices]
         all_input_ids_tensor = self.all_input_ids_tensor[indices]
+        block_tables_tensor = self.block_tables_tensor[indices]
+        input_lengths_tensor = self.input_lengths_tensor[indices]
+        slots = self.slots[slot_filtering_indices]
         next_token_chooser = self.next_token_chooser.filter(indices)
-        past_key_values = self.past_key_values[past_indices]
+
+        start_slots = torch.tensor(start_slots, dtype=torch.int64)
 
         # Move to GPU now that we have the whole tensor
-        start_seq = start_seq.to(device)
-        end_seq = end_seq.to(device)
-        past_present_indices = end_seq - 1
+        slot_indices = slot_indices.to(device)
 
         return FlashCausalLMBatch(
             batch_id=self.batch_id,
@@ -377,26 +498,27 @@ class FlashCausalLMBatch(Batch):
             requests_idx_mapping=requests_idx_mapping,
             input_ids=input_ids,
             position_ids=position_ids,
-            past_present_indices=past_present_indices,
-            start_seq=start_seq,
-            end_seq=end_seq,
-            start_seq_prefill=None,
-            end_seq_prefill=None,
-            start_seq_q=start_seq_q,
-            end_seq_q=end_seq_q,
+            cu_seqlen_prefill=None,
+            start_slots=start_slots,
+            slot_indices=slot_indices,
+            needed_blocks_slots=None,
+            block_tables=block_tables,
+            block_tables_tensor=block_tables_tensor,
+            slots=slots,
             max_seqlen=max_seqlen,
             prefill_head_indices=None,
             prefill_next_token_indices=None,
             prefill_cu_outlens=None,
-            past_key_values=past_key_values,
             input_lengths=input_lengths,
+            input_lengths_tensor=input_lengths_tensor,
             prefix_offsets=prefix_offsets,
             read_offsets=read_offsets,
             all_input_ids=all_input_ids,
             all_input_ids_tensor=all_input_ids_tensor,
             next_token_chooser=next_token_chooser,
             stopping_criterias=stopping_criterias,
-            max_tokens=cumulative_max_length,
+            blocks=blocks,
+            max_blocks=max_blocks,
         )
 
     @classmethod
@@ -406,22 +528,46 @@ class FlashCausalLMBatch(Batch):
         requests = []
         requests_idx_mapping = {}
 
-        total_batch_size = sum([len(b) for b in batches])
-
-        dtype = batches[0].past_key_values.dtype
-        device = batches[0].input_ids.device
+        blocks = 0
+        total_batch_size = 0
+        total_slots = 0
+        max_blocks = 0
+        max_length = 0
+        max_seqlen = 0
+        for b in batches:
+            total_batch_size += len(b)
+            total_slots += len(b.slots)
+            blocks += b.blocks
+            max_blocks = max(max_blocks, b.max_blocks)
+            max_seqlen = max(max_seqlen, b.max_seqlen)
+            max_length = max(
+                max_length,
+                max(
+                    input_length
+                    + stopping_criteria.max_new_tokens
+                    - stopping_criteria.current_tokens
+                    for input_length, stopping_criteria in zip(
+                        b.input_lengths, b.stopping_criterias
+                    )
+                ),
+            )
 
         input_ids = batches[0].input_ids.new_empty(total_batch_size)
         position_ids = batches[0].position_ids.new_empty(total_batch_size)
-        start_seq = batches[0].start_seq.new_empty(total_batch_size)
-        end_seq = batches[0].end_seq.new_empty(total_batch_size)
-        start_seq_q = torch.arange(
-            0, total_batch_size, device=device, dtype=torch.int32
+        slots = batches[0].slots.new_empty(total_slots)
+        slot_indices = batches[0].slot_indices.new_empty(total_batch_size)
+        input_lengths_tensor = batches[0].input_lengths_tensor.new_empty(
+            total_batch_size
+        )
+        block_tables_tensor = batches[0].block_tables_tensor.new_zeros(
+            (total_batch_size, max_blocks)
+        )
+        all_input_ids_tensor = batches[0].all_input_ids_tensor.new_zeros(
+            (total_batch_size, max_length)
         )
-        end_seq_q = start_seq_q + 1
-        max_seqlen = 0
-        past_key_values = []
 
