[Neuron] Adding support for context-lenght, token-gen buckets. (#7885)

Co-authored-by: Harsha Bikki <harbikh@amazon.com>

examples/offline_inference_neuron.py

@@ -1,5 +1,12 @@
+import os
+
 from vllm import LLM, SamplingParams
 
+# creates XLA hlo graphs for all the context length buckets.
+os.environ['NEURON_CONTEXT_LENGTH_BUCKETS'] = "128,512,1024,2048"
+# creates XLA hlo graphs for all the token gen buckets.
+os.environ['NEURON_TOKEN_GEN_BUCKETS'] = "128,512,1024,2048"
+
 # Sample prompts.
 prompts = [
     "Hello, my name is",
@@ -19,8 +26,8 @@ llm = LLM(
     # Currently, this is a known limitation in continuous batching support
     # in transformers-neuronx.
     # TODO(liangfu): Support paged-attention in transformers-neuronx.
-    max_model_len=128,
-    block_size=128,
+    max_model_len=2048,
+    block_size=2048,
     # The device can be automatically detected when AWS Neuron SDK is installed.
     # The device argument can be either unspecified for automated detection,
     # or explicitly assigned.

vllm/model_executor/model_loader/neuron.py

@@ -1,7 +1,7 @@
 """Utilities for selecting and loading neuron models."""
 import importlib
 import os
-from typing import Dict, Optional, Tuple
+from typing import Dict, List, Optional, Tuple
 
 import torch
 import torch.nn as nn
@@ -109,6 +109,17 @@ def _get_model_architecture(config: PretrainedConfig) -> str:
         f"{list(_NEURON_SUPPORTED_MODELS.keys())}")
 
 
+def _get_buckets(env: str, default_value: List[int]) -> List[int]:
+    env_value = os.getenv(env)
+    if env_value is None:
+        return default_value
+    buckets_remove_empty = filter(
+        lambda x: x is not None and len(x.strip()) > 0, env_value.split(","))
+    buckets_int = map(int, buckets_remove_empty)
+    buckets_list = list(buckets_int)
+    return buckets_list
+
+
 def get_neuron_model(model_config: ModelConfig,
                      parallel_config: ParallelConfig,
                      scheduler_config: SchedulerConfig) -> nn.Module:
@@ -123,14 +134,18 @@ def get_neuron_model(model_config: ModelConfig,
     neuron_config = NeuronConfig(
         continuous_batching=continuous_batching_config)
 
+    context_length_estimates = _get_buckets("NEURON_CONTEXT_LENGTH_BUCKETS",
+                                            [scheduler_config.max_model_len])
+    n_positions = _get_buckets("NEURON_TOKEN_GEN_BUCKETS",
+                               [scheduler_config.max_model_len])
+
     # Load the weights from the cached or downloaded files.
-    model.load_weights(
-        model_config.model,
+    model.load_weights(model_config.model,
                        tp_degree=parallel_config.tensor_parallel_size,
                        amp=TORCH_DTYPE_TO_NEURON_AMP[model_config.dtype],
                        neuron_config=neuron_config,
-        context_length_estimate=[scheduler_config.max_model_len],
-        n_positions=[scheduler_config.max_model_len],
+                       context_length_estimate=context_length_estimates,
+                       n_positions=n_positions,
                        batch_size=scheduler_config.max_num_seqs)
 
     return model.eval()