GPTBigCode (StarCoder, SantaCoder Support) (#209)

vllm/model_executor/layers/activation.py

@@ -8,6 +8,7 @@ _ACTIVATION_REGISTRY = {
     "gelu": nn.GELU(),
     "gelu_new": nn.GELU(approximate="tanh"),   # NOTE: This may introduce small rounding errors.
     "gelu_fast": nn.GELU(approximate="tanh"),  # NOTE: This may introduce small rounding errors.
+    "gelu_pytorch_tanh": nn.GELU(approximate="tanh"),  # NOTE: This may introduce small rounding errors.
     "relu": nn.ReLU(),
 }
 

vllm/model_executor/model_loader.py

@@ -6,13 +6,14 @@ import torch.nn as nn
 from transformers import PretrainedConfig
 
 from vllm.config import ModelConfig
-from vllm.model_executor.models import (GPT2LMHeadModel, GPTNeoXForCausalLM,
+from vllm.model_executor.models import (GPT2LMHeadModel, GPTBigCodeForCausalLM, GPTNeoXForCausalLM,
                                         LlamaForCausalLM, OPTForCausalLM)
 from vllm.model_executor.weight_utils import initialize_dummy_weights
 
 # TODO(woosuk): Lazy-load the model classes.
 _MODEL_REGISTRY = {
     "GPT2LMHeadModel": GPT2LMHeadModel,
+    "GPTBigCodeForCausalLM": GPTBigCodeForCausalLM,
     "GPTNeoXForCausalLM": GPTNeoXForCausalLM,
     "LlamaForCausalLM": LlamaForCausalLM,
     "OPTForCausalLM": OPTForCausalLM,

vllm/model_executor/models/__init__.py

-from vllm.model_executor.models.gpt_neox import GPTNeoXForCausalLM
 from vllm.model_executor.models.gpt2 import GPT2LMHeadModel
+from vllm.model_executor.models.gpt_bigcode import GPTBigCodeForCausalLM
+from vllm.model_executor.models.gpt_neox import GPTNeoXForCausalLM
 from vllm.model_executor.models.llama import LlamaForCausalLM
 from vllm.model_executor.models.opt import OPTForCausalLM
 
 
+
 __all__ = [
     "GPT2LMHeadModel",
+    "GPTBigCodeForCausalLM",
     "GPTNeoXForCausalLM",
     "LlamaForCausalLM",
     "OPTForCausalLM",