+        start_slots = []
+        block_tables = []
         all_input_ids = []
 
         input_lengths = []
@@ -433,8 +579,7 @@ class FlashCausalLMBatch(Batch):
 
         # Cumulative length
         cumulative_batch_size = 0
-        max_tokens = 0
-        max_length = 0
+        cumulative_slots = 0
 
         for i, batch in enumerate(batches):
             requests.extend(batch.requests)
@@ -448,16 +593,27 @@ class FlashCausalLMBatch(Batch):
 
             start_index = cumulative_batch_size
             end_index = cumulative_batch_size + len(batch)
+            slots_start_index = cumulative_slots
+            slots_end_index = cumulative_slots + len(batch.slots)
 
             # Copy tensors (GPU)
             input_ids[start_index:end_index] = batch.input_ids
             position_ids[start_index:end_index] = batch.position_ids
+            slot_indices[start_index:end_index] = batch.slot_indices + cumulative_slots
+            input_lengths_tensor[start_index:end_index] = batch.input_lengths_tensor
+            slots[slots_start_index:slots_end_index] = batch.slots
 
-            start_seq[start_index:end_index] = batch.start_seq + max_tokens
-            end_seq[start_index:end_index] = batch.end_seq + max_tokens
+            all_input_ids_tensor[
+                start_index:end_index, : batch.all_input_ids_tensor.shape[1]
+            ] = batch.all_input_ids_tensor[:, :max_length]
 
-            max_seqlen = max(max_seqlen, batch.max_seqlen)
+            block_tables_tensor[
+                start_index:end_index, : batch.block_tables_tensor.shape[1]
+            ] = batch.block_tables_tensor[:, :max_blocks]
 
+            start_slots.append(batch.start_slots + cumulative_slots)
+
+            block_tables.extend(batch.block_tables)
             all_input_ids.extend(batch.all_input_ids)
 
             input_lengths.extend(batch.input_lengths)
@@ -466,73 +622,58 @@ class FlashCausalLMBatch(Batch):
 
             next_token_chooser_parameters.extend([r.parameters for r in batch.requests])
             stopping_criterias.extend(batch.stopping_criterias)
-            past_key_values.append(batch.past_key_values)
 
             # Update
             cumulative_batch_size += len(batch)
-            max_tokens += batch.max_tokens
-            max_length = max(
-                max_length,
-                max(
-                    input_length
-                    + stopping_criteria.max_new_tokens
-                    - stopping_criteria.current_tokens
-                    for input_length, stopping_criteria in zip(
-                        batch.input_lengths, batch.stopping_criterias
-                    )
-                ),
-            )
+            cumulative_slots += len(batch.slots)
 
-        past_key_values = torch.cat(past_key_values, dim=0)
-        past_present_indices = end_seq - 1
-
-        all_input_ids_tensor = torch.zeros(
-            (total_batch_size, max_length), dtype=torch.int64, device=device
-        )
-
-        cumulative_batch_size = 0
-        for i, batch in enumerate(batches):
-            start_index = cumulative_batch_size
-            end_index = cumulative_batch_size + len(batch)
-
-            all_input_ids_tensor[
-                start_index:end_index, : batch.all_input_ids_tensor.shape[1]
-            ] = batch.all_input_ids_tensor[:, :max_length]
-
-            cumulative_batch_size += len(batch)
+        start_slots = torch.concat(start_slots)
 
         next_token_chooser = HeterogeneousNextTokenChooser.from_pb(
-            next_token_chooser_parameters, dtype=dtype, device=device
+            next_token_chooser_parameters,
+            dtype=batches[0].next_token_chooser.dtype,
+            device=batches[0].next_token_chooser.device,
         )
 
