306 lines
11 KiB
Python
306 lines
11 KiB
Python
# coding=utf-8
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# Adapted from
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# https://huggingface.co/microsoft/phi-1_5/blob/main/modeling_phi.py
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# Copyright 2023 The vLLM team.
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT license.
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#
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# BSD 3-Clause License
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#
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# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# * Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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# * Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# * Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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"""Inference-only Phi-1.5 model compatible with HuggingFace weights."""
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from typing import List, Optional, Tuple
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from vllm.model_executor.input_metadata import InputMetadata
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.attention import PagedAttention
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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LinearMethodBase,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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VocabParallelEmbedding, ParallelLMHead)
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from vllm.model_executor.parallel_utils.parallel_state import (
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get_tensor_model_parallel_world_size)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.model_executor.weight_utils import (default_weight_loader,
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hf_model_weights_iterator)
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from vllm.sequence import SamplerOutput
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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class PhiAttention(nn.Module):
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def __init__(self,
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config: PretrainedConfig,
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linear_method: Optional[LinearMethodBase] = None):
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super().__init__()
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self.total_num_heads = config.num_attention_heads
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self.hidden_size = config.hidden_size
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self.head_size = self.hidden_size // self.total_num_heads
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tensor_model_parallel_world_size = (
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get_tensor_model_parallel_world_size())
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assert self.total_num_heads % tensor_model_parallel_world_size == 0
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self.num_heads = (self.total_num_heads //
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tensor_model_parallel_world_size)
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# pylint: disable=C0103
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self.qkv_proj = QKVParallelLinear(
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self.hidden_size,
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self.head_size,
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self.total_num_heads,
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bias=True,
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linear_method=linear_method,
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)
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self.dense = RowParallelLinear(
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self.hidden_size,
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self.hidden_size,
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linear_method=linear_method,
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)
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scaling = self.head_size**-0.5
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rotary_dim = int(config.partial_rotary_factor *
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(config.hidden_size // config.num_attention_heads))
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assert rotary_dim % 2 == 0
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# pylint: disable=C0301
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# Refer to:
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# https://huggingface.co/microsoft/phi-1_5/blob/d212a789620c380ff32ca1d1ee9943a777360987/modeling_phi.py#L518
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rope_theta = 10000
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max_position_embeddings = getattr(config, "n_positions", 2048)
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self.rotary_emb = get_rope(
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self.head_size,
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rotary_dim=rotary_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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)
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self.attn = PagedAttention(self.num_heads, self.head_size, scaling)
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def forward(
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self,
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position_ids: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: KVCache,
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input_metadata: InputMetadata,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.chunk(chunks=3, dim=-1)
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q, k = self.rotary_emb(position_ids, q, k)
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k_cache, v_cache = kv_cache
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attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
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output, _ = self.dense(attn_output)
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return output
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class PhiMLP(nn.Module):
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def __init__(self,
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config: PretrainedConfig,
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linear_method: Optional[LinearMethodBase] = None):
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super().__init__()
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n_inner = getattr(config, "n_inner", None)
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n_inner = n_inner if n_inner is not None else 4 * config.hidden_size
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self.fc1 = ColumnParallelLinear(
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config.hidden_size,
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n_inner,
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linear_method=linear_method,
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)
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self.fc2 = RowParallelLinear(
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n_inner,
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config.hidden_size,
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linear_method=linear_method,
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)
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quant_config = getattr(linear_method, "quant_config", None)
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self.act = get_act_fn(config.hidden_act, quant_config, n_inner)
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def forward(self, hidden_states):
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hidden_states, _ = self.fc1(hidden_states)
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hidden_states = self.act(hidden_states)
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hidden_states, _ = self.fc2(hidden_states)
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return hidden_states
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class PhiLayer(nn.Module):
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def __init__(self,
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config: PretrainedConfig,
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linear_method: Optional[LinearMethodBase] = None):
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super().__init__()
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self.input_layernorm = nn.LayerNorm(config.hidden_size,
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eps=config.layer_norm_eps)
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self.self_attn = PhiAttention(config, linear_method)
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self.mlp = PhiMLP(config, linear_method)
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def forward(
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self,
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position_ids: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: KVCache,
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input_metadata: InputMetadata,
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) -> torch.Tensor:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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attn_outputs = self.self_attn(
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position_ids=position_ids,
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hidden_states=hidden_states,
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kv_cache=kv_cache,
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input_metadata=input_metadata,
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)
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feed_forward_hidden_states = self.mlp(hidden_states)
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hidden_states = attn_outputs + feed_forward_hidden_states + residual
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return hidden_states
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class PhiModel(nn.Module):
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def __init__(self,
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config: PretrainedConfig,
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linear_method: Optional[LinearMethodBase] = None):
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super().__init__()
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self.config = config
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self.linear_method = linear_method
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self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
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config.hidden_size)
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self.layers = nn.ModuleList([
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PhiLayer(config, linear_method)
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for _ in range(config.num_hidden_layers)
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])
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self.final_layernorm = nn.LayerNorm(config.hidden_size,
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eps=config.layer_norm_eps)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: List[KVCache],
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input_metadata: InputMetadata,
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) -> torch.Tensor:
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hidden_states = self.embed_tokens(input_ids)
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for i in range(self.config.num_hidden_layers):
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layer = self.layers[i]
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hidden_states = layer(
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positions,
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hidden_states,
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kv_caches[i],
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input_metadata,
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)
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hidden_states = self.final_layernorm(hidden_states)
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return hidden_states
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class PhiForCausalLM(nn.Module):
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def __init__(self,
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config: PretrainedConfig,
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linear_method: Optional[LinearMethodBase] = None):
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super().__init__()
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self.config = config
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self.linear_method = linear_method
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self.model = PhiModel(config, linear_method)
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self.lm_head = ParallelLMHead(config.vocab_size,
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config.hidden_size,
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bias=True)
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self.sampler = Sampler(config.vocab_size)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: List[KVCache],
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input_metadata: InputMetadata,
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) -> torch.Tensor:
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hidden_states = self.model(input_ids, positions, kv_caches,
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input_metadata)
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return hidden_states
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def sample(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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head = self.lm_head
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next_tokens = self.sampler(head.weight, hidden_states,
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sampling_metadata, head.bias)
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return next_tokens
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def load_weights(self,
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model_name_or_path: str,
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cache_dir: Optional[str] = None,
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load_format: str = "auto",
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revision: Optional[str] = None):
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v")
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]
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in hf_model_weights_iterator(
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model_name_or_path, cache_dir, load_format, revision):
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if "rotary_emb.inv_freq" in name:
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continue
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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# pylint: disable=E1136
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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