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[Model] Add support for GraniteMoeShared models (#13313)
Signed-off-by: Travis Johnson <tsjohnso@us.ibm.com> Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
This commit is contained in:
Travis Johnson
GitHub
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c060b71408
@ -298,6 +298,11 @@ See [this page](#generative-models) for more information on how to use generativ
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* `ibm-granite/granite-3.0-1b-a400m-base`, `ibm-granite/granite-3.0-3b-a800m-instruct`, `ibm/PowerMoE-3b`, etc.
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* ✅︎
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* ✅︎
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- * `GraniteMoeSharedForCausalLM`
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* Granite MoE Shared
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* `ibm-research/moe-7b-1b-active-shared-experts` (test model)
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* ✅︎
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* ✅︎
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- * `GritLM`
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* GritLM
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* `parasail-ai/GritLM-7B-vllm`.
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@ -131,6 +131,8 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
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"GPTNeoXForCausalLM": _HfExamplesInfo("EleutherAI/pythia-160m"),
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"GraniteForCausalLM": _HfExamplesInfo("ibm/PowerLM-3b"),
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"GraniteMoeForCausalLM": _HfExamplesInfo("ibm/PowerMoE-3b"),
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"GraniteMoeSharedForCausalLM": _HfExamplesInfo("ibm-research/moe-7b-1b-active-shared-experts", # noqa: E501
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min_transformers_version="4.49"), # noqa: E501
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"Grok1ModelForCausalLM": _HfExamplesInfo("hpcai-tech/grok-1",
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trust_remote_code=True),
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"InternLMForCausalLM": _HfExamplesInfo("internlm/internlm-chat-7b",
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343
vllm/model_executor/models/granitemoeshared.py
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343
vllm/model_executor/models/granitemoeshared.py
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@ -0,0 +1,343 @@
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# SPDX-License-Identifier: Apache-2.0
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"""Inference-only GraniteMoeShared model.
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The architecture is the same as granitemoe but with the addition of shared
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experts.
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"""
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from typing import Iterable, Optional, Set, Tuple
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import torch
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from torch import nn
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from transformers.models.granitemoeshared import GraniteMoeSharedConfig
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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
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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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.sampler import SamplerOutput, get_sampler
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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.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from . import mixtral
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from .granitemoe import GraniteMoeAttention, GraniteMoeMoE
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from .interfaces import SupportsLoRA, SupportsPP
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from .utils import make_layers, maybe_prefix
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class GraniteMoeSharedMLP(nn.Module):
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def __init__(
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self,
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config: GraniteMoeSharedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.input_size = config.hidden_size
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self.hidden_size = config.shared_intermediate_size
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self.input_linear = MergedColumnParallelLinear(
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input_size=self.input_size,
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output_sizes=[self.hidden_size] * 2,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.input_linear")
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self.output_linear = RowParallelLinear(
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self.hidden_size,
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self.input_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.output_linear")
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if config.hidden_act != "silu":
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raise ValueError(f"Unsupported activation: {config.hidden_act}. "
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"Only silu is supported for now.")
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self.act_fn = SiluAndMul()
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states, _ = self.input_linear(hidden_states)
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hidden_states = self.act_fn(hidden_states)
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hidden_states, _ = self.output_linear(hidden_states)
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return hidden_states
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class GraniteMoeSharedDecoderLayer(nn.Module):
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def __init__(
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self,
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config: GraniteMoeSharedConfig,
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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 = GraniteMoeAttention(
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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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rope_theta=rope_theta,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.self_attn",
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attention_multiplier=config.attention_multiplier)
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self.block_sparse_moe = GraniteMoeMoE(
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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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self.shared_mlp = None if \
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getattr(config, 'shared_intermediate_size', 0) == 0 \
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else GraniteMoeSharedMLP(
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config,
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quant_config=quant_config,
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prefix=f"{prefix}.shared_mlp"
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)
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self.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.residual_multiplier = config.residual_multiplier
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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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# Self Attention
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residual = hidden_states
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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 = residual + hidden_states * self.residual_multiplier
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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if self.shared_mlp is None:
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hidden_states = self.block_sparse_moe(hidden_states)
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else:
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# create a copy since block_sparse_moe modifies in-place
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moe_hidden_states = hidden_states.clone()
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moe_hidden_states = self.block_sparse_moe(moe_hidden_states)
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hidden_states = moe_hidden_states + self.shared_mlp(hidden_states)
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del moe_hidden_states
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hidden_states = residual + hidden_states * self.residual_multiplier
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return hidden_states
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@support_torch_compile
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class GraniteMoeSharedModel(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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self.padding_idx = config.pad_token_id
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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.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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quant_config=quant_config,
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)
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self.embedding_multiplier = config.embedding_multiplier
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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: GraniteMoeSharedDecoderLayer(