+        # Needed to avoid dropping blocks when the batches will go out of scope
+        for b in batches:
+            b.block_tables = None
+
         return FlashCausalLMBatch(
             batch_id=batches[0].batch_id,
             requests=requests,
             requests_idx_mapping=requests_idx_mapping,
             input_ids=input_ids,
             position_ids=position_ids,
-            past_present_indices=past_present_indices,
-            start_seq=start_seq,
-            end_seq=end_seq,
-            start_seq_prefill=None,
-            end_seq_prefill=None,
-            start_seq_q=start_seq_q,
-            end_seq_q=end_seq_q,
+            cu_seqlen_prefill=None,
+            start_slots=start_slots,
+            slot_indices=slot_indices,
+            needed_blocks_slots=None,
+            block_tables=block_tables,
+            block_tables_tensor=block_tables_tensor,
+            slots=slots,
             max_seqlen=max_seqlen,
             prefill_head_indices=None,
             prefill_next_token_indices=None,
             prefill_cu_outlens=None,
-            past_key_values=past_key_values,
             input_lengths=input_lengths,
+            input_lengths_tensor=input_lengths_tensor,
             prefix_offsets=prefix_offsets,
             read_offsets=read_offsets,
             all_input_ids=all_input_ids,
             all_input_ids_tensor=all_input_ids_tensor,
             next_token_chooser=next_token_chooser,
             stopping_criterias=stopping_criterias,
-            max_tokens=max_tokens,
+            blocks=blocks,
+            max_blocks=max_blocks,
         )
 
+    def __del__(self):
+        if self.block_tables is not None and self.block_tables:
+            global CACHE_MANAGER
+            # Free blocks
+            CACHE_MANAGER.free(list(itertools.chain.from_iterable(self.block_tables)))
+
     def __len__(self):
         return len(self.requests)
 
@@ -540,32 +681,19 @@ class FlashCausalLMBatch(Batch):
 class FlashCausalLM(Model):
     def __init__(
         self,
-        model_cls: Type[PreTrainedModel],
-        model_id: str,
-        revision: Optional[str] = None,
-        quantize: Optional[str] = None,
-        trust_remote_code: bool = False,
+        model: torch.nn.Module,
+        tokenizer: PreTrainedTokenizerBase,
+        num_layers: int,
+        num_kv_heads: int,
+        head_size: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        rank: int = 0,
+        world_size: int = 1,
     ):
-        if torch.cuda.is_available():
-            device = torch.device("cuda")
-            dtype = torch.float16
-        else:
-            raise NotImplementedError("FlashCausalLM is only available on GPU")
-
-        tokenizer = AutoTokenizer.from_pretrained(
-            model_id,
-            revision=revision,
-            padding_side="left",
-            truncation_side="left",
-            trust_remote_code=trust_remote_code,
-        )
-        model = model_cls.from_pretrained(
-            model_id,
-            revision=revision,
-            torch_dtype=dtype,
-            load_in_8bit=quantize == "bitsandbytes",
-            trust_remote_code=trust_remote_code,
-        ).to(device)
+        self.num_layers = num_layers
+        self.num_kv_heads = num_kv_heads
+        self.head_size = head_size
 
         super(FlashCausalLM, self).__init__(
             model=model,
@@ -573,12 +701,38 @@ class FlashCausalLM(Model):
             requires_padding=False,
             dtype=dtype,
             device=device,
+            rank=rank,
+            world_size=world_size,
         )
 
     @property
     def batch_type(self) -> Type[FlashCausalLMBatch]:
         return FlashCausalLMBatch
 
+    def warmup(self, batch: FlashCausalLMBatch, max_total_tokens: int):
+        global CACHE_MANAGER
+
+        torch.cuda.empty_cache()
+        try:
+            CACHE_MANAGER = CacheManager(
+                # Adds some wiggle room
+                math.ceil(max_total_tokens / BLOCK_SIZE) + 10,
+                self.num_layers,
+                self.num_kv_heads,
+                self.head_size,
+                self.dtype,
+                self.device,
+            )
+            _, batch = self.generate_token(batch)
+        except Exception as e:
+            logger.exception(
+                f"Not enough memory to handle {max_total_tokens} total tokens with {len(batch.input_ids)} "
+                f"prefill tokens. "
+                f"You need to decrease `--max-batch-total-tokens` or `--max-batch-prefill-tokens`"
+            )
+            raise e
+        del batch
+
     def decode(self, generated_ids: Union[torch.Tensor, List[int]]) -> str:
         return self.tokenizer.decode(
             generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
@@ -588,28 +742,25 @@ class FlashCausalLM(Model):
         self,
         input_ids: torch.Tensor,
         position_ids: torch.Tensor,
-        start_seq: torch.Tensor,
-        end_seq: torch.Tensor,
-        start_seq_q: Optional[torch.Tensor],
-        end_seq_q: Optional[torch.Tensor],
+        cu_seqlen_prefill: Optional[torch.Tensor],
+        block_tables: torch.Tensor,
+        slots: torch.Tensor,
+        input_lengths: torch.Tensor,
         max_s: int,
-        past_present_indices: torch.Tensor,
-        past_key_values: Optional = None,
-        pre_allocate_past_size: Optional[int] = None,
         lm_head_indices: Optional[torch.Tensor] = None,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
+        global CACHE_MANAGER
+
         # Model Forward
         return self.model.forward(
             input_ids=input_ids,
             position_ids=position_ids,
-            start_seq=start_seq,
-            end_seq=end_seq,
-            start_seq_q=start_seq_q,
-            end_seq_q=end_seq_q,
+            cu_seqlen_prefill=cu_seqlen_prefill,
+            kv_cache=CACHE_MANAGER.kv_cache,
+            block_tables=block_tables,
+            slots=slots,
+            input_lengths=input_lengths,
             max_s=max_s,
-            past_present_indices=past_present_indices,
-            past_key_values=past_key_values,
-            pre_allocate_past_size=pre_allocate_past_size,
             lm_head_indices=lm_head_indices,
         )
 
