Bump version to 0.5.3.post1 (#6696)

README.md

@@ -26,6 +26,7 @@ Register now [here](https://lu.ma/lp0gyjqr) and be part of the event!
 ---
 
 *Latest News* 🔥
+- [2024/07] In partnership with Meta, vLLM officially supports Llama 3.1 with FP8 quantization and pipeline parallelism! Please check out our blog post [here](https://blog.vllm.ai/2024/07/23/llama31.html).
 - [2024/06] We hosted [the fourth vLLM meetup](https://lu.ma/agivllm) with Cloudflare and BentoML! Please find the meetup slides [here](https://docs.google.com/presentation/d/1iJ8o7V2bQEi0BFEljLTwc5G1S10_Rhv3beed5oB0NJ4/edit?usp=sharing).
 - [2024/04] We hosted [the third vLLM meetup](https://robloxandvllmmeetup2024.splashthat.com/) with Roblox! Please find the meetup slides [here](https://docs.google.com/presentation/d/1A--47JAK4BJ39t954HyTkvtfwn0fkqtsL8NGFuslReM/edit?usp=sharing).
 - [2024/01] We hosted [the second vLLM meetup](https://lu.ma/ygxbpzhl) with IBM! Please find the meetup slides [here](https://docs.google.com/presentation/d/12mI2sKABnUw5RBWXDYY-HtHth4iMSNcEoQ10jDQbxgA/edit?usp=sharing).

docs/source/models/supported_models.rst

@@ -94,8 +94,8 @@ Decoder-only Language Models
     - :code:`ai21labs/Jamba-v0.1`, etc.
     - ✅︎
   * - :code:`LlamaForCausalLM`
-    - LLaMA, Llama 2, Meta Llama 3, Vicuna, Alpaca, Yi
-    - :code:`meta-llama/Meta-Llama-3-8B-Instruct`, :code:`meta-llama/Meta-Llama-3-70B-Instruct`, :code:`meta-llama/Llama-2-13b-hf`, :code:`meta-llama/Llama-2-70b-hf`, :code:`openlm-research/open_llama_13b`, :code:`lmsys/vicuna-13b-v1.3`, :code:`01-ai/Yi-6B`, :code:`01-ai/Yi-34B`, etc.
+    - Llama 3.1, Llama 3, Llama 2, LLaMA, Yi
+    - :code:`meta-llama/Meta-Llama-3.1-405B-Instruct`, :code:`meta-llama/Meta-Llama-3.1-70B`, :code:`meta-llama/Meta-Llama-3-70B-Instruct`, :code:`meta-llama/Llama-2-70b-hf`, :code:`01-ai/Yi-34B`, etc.
     - ✅︎
   * - :code:`MiniCPMForCausalLM`
     - MiniCPM

docs/source/serving/distributed_serving.rst

@@ -66,8 +66,8 @@ Pick a node as the head node, and run the following command:
     $ bash run_cluster.sh \
     $                   vllm/vllm-openai \
     $                   ip_of_head_node \
-    $                   /path/to/the/huggingface/home/in/this/node \
-    $                   --head
+    $                   --head \
+    $                   /path/to/the/huggingface/home/in/this/node
 
 On the rest of the worker nodes, run the following command:
 
@@ -76,8 +76,8 @@ On the rest of the worker nodes, run the following command:
     $ bash run_cluster.sh \
     $                   vllm/vllm-openai \
     $                   ip_of_head_node \
-    $                   /path/to/the/huggingface/home/in/this/node \
-    $                   --worker
+    $                   --worker \
+    $                   /path/to/the/huggingface/home/in/this/node
 
 Then you get a ray cluster of containers. Note that you need to keep the shells running these commands alive to hold the cluster. Any shell disconnect will terminate the cluster.
 

vllm/config.py

@@ -154,15 +154,6 @@ class ModelConfig:
         self.hf_text_config = get_hf_text_config(self.hf_config)
         self.dtype = _get_and_verify_dtype(self.hf_text_config, dtype)
 
