682 lines
25 KiB
Python
682 lines
25 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Inference-only PhiMoE model."""
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from collections.abc import Iterable
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from itertools import islice
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from typing import Optional, Union
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import torch
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from torch import nn
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from transformers.configuration_utils import PretrainedConfig
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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.fused_moe import FusedMoE
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from vllm.model_executor.layers.linear import (QKVParallelLinear,
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ReplicatedLinear,
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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.base_config import (
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QuantizationConfig)
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader, maybe_remap_kv_scale_name)
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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 (AutoWeightsLoader, 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 PhiMoEConfig(PretrainedConfig):
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model_type = "phimoe"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=32000,
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hidden_size=4096,
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intermediate_size=14336,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=8,
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head_dim=None,
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hidden_act="silu",
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max_position_embeddings=4096 * 32,
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initializer_range=0.02,
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rms_norm_eps=1e-5,
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use_cache=True,
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pad_token_id=None,
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bos_token_id=1,
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eos_token_id=2,
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tie_word_embeddings=False,
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rope_theta=1e6,
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sliding_window=None,
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attention_dropout=0.0,
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num_experts_per_tok=2,
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num_local_experts=16,
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output_router_logits=False,
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router_aux_loss_coef=0.001,
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router_jitter_noise=0.0,
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attention_bias=False,
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lm_head_bias=False,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.sliding_window = sliding_window
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self.attention_bias = attention_bias
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self.lm_head_bias = lm_head_bias
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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if head_dim is None:
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head_dim = hidden_size // num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.head_dim = head_dim
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.attention_dropout = attention_dropout
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self.num_experts_per_tok = num_experts_per_tok
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self.num_local_experts = num_local_experts
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self.output_router_logits = output_router_logits
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self.router_aux_loss_coef = router_aux_loss_coef
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self.router_jitter_noise = router_jitter_noise
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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class mp(torch.autograd.Function):
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@staticmethod
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def forward(
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ctx,
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scores: torch.Tensor,
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multiplier: torch.Tensor,
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selected_experts: torch.Tensor,
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masked_gates: torch.Tensor,
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mask_for_one: torch.Tensor,
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):
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ctx.save_for_backward(multiplier, selected_experts, masked_gates)
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return multiplier * mask_for_one
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@staticmethod
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def backward(
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ctx,
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grad_at_output: torch.Tensor,
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):
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multiplier, selected_experts, masked_gates = ctx.saved_tensors
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grad_at_output = grad_at_output * multiplier
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grad_at_scores_expanded = masked_gates * grad_at_output.mul(-1)
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grad_at_scores_expanded.scatter_add_(
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dim=-1,
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index=selected_experts,
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src=grad_at_output,
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)
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return (
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grad_at_scores_expanded,
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None,
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None,
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None,
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None,
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)
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def sparsemixer(scores, jitter_eps=0.01):
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################ first expert ################
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with torch.no_grad():
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# compute mask for sparsity
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mask_logits_threshold, max_ind = scores.max(dim=-1, keepdim=True)
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factor = scores.abs().clamp(min=mask_logits_threshold)
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mask_logits_threshold = ((mask_logits_threshold - scores) /
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factor) > (2 * jitter_eps)
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# apply mask
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masked_gates = scores.masked_fill(mask_logits_threshold, float("-inf"))
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selected_experts = max_ind
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# compute scores for gradients
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masked_gates = torch.softmax(masked_gates, dim=-1)
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multiplier_o = masked_gates.gather(dim=-1, index=selected_experts)
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multiplier = multiplier_o
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# masked out first expert
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masked_scores = torch.scatter(
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scores,
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-1,
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selected_experts,
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float("-inf"),
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)
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with torch.no_grad():
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# compute mask for sparsity
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mask_logits_threshold, max_ind = masked_scores.max(dim=-1,
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keepdim=True)
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factor = scores.abs().clamp(min=mask_logits_threshold)
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mask_logits_threshold = ((mask_logits_threshold - scores) /
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factor) > (2 * jitter_eps)
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# apply mask
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masked_gates_top2 = masked_scores.masked_fill(mask_logits_threshold,
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float("-inf"))
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selected_experts_top2 = max_ind
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# compute scores for gradients
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masked_gates_top2 = torch.softmax(masked_gates_top2, dim=-1)
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multiplier_top2 = masked_gates_top2.gather(dim=-1,
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index=selected_experts_top2)
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multiplier = torch.concat((multiplier, multiplier_top2), dim=-1)
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selected_experts = torch.concat((selected_experts, selected_experts_top2),
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dim=-1)
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return (
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multiplier,
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selected_experts,
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)
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def phimoe_routing_function(
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hidden_states: torch.Tensor,
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gating_output: torch.Tensor,
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topk: int,
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renormalize: bool,
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):
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assert hidden_states.shape[0] == gating_output.shape[0], (
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"Number of tokens mismatch")
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assert topk == 2, "Only top-2 routing is supported"
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assert renormalize is False, "Renormalization is not supported"
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topk_weights, topk_ids = sparsemixer(gating_output)
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return topk_weights, topk_ids
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class PhiMoE(nn.Module):
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"""A tensor-parallel MoE implementation for PhiMoE that shards each expert
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across all ranks.