@@ -617,31 +768,21 @@ class FlashCausalLM(Model):
     def generate_token(
         self, batch: FlashCausalLMBatch
     ) -> Tuple[List[Generation], Optional[FlashCausalLMBatch]]:
-        prefill = batch.past_key_values is None
+        prefill = batch.cu_seqlen_prefill is not None
         prefill_logprobs = batch.prefill_next_token_indices is not None
 
-        if prefill:
-            # Ask to pre-allocate kv to its max size
-            # == Sum over batch size (number of tokens + max_new_tokens) - batch size
-            pre_allocate_past_size = batch.max_tokens
-            start_seq = batch.start_seq_prefill
-            end_seq = batch.end_seq_prefill
-        else:
-            pre_allocate_past_size = None
-            start_seq = batch.start_seq
-            end_seq = batch.end_seq
+        if batch.needed_blocks_slots:
+            # Allocate blocks to this batch
+            CACHE_MANAGER.allocate(batch)
 
-        out, present = self.forward(
+        out = self.forward(
             batch.input_ids,
             batch.position_ids,
-            start_seq,
-            end_seq,
-            batch.start_seq_q,
-            batch.end_seq_q,
+            batch.cu_seqlen_prefill,
+            batch.block_tables_tensor,
+            batch.slots[batch.slot_indices],
+            batch.input_lengths_tensor,
             batch.max_seqlen,
-            batch.past_present_indices,
-            batch.past_key_values,
-            pre_allocate_past_size,
             batch.prefill_head_indices,
         )
 
@@ -662,15 +803,10 @@ class FlashCausalLM(Model):
                 # When batch == 1, we will just use the batch.input_ids values directly
                 prefill_tokens_indices = batch.input_ids.new_zeros(len(out))
 
-            # Create batch.start_seq_q and batch.end_seq_q for decode
-            batch.start_seq_q = torch.arange(
-                0, len(batch), device=self.device, dtype=torch.int32
-            )
-            batch.end_seq_q = batch.start_seq_q + 1
             next_position_ids = batch.position_ids.new_empty(len(batch))
-            # We do not need start_seq_prefill and end_seq_prefill anymore
-            batch.start_seq_prefill = None
-            batch.end_seq_prefill = None
+            batch.slot_indices = batch.slot_indices[batch.cu_seqlen_prefill[1:] - 1]
+            # We do not need cu_seqlen_prefill anymore
+            batch.cu_seqlen_prefill = None
         else:
             prefill_logprobs = None
             next_position_ids = batch.position_ids
@@ -731,8 +867,8 @@ class FlashCausalLM(Model):
         # Set values in batch
         batch.input_ids = next_input_ids
         batch.position_ids = next_position_ids + 1
-        batch.past_present_indices = batch.end_seq
-        batch.end_seq = batch.end_seq + 1
+        batch.input_lengths_tensor += 1
+        batch.slot_indices += 1
 
         if prefill and prefill_logprobs:
             # Get prefill logprobs
@@ -755,7 +891,6 @@ class FlashCausalLM(Model):
             batch.read_offsets,
             batch.stopping_criterias,
             batch.all_input_ids,
-            batch.all_input_ids_tensor,
             batch.next_token_chooser.do_sample,
             batch.next_token_chooser.seeds,
             next_token_ids,
@@ -770,7 +905,6 @@ class FlashCausalLM(Model):
             read_offset,
             stopping_criteria,
             all_input_ids,
-            all_input_ids_tensor,
             do_sample,
             seed,
             next_token_id,
@@ -845,19 +979,20 @@ class FlashCausalLM(Model):
 
                 generations.append(generation)
 
