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Support Microsoft Phi 1.5 (vllm-project#1664)
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"EleutherAI/pythia-70m", | ||
"bigscience/bloom-560m", | ||
"mosaicml/mpt-7b", | ||
"microsoft/phi-1_5", | ||
] | ||
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# coding=utf-8 | ||
# Adapted from | ||
# https://huggingface.co/microsoft/phi-1_5/blob/main/modeling_phi.py | ||
# Copyright 2023 The vLLM team. | ||
# Copyright (c) Microsoft Corporation. | ||
# Licensed under the MIT license. | ||
# | ||
# BSD 3-Clause License | ||
# | ||
# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu. | ||
# All rights reserved. | ||
# | ||
# Redistribution and use in source and binary forms, with or without | ||
# modification, are permitted provided that the following conditions are met: | ||
# | ||
# * Redistributions of source code must retain the above copyright notice, this | ||
# list of conditions and the following disclaimer. | ||
# | ||
# * Redistributions in binary form must reproduce the above copyright notice, | ||
# this list of conditions and the following disclaimer in the documentation | ||
# and/or other materials provided with the distribution. | ||
# | ||
# * Neither the name of the copyright holder nor the names of its | ||
# contributors may be used to endorse or promote products derived from | ||
# this software without specific prior written permission. | ||
# | ||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" | ||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | ||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE | ||
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE | ||
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL | ||
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR | ||
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER | ||
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, | ||
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE | ||
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | ||
"""Inference-only Phi-1.5 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 List, Optional, Tuple | ||
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import torch | ||
from torch import nn | ||
from transformers import PretrainedConfig | ||
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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 PagedAttentionWithRoPE | ||
from vllm.model_executor.layers.linear import (ColumnParallelLinear, | ||
LinearMethodBase, | ||
QKVParallelLinear, | ||
RowParallelLinear) | ||
from vllm.model_executor.layers.sampler import Sampler | ||
from vllm.model_executor.layers.vocab_parallel_embedding import ( | ||
VocabParallelEmbedding, ParallelLMHead) | ||
from vllm.model_executor.parallel_utils.parallel_state import ( | ||
get_tensor_model_parallel_world_size) | ||
from vllm.model_executor.weight_utils import (default_weight_loader, | ||
hf_model_weights_iterator) | ||
from vllm.sequence import SamplerOutput | ||
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KVCache = Tuple[torch.Tensor, torch.Tensor] | ||
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class PhiEmbedding(nn.Module): | ||
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def __init__(self, config: PretrainedConfig): | ||
super().__init__() | ||
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self.wte = VocabParallelEmbedding( | ||
config.vocab_size, | ||
config.hidden_size, | ||
) | ||
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def forward(self, input_ids: torch.LongTensor): | ||
return self.wte(input_ids) | ||
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class PhiAttention(nn.Module): | ||
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def __init__(self, | ||
config: PretrainedConfig, | ||
linear_method: Optional[LinearMethodBase] = None): | ||
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 | ||
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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) | ||
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# pylint: disable=C0103 | ||
self.Wqkv = QKVParallelLinear( | ||
self.hidden_size, | ||
self.head_size, | ||
self.total_num_heads, | ||
linear_method=linear_method, | ||
) | ||
self.qkv_proj = QKVParallelLinear( | ||
config.hidden_size, | ||
self.head_size, | ||
self.total_num_heads, | ||
bias=False, | ||
linear_method=linear_method, | ||
) | ||
self.out_proj = RowParallelLinear( | ||
self.hidden_size, | ||
self.hidden_size, | ||
linear_method=linear_method, | ||
) | ||
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scaling = self.head_size**-0.5 | ||
rotary_dim = config.rotary_dim | ||
assert rotary_dim % 2 == 0 | ||
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# pylint: disable=C0301 | ||
# Refer to: | ||
# https://huggingface.co/microsoft/phi-1_5/blob/d212a789620c380ff32ca1d1ee9943a777360987/modeling_phi.py#L518 | ||
rope_theta = 10000 | ||
max_position_embeddings = getattr(config, "n_positions", 2048) | ||
self.attn = PagedAttentionWithRoPE( | ||
self.num_heads, | ||
self.head_size, | ||
scaling, | ||
rotary_dim, | ||
base=rope_theta, | ||
max_position=max_position_embeddings) | ||
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def forward( | ||
self, | ||
position_ids: torch.Tensor, | ||
hidden_states: torch.Tensor, | ||
kv_cache: KVCache, | ||
input_metadata: InputMetadata, | ||
cache_event: Optional[torch.cuda.Event], | ||
) -> torch.Tensor: | ||
qkv, _ = self.Wqkv(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.out_proj(attn_output) | ||
return output | ||
