360 lines
14 KiB
Python
360 lines
14 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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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 Iterable, Optional, Set, Tuple, Union
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import torch
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from torch import nn
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from transformers import PhiConfig
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from vllm.attention import Attention
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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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 SamplerOutput, get_sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsLoRA, SupportsPP
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from .utils import (is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers,
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maybe_prefix)
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class PhiAttention(nn.Module):
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def __init__(self,
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config: PhiConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = ""):
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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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quant_config=quant_config,
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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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quant_config=quant_config,
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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 = getattr(config, "rope_theta", 10000.0)
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max_position_embeddings = getattr(config, "max_position_embeddings",
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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 = Attention(self.num_heads,
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self.head_size,
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scaling,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn")
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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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) -> 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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attn_output = self.attn(q, k, v)
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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: PhiConfig,
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quant_config: Optional[QuantizationConfig] = 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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quant_config=quant_config,
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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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quant_config=quant_config,
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)
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self.act = get_act_fn(config.hidden_act)
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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: PhiConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = ""):
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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,
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cache_config,
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quant_config,
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prefix=f"{prefix}.self_attn")
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self.mlp = PhiMLP(config, quant_config)
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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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) -> 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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)
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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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@support_torch_compile
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class PhiModel(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
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self.config = config
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self.quant_config = quant_config
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self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
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config.hidden_size)
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self.start_layer, self.end_layer, self.layers = make_layers(
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config.num_hidden_layers,
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lambda prefix: PhiLayer(
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config, cache_config, quant_config, prefix=prefix),
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prefix=f"{prefix}.layers")
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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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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(["hidden_states"],
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config.hidden_size))
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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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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intermediate_tensors: Optional[IntermediateTensors],
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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for layer in self.layers[self.start_layer:self.end_layer]:
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hidden_states = layer(positions, hidden_states)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({"hidden_states": hidden_states})
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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, SupportsLoRA, SupportsPP):
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packed_modules_mapping = {
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"qkv_proj": [
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"q_proj",
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"k_proj",
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"v_proj",
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]
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}
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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lora_config = vllm_config.lora_config
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self.config = config
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# lm_head use bias, cannot share word embeddings
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assert not config.tie_word_embeddings
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self.lora_config = lora_config
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self.quant_config = quant_config
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self.model = PhiModel(vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"))
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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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quant_config=quant_config)
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.sampler = get_sampler()
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.get_input_embeddings(input_ids)
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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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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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hidden_states = self.model(input_ids, positions, intermediate_tensors,
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inputs_embeds)
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return hidden_states
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def compute_logits(
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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[torch.Tensor]:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata, self.lm_head.bias)
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return logits
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def sample(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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next_tokens = self.sampler(logits, sampling_metadata)
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return next_tokens
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def load_weights(self, weights: Iterable[Tuple[str,
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torch.Tensor]]) -> Set[str]:
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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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loaded_params: Set[str] = set()
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for name, loaded_weight in weights:
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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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if is_pp_missing_parameter(name, self):
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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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if is_pp_missing_parameter(name, self):
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continue
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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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loaded_params.add(name)
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return loaded_params
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