-            new_input_length = input_length + 1
-
             # Update values
-            batch.input_lengths[i] = new_input_length
+            batch.input_lengths[i] = input_length + 1
             batch.prefix_offsets[i] = prefix_offset
             batch.read_offsets[i] = read_offset
             batch.all_input_ids[i] = all_input_ids
 
+        if stopped:
+            del batch
+            # No need to return a batch if we know that all requests stopped
+            return generations, None
+
         batch.prefill_cu_outlens = None
         batch.prefill_head_indices = None
         batch.prefill_next_token_indices = None
         batch.max_seqlen = batch.max_seqlen + 1
-        batch.past_key_values = present
 
-        # No need to return a batch if we know that all requests stopped
-        return generations, batch if not stopped else None
+        return generations, batch

server/text_generation_server/models/flash_llama.py

@@ -25,12 +25,13 @@ class FlashLlama(FlashCausalLM):
         model_id: str,
         revision: Optional[str] = None,
         quantize: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
-            dtype = torch.float16
+            dtype = torch.float16 if dtype is None else dtype
         else:
             raise NotImplementedError("FlashLlama is only available on GPU")
 
@@ -64,10 +65,12 @@ class FlashLlama(FlashCausalLM):
         model = FlashLlamaForCausalLM(config, weights)
 
         torch.distributed.barrier(group=self.process_group)
-        super(FlashCausalLM, self).__init__(
+        super(FlashLlama, self).__init__(
             model=model,
             tokenizer=tokenizer,
-            requires_padding=False,
+            num_layers=len(model.model.layers),
+            num_kv_heads=model.model.num_heads,
+            head_size=model.model.head_size,
             dtype=dtype,
             device=device,
             rank=rank,

server/text_generation_server/models/flash_neox.py

@@ -24,12 +24,13 @@ class FlashNeoXSharded(FlashCausalLM):
         model_id: str,
         revision: Optional[str] = None,
         quantize: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
-            dtype = torch.float16
+            dtype = torch.float16 if dtype is None else dtype
         else:
             raise NotImplementedError("FlashNeoX is only available on GPU")
 
@@ -55,10 +56,12 @@ class FlashNeoXSharded(FlashCausalLM):
         model = FlashGPTNeoXForCausalLM(config, weights)
 
         torch.distributed.barrier(group=self.process_group)
-        super(FlashCausalLM, self).__init__(
+        super(FlashNeoXSharded, self).__init__(
             model=model.to(device),
             tokenizer=tokenizer,
-            requires_padding=False,
+            num_layers=len(model.gpt_neox.layers),
+            num_kv_heads=model.gpt_neox.num_heads,
+            head_size=model.gpt_neox.head_size,
             dtype=dtype,
             device=device,
             rank=rank,

server/text_generation_server/models/flash_rw.py

@@ -25,12 +25,13 @@ class FlashRWSharded(FlashCausalLM):
         model_id: str,
         revision: Optional[str] = None,
         quantize: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
-            dtype = torch.float16
+            dtype = torch.float16 if dtype is None else dtype
         else:
             raise NotImplementedError("FlashRW is only available on GPU")
 
@@ -55,10 +56,12 @@ class FlashRWSharded(FlashCausalLM):
         model = FlashRWForCausalLM(config, weights)
 
         torch.distributed.barrier(group=self.process_group)
-        super(FlashCausalLM, self).__init__(
+        super(FlashRWSharded, self).__init__(
             model=model.to(device),
             tokenizer=tokenizer,
-            requires_padding=False,
+            num_layers=len(model.transformer.h),
+            num_kv_heads=model.transformer.cache_size,
+            head_size=model.transformer.head_size,
             dtype=dtype,
             device=device,
             rank=rank,

server/text_generation_server/models/flash_santacoder.py

@@ -24,12 +24,13 @@ class FlashSantacoderSharded(FlashCausalLM):
         model_id: str,
         revision: Optional[str] = None,
         quantize: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
-            dtype = torch.float16
+            dtype = torch.float16 if dtype is None else dtype
         else:
             raise NotImplementedError("FlashSantacoderSharded is only available on GPU")
 