-        if (getattr(self.hf_config, "max_position_embeddings", 0) == 131072
-                and getattr(self.hf_config, "rope_scaling", None) is None):
-            # Note(simon): this is a special case for a model that doesn't
-            # supply rope_scaling. We should remove this once the model is
-            # updated.
-            self.hf_config.update({"rope_scaling": {
-                "type": "extended",
-            }})
-
         if (not self.disable_sliding_window
                 and self.hf_text_config.model_type == "gemma2"
                 and self.hf_text_config.sliding_window is not None):
@@ -814,7 +805,7 @@ class SchedulerConfig:
         if enable_chunked_prefill:
             logger.info(
                 "Chunked prefill is enabled with max_num_batched_tokens=%d.",
-                max_num_batched_tokens)
+                self.max_num_batched_tokens)
 
         self.max_num_seqs = max_num_seqs
         self.max_model_len = max_model_len
@@ -1492,24 +1483,32 @@ def _get_and_verify_max_len(
         derived_max_model_len = default_max_len
 
     rope_scaling = getattr(hf_config, "rope_scaling", None)
-    # The correct one should be "longrope", kept "su" here
-    # to be backward compatible
-    if rope_scaling is not None and rope_scaling["type"] not in {
-            "su", "longrope", "extended"
-    }:
-        if disable_sliding_window:
-            # TODO(robertgshaw): Find a model that supports rope_scaling
-            # with sliding window to see if this case should be allowed.
-            raise NotImplementedError(
-                "Disabling sliding window is not supported for models "
-                "with rope_scaling. Please raise an issue so we can "
-                "investigate.")
-        assert "factor" in rope_scaling
-        scaling_factor = rope_scaling["factor"]
-        if rope_scaling["type"] == "yarn":
-            derived_max_model_len = rope_scaling[
-                "original_max_position_embeddings"]
-        derived_max_model_len *= scaling_factor
+    if rope_scaling is not None:
+        if "type" in rope_scaling:
+            rope_type = rope_scaling["type"]
+        elif "rope_type" in rope_scaling:
+            rope_type = rope_scaling["rope_type"]
+        else:
+            raise ValueError(
+                "rope_scaling must have a 'type' or 'rope_type' key.")
+
+        # The correct one should be "longrope", kept "su" here
+        # to be backward compatible
+        if rope_type not in ("su", "longrope", "llama3"):
+            if disable_sliding_window:
+                # TODO(robertgshaw): Find a model that supports rope_scaling
+                # with sliding window to see if this case should be allowed.
+                raise NotImplementedError(
+                    "Disabling sliding window is not supported for models "
+                    "with rope_scaling. Please raise an issue so we can "
+                    "investigate.")
+
+            assert "factor" in rope_scaling
+            scaling_factor = rope_scaling["factor"]
+            if rope_type == "yarn":
+                derived_max_model_len = rope_scaling[
+                    "original_max_position_embeddings"]
+            derived_max_model_len *= scaling_factor
 
     # If the user specified a max length, make sure it is smaller than the
     # derived length from the HF model config.

vllm/model_executor/layers/rotary_embedding.py

@@ -794,12 +794,13 @@ def get_rope(
         rotary_emb = RotaryEmbedding(head_size, rotary_dim, max_position, base,
                                      is_neox_style, dtype)
     else:
-        scaling_type = rope_scaling["type"]
+        scaling_type = rope_scaling[
+            "type"] if "type" in rope_scaling else rope_scaling["rope_type"]
         # The correct one should be "longrope" but keep "su" here
         # for backward compatible
-        if scaling_type not in {"su", "longrope", "extended"}:
+        if scaling_type not in {"su", "longrope", "llama3"}:
             scaling_factor = rope_scaling["factor"]
-        if scaling_type == "extended":
+        if scaling_type == "llama3":
             rotary_emb = ExtendedRotaryEmbedding(head_size, rotary_dim,
                                                  max_position, base,
                                                  is_neox_style, dtype)

vllm/version.py

@@ -9,4 +9,4 @@ except Exception as e:
                   stacklevel=2)
     __commit__ = "COMMIT_HASH_PLACEHOLDER"
 
-__version__ = "0.5.3"
+__version__ = "0.5.3.post1"