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Each expert's weights are sharded across all ranks and a fused MoE
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kernel is used for the forward pass, and finally we reduce the outputs
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across ranks.
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"""
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def __init__(
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self,
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num_experts: int,
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top_k: int,
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hidden_size: int,
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intermediate_size: int,
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params_dtype: Optional[torch.dtype] = None,
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quant_config: Optional[QuantizationConfig] = None,
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tp_size: Optional[int] = None,
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prefix: str = "",
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):
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super().__init__()
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self.hidden_size = hidden_size
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# Gate always runs at half / full precision for now.
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self.gate = ReplicatedLinear(
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hidden_size,
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num_experts,
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bias=False,
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params_dtype=params_dtype,
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quant_config=None,
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)
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self.experts = FusedMoE(
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num_experts=num_experts,
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top_k=top_k,
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hidden_size=hidden_size,
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intermediate_size=intermediate_size,
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params_dtype=params_dtype,
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reduce_results=True,
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renormalize=False,
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quant_config=quant_config,
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tp_size=tp_size,
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custom_routing_function=phimoe_routing_function,
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prefix=f"{prefix}.experts")
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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# NOTE: hidden_states can have either 1D or 2D shape.
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orig_shape = hidden_states.shape
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hidden_states = hidden_states.view(-1, self.hidden_size)
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# router_logits: (num_tokens, n_experts)
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router_logits, _ = self.gate(hidden_states)
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final_hidden_states = self.experts(hidden_states, router_logits)
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return final_hidden_states.view(orig_shape)
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class PhiMoEAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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head_dim: Optional[int] = None,
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max_position: int = 4096 * 32,
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rope_theta: float = 10000,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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rope_scaling: Optional[dict] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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if head_dim is None:
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head_dim = hidden_size // num_heads
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self.head_dim = head_dim
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=True,
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quant_config=quant_config,
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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hidden_size,
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bias=True,
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quant_config=quant_config,
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)
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position,
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base=int(self.rope_theta),
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is_neox_style=True,
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rope_scaling=self.rope_scaling,
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)
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self.attn = Attention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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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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)
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def forward(
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self,
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positions: 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.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v)
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output, _ = self.o_proj(attn_output)
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return output
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class PhiMoEDecoderLayer(nn.Module):
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def __init__(
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self,
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config: PhiMoEConfig,
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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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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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# Requires transformers > 4.32.0
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rope_theta = getattr(config, "rope_theta", 10000)
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self.self_attn = PhiMoEAttention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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max_position=config.max_position_embeddings,
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num_kv_heads=config.num_key_value_heads,
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head_dim=getattr(config, "head_dim",
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self.hidden_size // config.num_attention_heads),
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rope_theta=rope_theta,
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cache_config=cache_config,
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quant_config=quant_config,
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rope_scaling=config.rope_scaling,
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prefix=f"{prefix}.self_attn",
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)
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self.block_sparse_moe = PhiMoE(
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num_experts=config.num_local_experts,
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top_k=config.num_experts_per_tok,
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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quant_config=quant_config,
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prefix=f"{prefix}.block_sparse_moe",
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)