@@ -52,18 +53,22 @@ class FlashSantacoderSharded(FlashCausalLM):
         torch.distributed.barrier(group=self.process_group)
         filenames = weight_files(model_id, revision=revision, extension=".safetensors")
         weights = Weights(
-            filenames, device=device, dtype=dtype, process_group=self.process_group,
-            aliases = {"transformer.wte.weight": ["lm_head.weight"]}
+            filenames,
+            device=device,
+            dtype=dtype,
+            process_group=self.process_group,
+            aliases={"transformer.wte.weight": ["lm_head.weight"]},
         )
 
         model = FlashSantacoderForCausalLM(config, weights)
 
         torch.distributed.barrier(group=self.process_group)
-
-        super(FlashCausalLM, self).__init__(
+        super(FlashSantacoderSharded, self).__init__(
             model=model.to(device),
             tokenizer=tokenizer,
-            requires_padding=False,
+            num_layers=len(model.transformer.h),
+            num_kv_heads=1,
+            head_size=model.transformer.head_size,
             dtype=dtype,
             device=device,
             rank=rank,

server/text_generation_server/models/galactica.py

@@ -158,12 +158,13 @@ class GalacticaSharded(CausalLM):
         model_id: str,
         revision: Optional[str] = None,
         quantize: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
-            dtype = torch.float16
+            dtype = torch.float16 if dtype is None else dtype
         else:
             device = torch.device("cpu")
             dtype = torch.float32

server/text_generation_server/models/gpt_neox.py

@@ -24,12 +24,13 @@ class GPTNeoxSharded(CausalLM):
         model_id: str,
         revision: Optional[str] = None,
         quantize: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
-            dtype = torch.float16
+            dtype = torch.float16 if dtype is None else dtype
         else:
             device = torch.device("cpu")
             dtype = torch.float32

server/text_generation_server/models/model.py

@@ -22,6 +22,9 @@ class Model(ABC):
         rank: int = 0,
         world_size: int = 1,
     ):
+        if torch.cuda.is_available():
+            torch.cuda.set_per_process_memory_fraction(1.0)
+
         self.model = model.eval()
         self.tokenizer = tokenizer
         self.all_special_ids = set(tokenizer.all_special_ids)
@@ -55,6 +58,9 @@ class Model(ABC):
     def generate_token(self, batch: B) -> Tuple[List[GeneratedText], Optional[B]]:
         raise NotImplementedError
 
+    def warmup(self, batch: B, max_total_tokens: int):
+        self.generate_token(batch)
+
     def decode_token(
         self,
         all_input_ids: List[int],