vllm/model_executor/models/gpt_bigcode.py

+# coding=utf-8
+# Adapted from https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt2/modeling_gpt2.py
+# Copyright 2023 The vLLM team.
+# Copyright 2023 CTranslate2, and Michael Feil
+# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+"""Inference-only GPTBigCode model compatible with HuggingFace weights.
+
+The input of the model is flattened to a 1D tensor of tokens. The model uses
+InputMetadata to extract the original 2D shape of the input.
+"""
+from typing import Dict, List, Optional, Tuple
+
+import torch
+from torch import nn
+import numpy as np
+from transformers import GPTBigCodeConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
+                                              load_tensor_parallel_weights)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.parallel_utils.tensor_parallel import (
+    VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
+from vllm.sequence import SequenceOutputs
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class GPTBigCodeAttention(nn.Module):
+
+    def __init__(self, config: GPTBigCodeConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        total_num_heads = config.num_attention_heads
+        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
+        assert total_num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = total_num_heads // tensor_model_parallel_world_size
+        self.head_dim = self.hidden_size // total_num_heads
+        self.scale = self.head_dim ** -0.5
+
+        self.c_attn = ColumnParallelLinear(self.hidden_size, 3 * self.hidden_size,
+                                           bias=True, gather_output=False,
+                                           perform_initialization=False)
+        self.c_proj = RowParallelLinear(self.hidden_size, self.hidden_size,
+                                        bias=True, input_is_parallel=True,
+                                        perform_initialization=False)
+        self.attn = PagedAttention(self.num_heads, self.head_dim,
+                                   scale=self.scale)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        qkv, _ = self.c_attn(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        key_cache, value_cache = kv_cache
+        attn_output = self.attn(
+            q, k, v, key_cache, value_cache, input_metadata, cache_event)
+        attn_output, _ = self.c_proj(attn_output)
+        return attn_output
+
+
+class GPTBigMLP(nn.Module):
+
+    def __init__(
+        self,
+        intermediate_size: int,
+        config: GPTBigCodeConfig,
+    ):
+        super().__init__()
+        hidden_size = config.hidden_size
+        self.c_fc = ColumnParallelLinear(hidden_size, intermediate_size,
+                                         bias=True, gather_output=False,
+                                         perform_initialization=False)
+        self.c_proj = RowParallelLinear(intermediate_size, hidden_size,
+                                        bias=True, input_is_parallel=True,
+                                        perform_initialization=False)
+        self.act = get_act_fn(config.activation_function)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.c_fc(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.c_proj(hidden_states)
+        return hidden_states
+
+
+class GPTBigCodeBlock(nn.Module):
+
+    def __init__(self, config: GPTBigCodeConfig):
+        super().__init__()
+        hidden_size = config.hidden_size
+        inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
+
+        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.attn = GPTBigCodeAttention(config)
+        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.mlp = GPTBigMLP(inner_dim, config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.ln_1(hidden_states)
+        attn_output = self.attn(
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+            cache_event=cache_event,
+        )
+        # residual connection
+        hidden_states = attn_output + residual
+
+        residual = hidden_states
+        hidden_states = self.ln_2(hidden_states)
+        feed_forward_hidden_states = self.mlp(hidden_states)
+        # residual connection
+        hidden_states = residual + feed_forward_hidden_states
+        return hidden_states
+
+
+class GPTBigCodeModel(nn.Module):
+
+    def __init__(self, config: GPTBigCodeConfig):
+        super().__init__()
+        self.config = config
+        assert config.add_cross_attention == False
+
+        self.embed_dim = config.hidden_size
+
+        # Optimization: While the vocab size of GPT-2 is 50257, we extend it
+        # to 50304 in order to make it divisible by 64.
+        # This improves performance since GPUs are faster if the dimension
+        # is divisible by 64. In addition, it allows us to shard the embedding
+        # layer across 2, 4, 8, or more GPUs.
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        self.wte = VocabParallelEmbedding(vocab_size, self.embed_dim)
+        self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
+        self.h = nn.ModuleList(
+            [GPTBigCodeBlock(config) for _ in range(config.num_hidden_layers)])
+        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> torch.Tensor:
+        inputs_embeds = self.wte(input_ids)
+        position_embeds = self.wpe(position_ids)
+        hidden_states = inputs_embeds + position_embeds
+
+        for i in range(len(self.h)):
+            if cache_events is None:
+                cache_event = None
+            else:
+                cache_event = cache_events[i]
+            layer = self.h[i]
+            hidden_states = layer(
+                hidden_states, kv_caches[i], input_metadata, cache_event)
+
+        hidden_states = self.ln_f(hidden_states)
+        return hidden_states
+
+
+class GPTBigCodeForCausalLM(nn.Module):
+
+    def __init__(self, config: GPTBigCodeConfig):
+        super().__init__()
+        self.config = config
+        self.transformer = GPTBigCodeModel(config)
+        # TODO(zhuohan): create a new weight after implementing pipeline
+        #                parallelism
+        self.lm_head_weight = self.transformer.wte.weight
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> Dict[int, SequenceOutputs]:
+        hidden_states = self.transformer(
+            input_ids, positions, kv_caches, input_metadata, cache_events)
+        next_tokens = self.sampler(
+            self.lm_head_weight, hidden_states, input_metadata)
+        return next_tokens
+
+    _column_parallel_weights = ["wte.weight", "c_fc.weight", "c_fc.bias"]
+    _row_parallel_weights = ["c_proj.weight"]
+
+    def load_weights(self, model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     use_np_cache: bool = False):
+        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
+        tensor_model_parallel_rank = get_tensor_model_parallel_rank()
+        state_dict = self.state_dict()
+
+        for name, loaded_weight in hf_model_weights_iterator(
+            model_name_or_path, cache_dir, use_np_cache):
+            if "lm_head.weight" in name:
+                # GPT-2 ties the weights of the embedding layer and the final
+                # linear layer.
+                continue
+            if ".attn.bias" in name:
+                # Skip attention mask.
+                # NOTE: "c_attn.bias" should not be skipped.
+                continue
+
+            param = state_dict[name]
+            
+            def _expand_mqa_mha(qkv_array, n_head, head_dim):
+                """manipulates along axis=0 from MQA to MHA
+                inputs: qkv_array.shape=((n_heads + 2) * head_dim, hidden_dim)
+                    with n_heads for q, then 1 for k, 1 for 1 v, times head dim
+                return: qkv_array.shape=(3 * n_heads * head_dim, hidden_dim)
+                
+                TODO: this function is no longer needed once vllm supports MQA.
+                """
+                qkv_array = qkv_array.numpy()
+                
+                dims_q = n_head * head_dim
+                q, k, v = np.split(qkv_array, (dims_q, dims_q + head_dim), axis=0)
+                # q is fine, but k & v have not replicated shape along the first axis
+                # as long as MQA is not nativly supported, increase memory and replicated
+                # (head_dim, hidden_dim) to (n_heads * head_dim, hidden_dim)
+                if k.ndim == 2 and v.ndim == 2:
+                    replication = (n_head, 1)  # weights
+                else:
+                    replication = n_head  # biases
+                # replicate n_head times for q, v
+                k, v = np.tile(k, replication), np.tile(v, replication)
+                # concat q, k, v along the first axis (n_heads * head_dim, hidden_dim)
+                # to (3 * n_heads * head_dim, hidden_dim)
+                qkv_array = np.concatenate((q, k, v), axis=0)
+                return torch.from_numpy(qkv_array)
+
+            # For the fused QKV linear layer, manually shard the weights.
+            if "c_attn" in name:
+                # GPT-2's fused QKV has the shape of [3 * num_heads * head_size, hidden_size].
+                # When tensor parallelism is used, we shard the weights along the head dimension.
+                total_num_heads = self.config.num_attention_heads
+                hidden_size = self.config.hidden_size
+                head_size = hidden_size // total_num_heads
+                num_heads = total_num_heads // tensor_model_parallel_world_size
+                head_start = tensor_model_parallel_rank * num_heads
+                head_end = (tensor_model_parallel_rank + 1) * num_heads
+
+                if name.endswith(".weight"):
+                    loaded_weight = _expand_mqa_mha(loaded_weight, n_head=total_num_heads, head_dim=head_size)
+                    loaded_weight = loaded_weight.view(3, total_num_heads, head_size, hidden_size)
+                    loaded_weight = loaded_weight[:, head_start:head_end, :, :]
+                    loaded_weight = loaded_weight.reshape(-1, hidden_size)
+                elif name.endswith(".bias"):
+                    loaded_weight = _expand_mqa_mha(loaded_weight, n_head=total_num_heads, head_dim=head_size)
+                    loaded_weight = loaded_weight.view(3, total_num_heads, head_size)
+                    loaded_weight = loaded_weight[:, head_start:head_end, :]
+                    loaded_weight = loaded_weight.reshape(-1)
+                else:
+                    raise ValueError(f"Unexpected parameter name {name}")
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         self._column_parallel_weights,
+                                         self._row_parallel_weights,
+                                         tensor_model_parallel_rank)