Add gemma support (#69)

add gemma 1

shard config for llamA

gemma 3

gemma4

formatter

Author: qihqi

default_shardings/gemma.yaml

@@ -0,0 +1,20 @@
+
+# Sharding config for gemma
+# "replicated" to signify "replicated".
+# Integer signify axis to shard: 0 <= shard axis < rank
+
+freqs_cis : null #  torch.complex64 (16384, 128)
+layers.*.self_attn.wo.weight : 1 # 1, -1] #  torch.float32 (2048, 2048)
+layers.*.self_attn.wq.weight : 0 # -1, 1] #  torch.float32 (2048, 2048)
+layers.*.self_attn.wk.weight : 0 # -1, 1] #  torch.float32 (256, 2048)
+layers.*.self_attn.wv.weight : 0 # -1, 1] #  torch.float32 (256, 2048)
+layers.*.mlp.gate_proj.weight : 0 # -1, 1] #  torch.float32 (16384, 2048)
+layers.*.mlp.gate_proj.bias : 0 # -1] #  torch.float32 (16384,)
+layers.*.mlp.up_proj.weight : 0 # -1, 1] #  torch.float32 (16384, 2048)
+layers.*.mlp.up_proj.bias : 0 # -1] #  torch.float32 (16384,)
+layers.*.mlp.down_proj.weight : 1 # 1, -1] #  torch.float32 (2048, 16384)
+layers.*.mlp.down_proj.bias : null #  torch.float32 (2048,)
+layers.*.input_layernorm.weight : null #  torch.float32 (2048,)
+layers.*.post_attention_layernorm.weight : null #  torch.float32 (2048,)
+norm.weight : null #  torch.float32 (2048,)
+embedder.weight : 1 # # 1, -1] #  torch.float32 (256000, 2048)

default_shardings/llama-2.yaml

@@ -0,0 +1,23 @@
+
+# Sharding config for llama-2
+# Sharding should either be an int between 0 and rank - 1
+# signifying the axis to shard or -1 / null signifying replicated
+
+
+freqs_cis : -1 #  torch.complex64 (2048, 64)
+tok_embeddings.weight : 1 #  torch.float32 (32000, 4096)
+layers.*.attention.wo.weight : 1 #  torch.int8 (4096, 4096)
+layers.*.attention.wo.weight_scaler : 0 #  torch.bfloat16 (4096,)
+layers.*.attention.wq.weight : 0 #  torch.int8 (4096, 4096)
+layers.*.attention.wq.weight_scaler : 0 #  torch.bfloat16 (4096,)
+layers.*.attention.wk.weight : 0 #  torch.int8 (4096, 4096)
+layers.*.attention.wk.weight_scaler : 0 #  torch.bfloat16 (4096,)
+layers.*.attention.wv.weight : 0 #  torch.int8 (4096, 4096)
+layers.*.attention.wv.weight_scaler : 0 #  torch.bfloat16 (4096,)
+layers.*.feed_forward.w1.weight : 0 #  torch.float32 (11008, 4096)
+layers.*.feed_forward.w2.weight : 1 #  torch.float32 (4096, 11008)
+layers.*.feed_forward.w3.weight : 0 #  torch.float32 (11008, 4096)
+layers.*.attention_norm.weight : -1 #  torch.float32 (4096,)
+layers.*.ffn_norm.weight : -1 #  torch.float32 (4096,)
+norm.weight : -1 #  torch.float32 (4096,)
+output.weight : 0 #  torch.float32 (32000, 4096)

jetstream_pt/__init__.py

@@ -13,3 +13,5 @@
 # limitations under the License.
 
 from jetstream_pt.engine import create_pytorch_engine
+
+__all__ = ["create_pytorch_engine"]

jetstream_pt/engine.py

@@ -34,6 +34,7 @@
 from jetstream_pt import quantize
 from jetstream_pt.environment import JetEngineEnvironment, JetEngineEnvironmentData
 from jetstream_pt.third_party.llama import model_exportable, model_args
+from jetstream_pt.third_party.gemma import config as gemma_config, model as gemma_model
 
 
 Mesh = jax.sharding.Mesh
@@ -108,32 +109,6 @@ def __init__(
     #      out_shardings=self.get_decode_state_sharding())
     self._lock = threading.RLock()
 
