Add support for GPT-NeoX (Pythia)

cacheflow/models/attention.py

@@ -150,20 +150,20 @@ def __init__(self, scale: float) -> None:
         super().__init__(scale)
 
 
-class LlamaCacheFlowAttention(GPTCacheFlowAttention):
-    """Llama uses GPT-NeoX style rotary embedding."""
+class GPTNeoXCacheFlowAttention(GPTCacheFlowAttention):
+    """Attention with GPT-NeoX style rotary embedding."""
 
     def __init__(
         self,
         scale: float,
-        head_size: int,
+        rotary_dim: int,
         max_position: int = 8192,
         base: int = 10000,
     ) -> None:
         super().__init__(scale)
 
         # Create the cos and sin cache.
-        inv_freq = 1.0 / (base ** (torch.arange(0, head_size, 2) / head_size))
+        inv_freq = 1.0 / (base ** (torch.arange(0, rotary_dim, 2) / rotary_dim))
         t = torch.arange(max_position).float()
         freqs = torch.einsum('i,j -> ij', t, inv_freq.float())
         cos = freqs.cos()
@@ -174,7 +174,7 @@ def __init__(
         # initializing the model. Make it more robust.
         torch_dtype = torch.get_default_dtype()
         cache = cache.to(torch_dtype)
-        # Embedding size: [max_position, head_size]
+        # Embedding size: [max_position, rotary_dim]
         self.register_buffer('cos_sin_cache', cache, persistent=False)
 
     def forward(
@@ -190,10 +190,12 @@ def forward(
     ) -> torch.Tensor:                          # [num_tokens, num_heads * head_size]
         # Apply rotary embedding to the query and key before passing them
         # to the attention op.
+        head_size = value_cache.shape[2]
         pos_encoding_ops.rotary_embedding_neox(
             positions,
             query,
             key,
+            head_size,
             self.cos_sin_cache,
         )
         return super().forward(
@@ -205,3 +207,7 @@ def forward(
             input_metadata,
             cache_event,
         )
+
+
+class LlamaCacheFlowAttention(GPTNeoXCacheFlowAttention):
+    """LLaMA uses the GPT-NeoX style rotary embedding."""