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config, cache_config, quant_config=quant_config, prefix=prefix
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),
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prefix=f"{prefix}.layers")
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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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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) -> torch.Tensor:
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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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hidden_states *= self.embedding_multiplier
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residual = None
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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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residual = intermediate_tensors["residual"]
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for i in range(self.start_layer, self.end_layer):
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layer = self.layers[i]
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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({
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"hidden_states": hidden_states,
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"residual": residual
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})
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hidden_states = self.norm(hidden_states)
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return hidden_states
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class GraniteMoeSharedForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
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fall_back_to_pt_during_load = False
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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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# LoRA specific attributes
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embedding_modules = {
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"embed_tokens": "input_embeddings",
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"lm_head": "output_embeddings",
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}
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embedding_padding_modules = ["lm_head"]
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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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self.lora_config = lora_config
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self.quant_config = quant_config
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self.model = GraniteMoeSharedModel(vllm_config=vllm_config,
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prefix=maybe_prefix(
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prefix, "model"))
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self.unpadded_vocab_size = config.vocab_size
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if lora_config:
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self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
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self.lm_head = ParallelLMHead(
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self.unpadded_vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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padding_size=DEFAULT_VOCAB_PADDING_SIZE
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# We need bigger padding if using lora for kernel
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# compatibility
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if not lora_config else lora_config.lora_vocab_padding_size,
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "lm_head"))
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if config.tie_word_embeddings:
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self.lm_head.weight = self.model.embed_tokens.weight
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self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
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config.vocab_size,
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scale=1 /
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self.config.logits_scaling)
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self.sampler = get_sampler()
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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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) -> torch.Tensor:
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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, hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata) -> Optional[torch.Tensor]:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def make_empty_intermediate_tensors(
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self, batch_size: int, dtype: torch.dtype,
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device: torch.device) -> IntermediateTensors:
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return IntermediateTensors({
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"hidden_states":
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torch.zeros((batch_size, self.config.hidden_size),
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dtype=dtype,
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device=device),
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"residual":
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torch.zeros((batch_size, self.config.hidden_size),
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dtype=dtype,
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device=device),
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})
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def sample(
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self,
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logits: Optional[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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new_weights = {}
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for n, p in weights:
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if n.endswith('.block_sparse_moe.input_linear.weight'):
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for e in range(p.size(0)):
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w1_name = n.replace(
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'.block_sparse_moe.input_linear.weight',
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f".block_sparse_moe.experts.{e}.w1.weight")
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w3_name = n.replace(
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'.block_sparse_moe.input_linear.weight',
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f".block_sparse_moe.experts.{e}.w3.weight")
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w1_param, w3_param = p[e].chunk(2, dim=0)
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assert w1_name not in new_weights
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assert w3_name not in new_weights
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new_weights[w1_name] = w1_param
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new_weights[w3_name] = w3_param
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elif n.endswith('.block_sparse_moe.output_linear.weight'):
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for e in range(p.size(0)):
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w2_name = n.replace(
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'.block_sparse_moe.output_linear.weight',
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f".block_sparse_moe.experts.{e}.w2.weight")
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w2_param = p[e]
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assert w2_name not in new_weights
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new_weights[w2_name] = w2_param
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elif n.endswith('.block_sparse_moe.router.layer.weight'):
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gate_name = n.replace('.block_sparse_moe.router.layer.weight',
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".block_sparse_moe.gate.weight")
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assert gate_name not in new_weights
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new_weights[gate_name] = p
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elif n == 'lm_head.weight' and self.config.tie_word_embeddings:
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pass
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else:
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new_weights[n] = p
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return mixtral.MixtralForCausalLM.load_weights(self,
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new_weights.items())
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@ -60,6 +60,7 @@ _TEXT_GENERATION_MODELS = {
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"GPTNeoXForCausalLM": ("gpt_neox", "GPTNeoXForCausalLM"),
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"GraniteForCausalLM": ("granite", "GraniteForCausalLM"),
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"GraniteMoeForCausalLM": ("granitemoe", "GraniteMoeForCausalLM"),
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"GraniteMoeSharedForCausalLM": ("granitemoeshared", "GraniteMoeSharedForCausalLM"), # noqa: E501
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"GritLM": ("gritlm", "GritLM"),
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"Grok1ModelForCausalLM": ("grok1", "Grok1ForCausalLM"),
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"InternLMForCausalLM": ("llama", "LlamaForCausalLM"),
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