-  # pylint: disable-next=all
-  def sharding_by_name(self, name):
-
-    # This allows easier way to edit shardings
-    """
-    for key, val in self.env._data.experimental_sharding_axis_override.items():
-      if name.endswith(key):
-        return self.env.sharding_by_axis(val)
-    """
-
-    if "weight_scaler" in name:
-      return self.x_sharding
-    if "tok_embeddings." in name:
-      return self.y_sharding
-    if "attention." in name:
-      if "wo" in name:
-        return self.y_sharding
-      return self.x_sharding
-    if "feed_forward." in name:
-      if "w2" in name:
-        return self.y_sharding
-      return self.x_sharding
-    if "output" in name:
-      return self.x_sharding
-    return self.replicated
-
   # pylint: disable-next=all
   def init_decode_state(
       self,
@@ -561,7 +536,7 @@ def _load_from_safetensors(self, path):
       for key, model_weights in self.pt_model.state_dict().items():
         if key == "freqs_cis":
           continue
-        arr = jax.device_put(f.get_tensor(key), self.sharding_by_name(key))
+        arr = jax.device_put(f.get_tensor(key), self.env.sharding_by_name(key))
         assert tuple(model_weights.shape) == tuple(
             arr.shape
         ), f"key: {key} error: {model_weights.shape} != {arr.shape}"
@@ -587,7 +562,7 @@ def load_params(self) -> Params:
       else:
         jax_weights = self._make_state_dict_jax(self.pt_model.state_dict())
     jax_weights = {
-        key: jax.device_put(value, self.sharding_by_name(key))
+        key: jax.device_put(value, self.env.sharding_by_name(key))
         for key, value in jax_weights.items()
     }
     for k, v in jax_weights.items():
@@ -664,6 +639,7 @@ def create_pytorch_engine(
     quantize_weights=False,
     quantize_kv=False,
     max_cache_length=1024,
+    sharding_config=None,
 ) -> PyTorchEngine:
   """Returns: The pytorch engine."""
 
@@ -706,42 +682,58 @@ def create_pytorch_engine(
   tokenizer = token_utils.load_vocab(tokenizer_path)
   pt_model = None
 
+  env_data = JetEngineEnvironmentData(
+      tokenizer_path=tokenizer_path,
+      checkpoint_path=checkpoint_path,
+      checkpoint_format=checkpoint_format,
+      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,
+  )
+
   if model_name.startswith("llama"):
 
     args = model_args.get_model_args(
         model_name + "-" + param_size, context_length, batch_size, bf16_enable
     )
     args.device = "meta"
     args.quantize = quantize_weights
-    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,
-        num_layers=args.n_layers,
-        cache_shape=(
-            batch_size,
-            args.n_kv_heads,
-            max_cache_length,
-            args.dim // args.n_heads,
-        ),
+    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)
-
-    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 == torch.int8 else 2)
-    print("Number of param Gbytes:", num_params_size / (1 << 30))
-    print("Number of param: ", num_params)
+  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 == torch.int8 else 2)
+  print("Number of param Gbytes:", num_params_size / (1 << 30))
+  print("Number of param: ", num_params)
 
   return PyTorchEngine(pt_model=pt_model, env=env)

jetstream_pt/environment.py

@@ -15,6 +15,8 @@
 from typing import Tuple, Dict
 
 import dataclasses
+import yaml
+
 import jax
 import jax.sharding as jsharding
 from jax.experimental import mesh_utils
@@ -71,6 +73,8 @@ class JetEngineEnvironmentData:
   # If Ture, use bfloat16 as dtype. If False, use float32 as dtype
   bf16_enable: bool = True
 
+  sharding_config_path: str = ""
+
 
 # pylint: disable-next=all
 class JetEngineEnvironment:
@@ -100,6 +104,15 @@ def __init__(self, data: JetEngineEnvironmentData):
     self.cache_sharding = jsharding.NamedSharding(
         self._mesh, P(*cache_sharding)
     )
+    self._load_sharding_config()
+
+  def _load_sharding_config(self):
+    """Load sharding config"""
+    if self._data.sharding_config_path:
+      with open(self._data.sharding_config_path, encoding="utf-8") as f:
+        self._sharding_config = yaml.safe_load(f)
+    else:
+      self._sharding_config = {}
 
