Neuron up mistral (#18222)

Signed-off-by: Satyajith Chilappagari <satchill@amazon.com>

tests/neuron/2_core/test_mistral.py

@@ -0,0 +1,32 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from vllm import LLM, SamplingParams
+
+
+def test_mistral():
+    llm = LLM(model="mistralai/Mistral-7B-v0.1",
+              tensor_parallel_size=2,
+              max_num_seqs=4,
+              max_model_len=512,
+              use_v2_block_manager=True,
+              override_neuron_config={
+                  "sequence_parallel_enabled": False,
+                  "skip_warmup": True
+              },
+              device="neuron")
+
+    prompts = [
+        "The president of the United States is",
+        "The capital of France is",
+    ]
+    outputs = llm.generate(prompts, SamplingParams(top_k=1))
+
+    expected_outputs = [
+        " the most powerful person in the world. He is the head of state "
+        "and head",
+        " a city of many faces. It is a city of history, culture, art"
+    ]
+
+    for expected_output, output in zip(expected_outputs, outputs):
+        generated_text = output.outputs[0].text
+        assert (expected_output == generated_text)

vllm/model_executor/model_loader/neuronx_distributed.py

@@ -48,6 +48,9 @@ TORCH_DTYPE_TO_NEURON_AMP = {
 # Models supported by Neuronx distributed for inference.
 _NEURON_SUPPORTED_MODELS: dict[str, tuple[str, str]] = {
     "LlamaForCausalLM":
+    ("neuronx_distributed_inference.models.llama.modeling_llama",
+     "NeuronLlamaForCausalLM"),
+    "MistralForCausalLM":
     ("neuronx_distributed_inference.models.llama.modeling_llama",
      "NeuronLlamaForCausalLM"),
     "DbrxForCausalLM":

vllm/platforms/neuron.py

@@ -51,8 +51,7 @@ class NeuronPlatform(Platform):
         assert (vllm_config.lora_config
                 is None), "LoRA is not supported for Neuron backend."
 
-        cache_config = vllm_config.cache_config
+        if vllm_config.cache_config and vllm_config.model_config:
-        if cache_config:
             # neuron needs block_size = max_model_len
             vllm_config.cache_config.block_size = \
                 vllm_config.model_config.max_model_len  # type: ignore