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class PhiMLP(nn.Module): | ||
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def __init__(self, | ||
config: PretrainedConfig, | ||
linear_method: Optional[LinearMethodBase] = None): | ||
super().__init__() | ||
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n_inner = getattr(config, "n_inner", None) | ||
n_inner = n_inner if n_inner is not None else 4 * config.hidden_size | ||
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self.fc1 = ColumnParallelLinear( | ||
config.hidden_size, | ||
n_inner, | ||
linear_method=linear_method, | ||
) | ||
self.fc2 = RowParallelLinear( | ||
n_inner, | ||
config.hidden_size, | ||
linear_method=linear_method, | ||
) | ||
self.act = get_act_fn(config.activation_function) | ||
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def forward(self, hidden_states): | ||
hidden_states, _ = self.fc1(hidden_states) | ||
hidden_states = self.act(hidden_states) | ||
hidden_states, _ = self.fc2(hidden_states) | ||
return hidden_states | ||
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class PhiLayer(nn.Module): | ||
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def __init__(self, | ||
config: PretrainedConfig, | ||
linear_method: Optional[LinearMethodBase] = None): | ||
super().__init__() | ||
self.ln = nn.LayerNorm(config.hidden_size, | ||
eps=config.layer_norm_epsilon) | ||
self.mixer = PhiAttention(config, linear_method) | ||
self.mlp = PhiMLP(config, linear_method) | ||
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def forward( | ||
self, | ||
position_ids: torch.Tensor, | ||
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(hidden_states) | ||
attn_outputs = self.mixer( | ||
position_ids=position_ids, | ||
hidden_states=hidden_states, | ||
kv_cache=kv_cache, | ||
input_metadata=input_metadata, | ||
cache_event=cache_event, | ||
) | ||
feed_forward_hidden_states = self.mlp(hidden_states) | ||
hidden_states = attn_outputs + feed_forward_hidden_states + residual | ||
return hidden_states | ||
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class PhiCausalLMHead(nn.Module): | ||
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def __init__(self, config: PretrainedConfig): | ||
super().__init__() | ||
self.ln = nn.LayerNorm(config.hidden_size, | ||
eps=config.layer_norm_epsilon) | ||
self.linear = ParallelLMHead(config.vocab_size, | ||
config.hidden_size, | ||
bias=True) | ||
self.sampler = Sampler(config.vocab_size) | ||
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def forward( | ||
self, | ||
hidden_states: torch.Tensor, | ||
input_metadata: InputMetadata, | ||
): | ||
hidden_states = self.ln(hidden_states) | ||
next_tokens = self.sampler(self.linear.weight, hidden_states, | ||
input_metadata, self.linear.bias) | ||
return next_tokens | ||
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class PhiModel(nn.Module): | ||
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def __init__(self, | ||
config: PretrainedConfig, | ||
linear_method: Optional[LinearMethodBase] = None): | ||
super().__init__() | ||
self.config = config | ||
self.linear_method = linear_method | ||
self.embd = PhiEmbedding(config) | ||
self.h = nn.ModuleList([ | ||
PhiLayer(config, linear_method) | ||
for _ in range(config.num_hidden_layers) | ||
]) | ||
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def forward( | ||
self, | ||
input_ids: torch.Tensor, | ||
positions: torch.Tensor, | ||
kv_caches: List[KVCache], | ||
input_metadata: InputMetadata, | ||
cache_events: Optional[List[torch.cuda.Event]], | ||
) -> SamplerOutput: | ||
hidden_states = self.embd(input_ids) | ||
for i in range(self.config.num_hidden_layers): | ||
if cache_events is None: | ||
cache_event = None | ||
else: | ||
cache_event = cache_events[i] | ||
layer = self.h[i] | ||
hidden_states = layer( | ||
positions, | ||
hidden_states, | ||
kv_caches[i], | ||
input_metadata, | ||
cache_event, | ||
) | ||
return hidden_states | ||
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class PhiForCausalLM(nn.Module): | ||
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def __init__(self, | ||
config: PretrainedConfig, | ||
linear_method: Optional[LinearMethodBase] = None): | ||
super().__init__() | ||
self.config = config | ||
self.linear_method = linear_method | ||
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self.transformer = PhiModel(config, linear_method) | ||
self.lm_head = PhiCausalLMHead(config) | ||
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def forward( | ||
self, | ||
input_ids: torch.Tensor, | ||
positions: torch.Tensor, | ||
kv_caches: List[KVCache], | ||
input_metadata: InputMetadata, | ||
cache_events: Optional[List[torch.cuda.Event]], | ||
) -> SamplerOutput: | ||
hidden_states = self.transformer(input_ids, positions, kv_caches, | ||
input_metadata, cache_events) | ||
lm_logits = self.lm_head(hidden_states, input_metadata) | ||
return lm_logits | ||
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def load_weights(self, | ||
model_name_or_path: str, | ||
cache_dir: Optional[str] = None, | ||
load_format: str = "auto", | ||
revision: Optional[str] = None): | ||
params_dict = dict(self.named_parameters()) | ||
for name, loaded_weight in hf_model_weights_iterator( | ||
model_name_or_path, cache_dir, load_format, revision): | ||
if "rotary_emb.inv_freq" in name: | ||
continue | ||
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# pylint: disable=E1136 | ||
param = params_dict[name] | ||
weight_loader = getattr(param, "weight_loader", | ||
default_weight_loader) | ||
weight_loader(param, loaded_weight) |