   def __getattr__(self, name):
     return getattr(self._data, name)
@@ -150,3 +163,35 @@ def make_caches_generate(self):
             )
         )
     return caches
+
+  def sharding_by_name(self, name):
+    """Create sharding specified in the config."""
+    if name in self._sharding_config:
+      return self.sharding_by_axis(self._sharding_config[name])
+
+    name = process_sharding_name(name)
+    if name in self._sharding_config:
+      return self.sharding_by_axis(self._sharding_config[name])
+
+    raise RuntimeError("Sharding for name: ", name, " not specified")
+
+
+def process_sharding_name(name):
+  """Replace integers in param name with *.
+
+  Presumably all layers should have the same sharding.
+  """
+
+  def is_integer(t):
+    try:
+      int(t)
+      return True
+    # pylint: disable-next=all
+    except:  # noqa: E722
+      return False
+
+  tokens = name.split(".")
+  for i, t in enumerate(tokens):
+    if is_integer(t):
+      tokens[i] = "*"
+  return ".".join(tokens)

jetstream_pt/layers.py

@@ -132,57 +132,53 @@ def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
 class Attention(nn.Module):
   """Attention module."""
 
-  def __init__(self, args, env):
+  def __init__(self, n_heads, n_kv_heads, head_dim, hidden_size, device, env):
     super().__init__()
-
-    self.n_kv_heads = (
-        args.n_heads if args.n_kv_heads is None else args.n_kv_heads
-    )
-    self.n_local_heads = args.n_heads
-    self.n_local_kv_heads = self.n_kv_heads
-    self.n_rep = self.n_local_heads // self.n_local_kv_heads
-    self.head_dim = args.dim // args.n_heads
-    self.max_seq_len = args.max_seq_len
-    self.n_heads = args.n_heads
-
+    self.n_heads = n_heads
+    self.n_kv_heads = n_kv_heads
+    self.head_dim = head_dim
+    self.n_rep = self.n_heads // self.n_kv_heads
     self.env = env
+    self.hidden_size = hidden_size
 
-    LinearLayer = WeightOnlyInt8Linear if args.quantize else nn.Linear
+    LinearLayer = (
+        WeightOnlyInt8Linear if env.enable_weight_quantization else nn.Linear
+    )
 
     self.wo = LinearLayer(
-        args.n_heads * self.head_dim,
-        args.dim,
+        n_heads * self.head_dim,
+        hidden_size,
         bias=False,
-        device=args.device,
+        device=device,
     )
-    self.q_size = args.n_heads * self.head_dim
+    self.q_size = n_heads * self.head_dim
     self.kv_size = self.n_kv_heads * self.head_dim
     if self.env.qkv_fusion:
       self._register_load_state_dict_pre_hook(self.load_hook)
       self.wqkv = LinearLayer(
-          args.dim,
-          (args.n_heads + 2 * self.n_kv_heads) * self.head_dim,
+          hidden_size,
+          (n_heads + 2 * self.n_kv_heads) * self.head_dim,
           bias=False,
-          device=args.device,
+          device=device,
       )
     else:
       self.wq = LinearLayer(
-          args.dim,
-          args.n_heads * self.head_dim,
+          hidden_size,
+          n_heads * self.head_dim,
           bias=False,
-          device=args.device,
+          device=device,
       )
       self.wk = LinearLayer(
-          args.dim,
+          hidden_size,
           self.n_kv_heads * self.head_dim,
           bias=False,
-          device=args.device,
+          device=device,
       )
       self.wv = LinearLayer(
-          args.dim,
+          hidden_size,
           self.n_kv_heads * self.head_dim,
           bias=False,
-          device=args.device,
+          device=device,
       )
 
   def load_hook(self, state_dict, prefix, *args):
@@ -210,9 +206,9 @@ def forward(
         )
       else:
         xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
-      xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
-      xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
-      xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
+      xq = xq.view(bsz, seqlen, self.n_heads, self.head_dim)
+      xk = xk.view(bsz, seqlen, self.n_kv_heads, self.head_dim)
+      xv = xv.view(bsz, seqlen, self.n_kv_heads, self.head_dim)
 
       self.env.apply_sharding(xq, axis=2)
       self.env.apply_sharding(xk, axis=2)
@@ -262,7 +258,8 @@ def forward(
         self.env.apply_sharding(output, axis=1)
         output = output.transpose(-3, -2).contiguous().view(bsz, seqlen, -1)
         self.env.apply_sharding(output, axis=2)
-      return self.wo(output)
+      output = self.wo(output)
+      return output
     else:
       with jax.named_scope("attn_insert_cache"):
         keys, values, k_scaler, v_scaler = cache.update(xk, xv)

jetstream_pt/third_party/gemma/__init__.py

@@ -0,0 +1,13 @@
+# Copyright 2024 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.