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self.input_layernorm = nn.LayerNorm(config.hidden_size,
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eps=config.rms_norm_eps,
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elementwise_affine=True)
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self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
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eps=config.rms_norm_eps,
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elementwise_affine=True)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: Optional[torch.Tensor],
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) -> torch.Tensor:
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residual = hidden_states
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# Self Attention
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hidden_states = self.input_layernorm(hidden_states)
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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)
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hidden_states = hidden_states + residual
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# Fully Connected
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.block_sparse_moe(hidden_states)
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hidden_states = hidden_states + residual
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return hidden_states, residual
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@support_torch_compile
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class PhiMoEModel(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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lora_config = vllm_config.lora_config
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lora_vocab = ((lora_config.lora_extra_vocab_size *
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(lora_config.max_loras or 1)) if lora_config else 0)
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self.vocab_size = config.vocab_size + lora_vocab
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self.org_vocab_size = config.vocab_size
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self.config = config
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self.quant_config = quant_config
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self.embed_tokens = VocabParallelEmbedding(
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self.vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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)
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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: PhiMoEDecoderLayer(
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config, cache_config, quant_config, prefix=prefix),
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prefix=f"{prefix}.layers")
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self.norm = nn.LayerNorm(config.hidden_size,
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eps=config.rms_norm_eps,
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elementwise_affine=True)
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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], 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(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors],
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
if get_pp_group().is_first_rank:
|
|
if inputs_embeds is not None:
|
|
hidden_states = inputs_embeds
|
|
else:
|
|
hidden_states = self.get_input_embeddings(input_ids)
|
|
residual = None
|
|
else:
|
|
assert intermediate_tensors is not None
|
|
hidden_states = intermediate_tensors["hidden_states"]
|
|
residual = intermediate_tensors["residual"]
|
|
|
|
for layer in islice(self.layers, self.start_layer, self.end_layer):
|
|
hidden_states, residual = layer(
|
|
positions,
|
|
hidden_states,
|
|
residual,
|
|
)
|
|
|
|
if not get_pp_group().is_last_rank:
|
|
return IntermediateTensors({
|
|
"hidden_states": hidden_states,
|
|
"residual": residual
|
|
})
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
return hidden_states
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
return FusedMoE.make_expert_params_mapping(
|
|
ckpt_gate_proj_name="w1",
|
|
ckpt_down_proj_name="w2",
|
|
ckpt_up_proj_name="w3",
|
|
num_experts=self.config.num_local_experts,
|
|
)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str,
|
|
torch.Tensor]]) -> set[str]:
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("qkv_proj", "q_proj", "q"),
|
|
("qkv_proj", "k_proj", "k"),
|
|
("qkv_proj", "v_proj", "v"),
|
|
]
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
loaded_params: set[str] = set()
|
|
expert_params_mapping = self.get_expert_mapping()
|
|
for name, loaded_weight in weights:
|
|
if (self.quant_config is not None and
|
|
(scale_name := self.quant_config.get_cache_scale(name))):
|
|
# Loading kv cache quantization scales
|
|
param = params_dict[scale_name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
|
|
loaded_weight[0])
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(scale_name)
|
|
continue
|
|
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
# Skip layers on other devices.
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(param, loaded_weight, shard_id)
|
|
break
|
|
else:
|
|
for mapping in expert_params_mapping:
|
|
param_name, weight_name, expert_id, shard_id = mapping
|
|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
# Skip layers on other devices.
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(
|
|
param,
|
|
loaded_weight,
|
|
name,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id,
|
|
)
|
|
break
|
|
else:
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
# Skip layers on other devices.
|
|
if is_pp_missing_parameter(name, self):
|
|
continue
|
|
# Remapping the name of FP8 kv-scale.
|
|
name = maybe_remap_kv_scale_name(name, params_dict)
|
|
if name is None:
|
|
continue
|
|
|
|
param = params_dict[name]
|
|
weight_loader = getattr(param, "weight_loader",
|
|
default_weight_loader)
|
|
weight_loader(param, loaded_weight)
|
|
loaded_params.add(name)
|
|
return loaded_params
|
|
|
|
|
|
class PhiMoEForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
|
|
fall_back_to_pt_during_load = False
|
|
|
|
packed_modules_mapping = {
|
|
"qkv_proj": [
|
|
"q_proj",
|
|
"k_proj",
|
|
"v_proj",
|
|
],
|
|
}
|
|
|
|
# LoRA specific attributes
|
|
embedding_modules = {
|
|
"embed_tokens": "input_embeddings",
|
|
"lm_head": "output_embeddings",
|
|
}
|
|
embedding_padding_modules = ["lm_head"]
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
lora_config = vllm_config.lora_config
|
|
self.config = config
|
|
self.lora_config = lora_config
|
|
self.quant_config = vllm_config.quant_config
|
|
|
|
self.model = PhiMoEModel(vllm_config=vllm_config,
|
|
prefix=maybe_prefix(prefix, "model"))
|
|
self.unpadded_vocab_size = config.vocab_size
|
|
if lora_config:
|
|
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
|
|
self.lm_head = ParallelLMHead(
|
|
self.unpadded_vocab_size,
|
|
config.hidden_size,
|
|
org_num_embeddings=config.vocab_size,
|
|
padding_size=(
|
|
DEFAULT_VOCAB_PADDING_SIZE
|
|
# We need bigger padding if using lora for kernel
|
|
# compatibility
|
|
if not lora_config else lora_config.lora_vocab_padding_size),
|
|
quant_config=None,
|
|
bias=True,
|
|
)
|
|
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
|
|
config.vocab_size)
|
|
|
|
self.make_empty_intermediate_tensors = (
|
|
self.model.make_empty_intermediate_tensors)
|
|
|
|
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.model.get_input_embeddings(input_ids)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
hidden_states = self.model(input_ids, positions, intermediate_tensors,
|
|
inputs_embeds)
|
|
return hidden_states
|
|
|
|
def compute_logits(self, hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata) -> torch.Tensor:
|
|
logits = self.logits_processor(self.lm_head, hidden_states,
|
|
sampling_metadata)
|
|
return logits
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str,
|
|
torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights)
|
|
|
|
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
|
|
return self.model.get_expert_mapping()
|