server/text_generation_server/models/mpt.py

@@ -0,0 +1,98 @@
+import torch
+import torch.distributed
+
+from pathlib import Path
+from typing import Optional, Type
+from opentelemetry import trace
+from transformers import AutoTokenizer, PretrainedConfig, PreTrainedTokenizerBase
+from huggingface_hub import hf_hub_download
+import json
+
+from text_generation_server.models import CausalLM
+from text_generation_server.models.causal_lm import CausalLMBatch
+from text_generation_server.pb import generate_pb2
+from text_generation_server.models.custom_modeling.mpt_modeling import (
+    MPTForCausalLM,
+)
+from text_generation_server.utils import (
+    initialize_torch_distributed,
+    weight_files,
+    Weights,
+)
+
+tracer = trace.get_tracer(__name__)
+
+
+class MPTCausalLMBatch(CausalLMBatch):
+    @classmethod
+    def from_pb(
+        cls,
+        pb: generate_pb2.Batch,
+        tokenizer: PreTrainedTokenizerBase,
+        dtype: torch.dtype,
+        device: torch.device,
+    ) -> "CausalLMBatch":
+        batch = super().from_pb(pb=pb, tokenizer=tokenizer, dtype=dtype, device=device)
+        batch.keys_head_dim_last = False
+        return batch
+
+
+class MPTSharded(CausalLM):
+    def __init__(
+        self,
+        model_id: str,
+        revision: Optional[str] = None,
+        quantize: Optional[str] = None,
+        trust_remote_code: bool = False,
+    ):
+        self.process_group, rank, world_size = initialize_torch_distributed()
+        if torch.cuda.is_available():
+            device = torch.device(f"cuda:{rank}")
+            dtype = torch.float16
+        else:
+            raise NotImplementedError("MPTSharded is only available on GPU")
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_id,
+            revision=revision,
+            padding_side="left",
+            truncation_side="left",
+            trust_remote_code=trust_remote_code,
+        )
+        tokenizer.pad_token = tokenizer.eos_token
+
+        # If model_id is a local path, load the file directly
+        local_path = Path(model_id, "config.json")
+        if local_path.exists():
+            filename = str(local_path.resolve())
+        else:
+            filename = hf_hub_download(
+                model_id, revision=revision, filename="config.json"
+            )
+        with open(filename, "r") as f:
+            config = json.load(f)
+        config = PretrainedConfig(**config)
+        config.quantize = quantize
+
+        torch.distributed.barrier(group=self.process_group)
+
+        filenames = weight_files(model_id, revision=revision, extension=".safetensors")
+        weights = Weights(filenames, device, dtype, process_group=self.process_group)
+
+        config.quantize = quantize
+        model = MPTForCausalLM(config, weights)
+
+        torch.distributed.barrier(group=self.process_group)
+        super(CausalLM, self).__init__(
+            model=model,
+            tokenizer=tokenizer,
+            requires_padding=False,
+            dtype=dtype,
+            device=device,
+            rank=rank,
+            world_size=world_size,
+        )
+
+    @property
+    def batch_type(self) -> Type[CausalLMBatch]:
+        return MPTCausalLMBatch

server/text_generation_server/models/opt.py

@@ -22,12 +22,13 @@ class OPTSharded(CausalLM):
         model_id: str,
         revision: Optional[str] = None,
         quantize: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
-            dtype = torch.float16
+            dtype = torch.float16 if dtype is None else dtype
         else:
             device = torch.device("cpu")
             dtype = torch.float32

server/text_generation_server/models/rw.py

@@ -12,11 +12,12 @@ class RW(CausalLM):
         model_id: str,
         revision: Optional[str] = None,
         quantize: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
         if torch.cuda.is_available():
             device = torch.device("cuda")
-            dtype = torch.float16
+            dtype = torch.float16 if dtype is None else dtype
         else:
             if quantize:
                 raise ValueError("quantization is not available on CPU")

server/text_generation_server/models/santacoder.py

@@ -19,11 +19,12 @@ class SantaCoder(CausalLM):
         model_id: str,
         revision: Optional[str] = None,
         quantize: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
         if torch.cuda.is_available():
             device = torch.device("cuda")
-            dtype = torch.float16
+            dtype = torch.float16 if dtype is None else dtype
         else:
             if quantize:
                 raise ValueError("quantization is not available on CPU")

server/text_generation_server/models/seq2seq_lm.py

@@ -127,7 +127,7 @@ class Seq2SeqLMBatch(Batch):
             read_offsets.append(1)
         all_decoder_input_ids = decoder_input_ids.view(-1).split(1)
 
-        max_tokens = len(inputs) * max_input_length + max_decode_tokens
+        max_tokens = len(inputs) * (max_input_length + max_decode_tokens)
 
         return cls(
             batch_id=pb.id,
@@ -504,11 +504,12 @@ class Seq2SeqLM(Model):
         model_id: str,
         revision: Optional[str] = None,
         quantize: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
         if torch.cuda.is_available():
             device = torch.device("cuda")
-            dtype = torch.float16
+            dtype = torch.float16 if dtype is None else dtype
         else:
             if quantize:
                 raise ValueError("quantization is not available on CPU")

server/text_generation_server/models/t5.py

@@ -25,12 +25,13 @@ class T5Sharded(Seq2SeqLM):
         model_id: str,
         revision: Optional[str] = None,
         quantize: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")
-            dtype = torch.float16
+            dtype = torch.float16 if dtype is None else dtype
         else:
             device = torch.device("cpu")
             dtype = torch.float32

server/text_generation_server/server.py

@@ -53,6 +53,13 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
 
         return generate_pb2.FilterBatchResponse(batch=filtered_batch.to_pb())
 