cacheflow/models/gpt_neox.py

@@ -0,0 +1,278 @@
+"""1D GPT-NeoX model compatible with HuggingFace weights."""
+import os
+import glob
+import filelock
+from tqdm import tqdm
+from typing import Dict, List, Optional, Tuple
+
+import numpy as np
+import torch
+from torch import nn
+from huggingface_hub import snapshot_download
+
+from cacheflow.models import InputMetadata
+from cacheflow.models.attention import GPTNeoXCacheFlowAttention
+from cacheflow.models.sample import Sampler
+from cacheflow.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from cacheflow.parallel_utils.tensor_parallel import (VocabParallelEmbedding,
+                                                      ColumnParallelLinear,
+                                                      RowParallelLinear)
+from cacheflow.sequence import SequenceOutputs
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class GPTNeoXAttention(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.total_num_heads = config.num_attention_heads
+        self.hidden_size = config.hidden_size
+        self.head_size = self.hidden_size // self.total_num_heads
+
+        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
+        assert self.total_num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = self.total_num_heads // tensor_model_parallel_world_size
+
+        self.query_key_value = ColumnParallelLinear(config.hidden_size,
+                                                    3 * config.hidden_size,
+                                                    gather_output=False,
+                                                    perform_initialization=False)
+        self.dense = RowParallelLinear(config.hidden_size, config.hidden_size,
+                                       input_is_parallel=True,
+                                       perform_initialization=False)
+
+        scaling = self.head_size ** -0.5
+        rotary_dim = int(self.head_size * config.rotary_pct)
+        assert rotary_dim % 2 == 0
+        self.attn = GPTNeoXCacheFlowAttention(scaling, rotary_dim)
+
+    def forward(
+        self,
+        position_ids: torch.LongTensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        qkv, _ = self.query_key_value(hidden_states)
+
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(
+            position_ids, q, k, v, k_cache, v_cache, input_metadata, cache_event)
+        output, _ = self.dense(attn_output)
+        return output
+
+
+class GPTNeoXMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense_h_to_4h = ColumnParallelLinear(config.hidden_size,
+                                                  config.intermediate_size,
+                                                  gather_output=False,
+                                                  perform_initialization=False)
+        self.dense_4h_to_h = RowParallelLinear(config.intermediate_size, config.hidden_size,
+                                               input_is_parallel=True,
+                                               perform_initialization=False)
+        if config.hidden_act != 'gelu':
+            raise ValueError(f'Unsupported activation: {config.hidden_act}. '
+                             'Only gelu is supported for now.')
+        self.act = torch.nn.GELU()
+
+    def forward(self, hidden_states):
+        hidden_states, _ = self.dense_h_to_4h(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.dense_4h_to_h(hidden_states)
+        return hidden_states
+
+
+class GPTNeoXLayer(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.use_parallel_residual = config.use_parallel_residual
+        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.attention = GPTNeoXAttention(config)
+        self.mlp = GPTNeoXMLP(config)
+
+    def forward(
+        self,
+        position_ids: torch.LongTensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        attn_input = self.input_layernorm(hidden_states)
+        attn_output = self.attention(
+            position_ids=position_ids,
+            hidden_states=attn_input,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+            cache_event=cache_event,
+        )
+
+        if self.use_parallel_residual:
+            # pseudocode:
+            # x = x + attn(ln1(x)) + mlp(ln2(x))
+            mlp_input = self.post_attention_layernorm(hidden_states)
+            mlp_output = self.mlp(mlp_input)
+            hidden_states = mlp_output + attn_output + hidden_states
+        else:
+            # pseudocode:
+            # x = x + attn(ln1(x))
+            # x = x + mlp(ln2(x))
+            attn_output = attn_output + hidden_states
+            mlp_input = self.post_attention_layernorm(attn_output)
+            mlp_output = self.mlp(mlp_input)
+            hidden_states = mlp_output + attn_output
+        return hidden_states
+
+
+class GPTNeoXModel(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+
+        self.embed_in = VocabParallelEmbedding(config.vocab_size, config.hidden_size,
+                                               perform_initialization=False)
+        self.layers = nn.ModuleList([GPTNeoXLayer(config) for _ in range(config.num_hidden_layers)])
+        self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        position_ids: torch.LongTensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> torch.Tensor:
+        hidden_states = self.embed_in(input_ids)
+        for i in range(len(self.layers)):
+            if cache_events is None:
+                cache_event = None
+            else:
+                cache_event = cache_events[i]
+            layer = self.layers[i]
+            hidden_states = layer(
+                position_ids,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                cache_event,
+            )
+        hidden_states = self.final_layer_norm(hidden_states)
+        return hidden_states
+
+
+class GPTNeoXForCausalLM(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.gpt_neox = GPTNeoXModel(config)
+        self.embed_out = ColumnParallelLinear(config.hidden_size, config.vocab_size,
+                                              bias=False, gather_output=False,
+                                              perform_initialization=False)
+        self.sampler = Sampler()
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        positions: torch.LongTensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> Dict[int, SequenceOutputs]:
+        hidden_states = self.gpt_neox(
+            input_ids, positions, kv_caches, input_metadata, cache_events)
+        next_tokens = self.sampler(
+            self.embed_out.weight, hidden_states, input_metadata)
+        return next_tokens
+
+    _column_parallel_weights = ["embed_in.weight", "embed_out.weight", "dense_h_to_4h.weight", "dense_h_to_4h.bias"]
+    _row_parallel_weights = ["dense.weight", "dense_4h_to_h.weight"]
+
+    def load_weights(self, weights_path: str):
+        tensor_model_parallel_rank = get_tensor_model_parallel_rank()
+        state_dict = self.state_dict()
+        for name, param in state_dict.items():
+            if "query_key_value" in name:
+                # NOTE(woosuk): GPT-NeoX's fused QKV has the shape of
+                # [num_heads * 3 * head_size, num_heads * head_size], while the
+                # required shape is [3 * num_heads * head_size, num_heads * head_size].
+                # Thus, we need weight conversion.
+                loaded_weight = torch.from_numpy(
+                    np.load(os.path.join(weights_path, name)))
+                shard_size = param.shape[0]
+                loaded_weight = loaded_weight[shard_size * tensor_model_parallel_rank
+                                              :shard_size * (tensor_model_parallel_rank + 1)]
+
+                num_heads = self.config.num_attention_heads
+                hidden_size = self.config.hidden_size
+                head_size = hidden_size // num_heads
+                if 'query_key_value.weight' in name:
+                    loaded_weight = loaded_weight.view(-1, 3, head_size, hidden_size)
+                    loaded_weight = loaded_weight.transpose(0, 1)
+                    loaded_weight = loaded_weight.reshape(-1, hidden_size).contiguous()
+                elif 'query_key_value.bias' in name:
+                    loaded_weight = loaded_weight.view(-1, 3, head_size)
+                    loaded_weight = loaded_weight.transpose(0, 1)
+                    loaded_weight = loaded_weight.reshape(-1).contiguous()
+                else:
+                    assert False
+            else:
+                loaded_weight = torch.from_numpy(
+                    np.load(os.path.join(weights_path, name)))
+                for p in self._column_parallel_weights:
+                    if p in name:
+                        shard_size = param.shape[0]
+                        loaded_weight = loaded_weight[
+                            shard_size * tensor_model_parallel_rank
+                            :shard_size * (tensor_model_parallel_rank + 1)]
+                        break
+                for p in self._row_parallel_weights:
+                    if p in name:
+                        shard_size = param.shape[1]
+                        loaded_weight = loaded_weight[
+                            :,
+                            shard_size * tensor_model_parallel_rank
+                            :shard_size * (tensor_model_parallel_rank + 1)]
+                        break
+
+            assert param.shape == loaded_weight.shape
+            param.data.copy_(loaded_weight)
+
+    @staticmethod
+    def get_weights(model_name: str, path: str):
+        path = os.path.join(path, f"{model_name}-np")
+        path = os.path.abspath(os.path.expanduser(path))
+        os.makedirs(path, exist_ok=True)
+        lock_path = os.path.join(path, "file_lock")
+        lock = filelock.FileLock(lock_path)
+
+        with lock:
+            test_weight_path = os.path.join(
+                path, "gpt_neox.embed_in.weight")
+            if os.path.exists(test_weight_path):
+                return path
+
+            folder = snapshot_download(model_name, allow_patterns="*.bin",
+                                       cache_dir=os.path.join(path, "cache"))
+            bin_files = glob.glob(os.path.join(folder, "*.bin"))
+
+            for bin_file in tqdm(bin_files, desc="Convert format"):
+                state = torch.load(bin_file, map_location="cpu")
+                for name, param in tqdm(state.items(), leave=False):
+                    param_path = os.path.join(path, name)
+                    with open(param_path, "wb") as f:
+                        np.save(f, param.cpu().detach().numpy())
+
+            return path
+
+    def initialize_dummy_weights(self) -> None:
+        for param in self.state_dict().values():
+            param.data.uniform_(-0.1, 0.1)