Add gemma and update recent changes to multiple host (#74)

add gemma and update recent changes to  multiple host

jetstream_pt/ray_engine.py

@@ -152,8 +152,14 @@ def create_pytorch_ray_engine(
     quantize_weights=False,
     quantize_kv=False,
     max_cache_length=1024,
+    sharding_config=None,
 ) -> PyTorchRayEngine:
 
+  supported_models = ["llama-2", "llama-3", "gemma"]
+  if model_name not in supported_models:
+    raise NotImplementedError(
+        f"Model name should be one of{','.join(supported_models)}"
+    )
   ray.init(ignore_reinit_error=True)
   pod_name = tpu.get_current_pod_name()
   num_hosts = tpu.get_current_pod_worker_count()
@@ -183,6 +189,7 @@ def create_pytorch_ray_engine(
         quantize_weights=quantize_weights,
         quantize_kv=quantize_kv,
         max_cache_length=max_cache_length,
+        sharding_config=sharding_config,
     )
     engine_workers.append(engine_worker)
   engine_master = PyTorchRayEngine(

jetstream_pt/ray_worker.py

@@ -17,6 +17,7 @@
 from typing import Any, List, Optional, Tuple, Union
 import threading
 import functools
+import os
 import humanize
 
 
@@ -39,6 +40,7 @@
 from jetstream_pt import cache_manager
 from jetstream_pt import quantize
 from jetstream_pt.environment import JetEngineEnvironment, JetEngineEnvironmentData
+from jetstream_pt.third_party.gemma import config as gemma_config, model as gemma_model
 
 
 Mesh = jax.sharding.Mesh
@@ -103,6 +105,7 @@ def __init__(
       quantize_weights=False,
       quantize_kv=False,
       max_cache_length=1024,
+      sharding_config=None,
   ):
 
     jax.config.update("jax_default_prng_impl", "unsafe_rbg")
@@ -144,38 +147,61 @@ def __init__(
       checkpoint_format = "safetensors"
       checkpoint_path = paths[0]
 
+    if not sharding_config:
+      sharding_config = os.path.join("default_shardings", model_name + ".yaml")
+
     env_data = JetEngineEnvironmentData(
         tokenizer_path=tokenizer_path,
         checkpoint_path=checkpoint_path,
         checkpoint_format=checkpoint_format,
-        model_type="llama-2-" + param_size,
         batch_size=batch_size,
         max_decode_length=max_decode_length,
         max_input_sequence_length=context_length,
         enable_weight_quantization=quantize_weights,
         enable_kv_quantization=quantize_kv,
         cache_sequence_length=max_cache_length,
         bf16_enable=bf16_enable,
+        sharding_config_path=sharding_config,
     )
     env = JetEngineEnvironment(env_data)
 
-    pt_model = None
-    if "llama" in model_name:
+    if model_name.startswith("llama"):
+
       args = model_args.get_model_args(
-          model_name + "-" + param_size,
-          context_length,
-          batch_size,
-          bf16_enable,
+          model_name + "-" + param_size, context_length, batch_size, bf16_enable
       )
       args.device = "meta"
       args.quantize = quantize_weights
+      env_data.cache_shape = (
+          batch_size,
+          args.n_kv_heads,
+          max_cache_length,
+          args.dim // args.n_heads,
+      )
+      env_data.model_type = "llama-2-" + param_size
+      env_data.num_layers = args.n_layers
+      env = JetEngineEnvironment(env_data)
       pt_model = model_exportable.Transformer(args, env)
+    elif model_name == "gemma":
+      args = gemma_config.get_model_config(param_size)
+      env_data.cache_shape = (
+          batch_size,
+          args.num_key_value_heads,
+          max_cache_length,
+          args.head_dim,
+      )
+      env_data.model_type = "gemma-" + param_size
+      env_data.num_layers = args.num_hidden_layers
+      env = JetEngineEnvironment(env_data)
+      pt_model = gemma_model.GemmaModel(args, env)
+    else:
+      raise RuntimeError(f"Model with name {model_name} not found")
 
-      num_params_size = 0
-      num_params = 0
-      for _, v in pt_model.state_dict().items():
-        num_params += 1
-        num_params_size += np.prod(v.shape) * (1 if v.dtype == jnp.int8 else 2)
+    num_params_size = 0
+    num_params = 0
+    for _, v in pt_model.state_dict().items():
+      num_params += 1
+      num_params_size += np.prod(v.shape) * (1 if v.dtype == jnp.int8 else 2)
     print("Number of param Gbytes:", num_params_size / (1 << 30))
     print("Number of param: ", num_params)
 

run_interactive_multiple_host.py

@@ -65,6 +65,14 @@
     "max_cache_length", 1024, "kv_cache_quantize"
 )
 
+_MODEL_NAME = flags.DEFINE_string(
+    "model_name", None, "model type", required=False
+)
+
+_SHARDING_CONFIG = flags.DEFINE_string(
+    "sharding_config", "", "config file for sharding"
+)
+
 
 def create_engine():
   """create a pytorch engine"""
@@ -73,6 +81,7 @@ def create_engine():
 
   start = time.perf_counter()
   engine = ray_engine.create_pytorch_ray_engine(
+      model_name=_MODEL_NAME.value,
       tokenizer_path=_TOKENIZER_PATH.value,
       ckpt_path=_CKPT_PATH.value,
       bf16_enable=True,
@@ -82,6 +91,7 @@ def create_engine():
       quantize_weights=_QUANTIZE_WEIGHTS.value,
       quantize_kv=_QUANTIZE_KV_CACHE.value,
       max_cache_length=_MAX_CACHE_LENGTH.value,
+      sharding_config=_SHARDING_CONFIG.value,
   )
 
   print("Initialize engine", time.perf_counter() - start)