+    async def Warmup(self, request, context):
+        batch = self.model.batch_type.from_pb(
+            request.batch, self.model.tokenizer, self.model.dtype, self.model.device
+        )
+        self.model.warmup(batch, request.max_total_tokens)
+        return generate_pb2.WarmupResponse()
+
     async def Prefill(self, request, context):
         batch = self.model.batch_type.from_pb(
             request.batch, self.model.tokenizer, self.model.dtype, self.model.device
@@ -99,6 +106,7 @@ def serve(
     revision: Optional[str],
     sharded: bool,
     quantize: Optional[str],
+    dtype: Optional[str],
     trust_remote_code: bool,
     uds_path: Path,
 ):
@@ -107,6 +115,7 @@ def serve(
         revision: Optional[str],
         sharded: bool = False,
         quantize: Optional[str] = None,
+        dtype: Optional[str] = None,
         trust_remote_code: bool = False,
     ):
         unix_socket_template = "unix://{}-{}"
@@ -121,7 +130,9 @@ def serve(
             server_urls = [local_url]
 
         try:
-            model = get_model(model_id, revision, sharded, quantize, trust_remote_code)
+            model = get_model(
+                model_id, revision, sharded, quantize, dtype, trust_remote_code
+            )
         except Exception:
             logger.exception("Error when initializing model")
             raise
@@ -152,4 +163,6 @@ def serve(
             logger.info("Signal received. Shutting down")
             await server.stop(0)
 
-    asyncio.run(serve_inner(model_id, revision, sharded, quantize, trust_remote_code))
+    asyncio.run(
+        serve_inner(model_id, revision, sharded, quantize, dtype, trust_remote_code)
+    )

server/text_generation_server/utils/layers.py

@@ -32,7 +32,19 @@ def load_layer_norm(cls, prefix, weights, eps):
     return ln
 
 
+@classmethod
+def load_layer_norm_no_bias(cls, prefix, weights, eps):
+    weight = weights.get_tensor(f"{prefix}.weight")
+    with init_empty_weights():
+        ln = cls(weight.shape, eps=eps)
+
+    ln.weight = nn.Parameter(weight)
+    ln.bias = None
+    return ln
+
+
 torch.nn.LayerNorm.load = load_layer_norm
+torch.nn.LayerNorm.load_no_bias = load_layer_norm_no_bias
 
 
 class FastLinear(nn.Module):
@@ -357,7 +369,7 @@ try:
         def __init__(self, inv_freq):
             super().__init__()
 
-            self.register_buffer("inv_freq", inv_freq)
+            self.inv_freq = inv_freq
             self._seq_len_cached = 0
             self._cos_cached = None
             self._sin_cached = None

server/text_generation_server/utils/logits_process.py

@@ -189,9 +189,8 @@ class HeterogeneousTopPLogitsWarper(LogitsWarper):
 
         # Remove tokens with cumulative top_p above the threshold (token with 0 are kept)
         sorted_indices_to_remove = probs <= self.top_p_opposite
-        if self.min_tokens_to_keep > 1:
-            # Keep at least min_tokens_to_keep
-            sorted_indices_to_remove[..., -self.min_tokens_to_keep :] = 0
+        # Keep at least min_tokens_to_keep
+        sorted_indices_to_remove[..., -self.min_tokens_to_keep :] = 0
 
         # scatter sorted tensors to original indexing
         indices_to_remove = sorted_indices_to_remove.scatter(

server/text_generation_server/utils/tokens.py

@@ -216,6 +216,8 @@ class HeterogeneousNextTokenChooser:
 
         self.seeds = seeds
         self.do_sample = do_sample
+        self.dtype = dtype
+        self.device = device
 
     def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor):
         if self.watermark_processor is not None:

server/text_generation_server/utils/weights.py

@@ -3,8 +3,16 @@ from typing import List, Dict, Optional
 from safetensors import safe_open
 import torch
 
+
 class Weights:
-    def __init__(self, filenames: List[Path], device, dtype, process_group, aliases: Optional[Dict[str, List[str]]]=None):
+    def __init__(
+        self,
+        filenames: List[Path],
+        device,
+        dtype,
+        process_group,
+        aliases: Optional[Dict[str, List[str]]] = None,
+    ):
         routing = {}
         for filename in filenames:
             with safe_open(filename, framework="pytorch") as f: