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[Model] Adding Granite MoE. (#8206)

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Co-authored-by: Nick Hill <nickhill@us.ibm.com>
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Shawn Tan 2024-10-02 21:33:57 -04:00 committed by GitHub
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4 changed files with 492 additions and 3 deletions
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39
tests/models/decoder_only/language/test_granitemoe.py Normal file
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@ -0,0 +1,39 @@
"""Compare the outputs of HF and vLLM for Granite models using greedy sampling.
Run `pytest tests/models/test_granite.py`.
"""
import pytest
from ...utils import check_logprobs_close
MODELS = [
"ibm/PowerMoE-3b",
]
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(
hf_runner,
vllm_runner,
example_prompts,
model: str,
dtype: str,
max_tokens: int,
num_logprobs: int,
) -> None:
with hf_runner(model, dtype=dtype) as hf_model:
hf_outputs = hf_model.generate_greedy_logprobs_limit(
example_prompts, max_tokens, num_logprobs)
with vllm_runner(model, dtype=dtype) as vllm_model:
vllm_outputs = vllm_model.generate_greedy_logprobs(
example_prompts, max_tokens, num_logprobs)
check_logprobs_close(
outputs_0_lst=hf_outputs,
outputs_1_lst=vllm_outputs,
name_0="hf",
name_1="vllm",
)

1
vllm/model_executor/models/__init__.py
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@ -32,6 +32,7 @@ _GENERATION_MODELS = {
"GPTJForCausalLM": ("gpt_j", "GPTJForCausalLM"),
"GPTNeoXForCausalLM": ("gpt_neox", "GPTNeoXForCausalLM"),
"GraniteForCausalLM": ("granite", "GraniteForCausalLM"),
"GraniteMoeForCausalLM": ("granitemoe", "GraniteMoeForCausalLM"),
"InternLMForCausalLM": ("llama", "LlamaForCausalLM"),
"InternLM2ForCausalLM": ("internlm2", "InternLM2ForCausalLM"),
"JAISLMHeadModel": ("jais", "JAISLMHeadModel"),

7
vllm/model_executor/models/granite.py
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@ -404,9 +404,12 @@ class GraniteForCausalLM(nn.Module, SupportsLoRA):
self.lm_head.weight = self.model.embed_tokens.weight
logit_scale = getattr(config, "logit_scale", 1.0)
if hasattr(config, "logits_scaling"):
logit_scale /= config.logits_scaling
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
config.vocab_size,
logit_scale)
scale=logit_scale)
self.sampler = Sampler()
else:
self.lm_head = PPMissingLayer()
@ -428,8 +431,6 @@ class GraniteForCausalLM(nn.Module, SupportsLoRA):
sampling_metadata: SamplingMetadata) -> Optional[torch.Tensor]:
logits = self.logits_processor(self.lm_head, hidden_states,
sampling_metadata)
if logits is not None:
logits /= self.config.logits_scaling
return logits
def sample(

448
vllm/model_executor/models/granitemoe.py Normal file
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@ -0,0 +1,448 @@
# coding=utf-8
# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only GraniteMoe model."""
from typing import Iterable, List, Optional, Tuple
import torch
from torch import nn
from transformers.models.granitemoe import GraniteMoeConfig
from vllm.attention import Attention, AttentionMetadata
from vllm.config import CacheConfig, LoRAConfig
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (QKVParallelLinear,
ReplicatedLinear,
RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization.base_config import (
QuantizationConfig)
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
from vllm.model_executor.layers.vocab_parallel_embedding import (
DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from . import mixtral
from .interfaces import SupportsLoRA
from .utils import make_layers
class GraniteMoeMoE(nn.Module):
"""A tensor-parallel MoE implementation for GraniteMoe that shards each
expert across all ranks.
Each expert's weights are sharded across all ranks and a fused MoE
kernel is used for the forward pass, and finally we reduce the outputs
across ranks.
"""
def __init__(self,
num_experts: int,
top_k: int,
hidden_size: int,
intermediate_size: int,
params_dtype: Optional[torch.dtype] = None,
quant_config: Optional[QuantizationConfig] = None,
tp_size: Optional[int] = None,
prefix: str = ""):
super().__init__()
self.hidden_size = hidden_size
# Gate always runs at half / full precision for now.
self.gate = ReplicatedLinear(hidden_size,
num_experts,
bias=False,
params_dtype=params_dtype,
quant_config=None,
prefix=f"{prefix}.gate")
self.experts = FusedMoE(num_experts=num_experts,
top_k=top_k,
hidden_size=hidden_size,
intermediate_size=intermediate_size,
params_dtype=params_dtype,
reduce_results=True,
renormalize=True,
quant_config=quant_config,
tp_size=tp_size,
prefix=f"{prefix}.experts")
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# NOTE: hidden_states can have either 1D or 2D shape.
orig_shape = hidden_states.shape
hidden_states = hidden_states.view(-1, self.hidden_size)
# router_logits: (num_tokens, n_experts)
router_logits, _ = self.gate(hidden_states)
final_hidden_states = self.experts(hidden_states, router_logits)
return final_hidden_states.view(orig_shape)
class GraniteMoeAttention(nn.Module):
def __init__(
self,
hidden_size: int,
num_heads: int,
num_kv_heads: int,
max_position: int = 4096 * 32,
rope_theta: float = 10000,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
attention_multiplier: Optional[float] = None,
prefix: str = "",
) -> None:
super().__init__()
self.hidden_size = hidden_size
tp_size = get_tensor_model_parallel_world_size()
self.total_num_heads = num_heads
assert self.total_num_heads % tp_size == 0
self.num_heads = self.total_num_heads // tp_size
self.total_num_kv_heads = num_kv_heads
if self.total_num_kv_heads >= tp_size:
# Number of KV heads is greater than TP size, so we partition
# the KV heads across multiple tensor parallel GPUs.
assert self.total_num_kv_heads % tp_size == 0
else:
# Number of KV heads is less than TP size, so we replicate
# the KV heads across multiple tensor parallel GPUs.
assert tp_size % self.total_num_kv_heads == 0
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
self.head_dim = hidden_size // self.total_num_heads
self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim
self.scaling = (attention_multiplier if attention_multiplier
is not None else self.head_dim**-1)
self.rope_theta = rope_theta
self.qkv_proj = QKVParallelLinear(
hidden_size,
self.head_dim,
self.total_num_heads,
self.total_num_kv_heads,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.qkv_proj",
)
self.o_proj = RowParallelLinear(
self.total_num_heads * self.head_dim,
hidden_size,
bias=False,
quant_config=quant_config,
prefix=f"{prefix}.o_proj",
)
self.rotary_emb = get_rope(
self.head_dim,
rotary_dim=self.head_dim,
max_position=max_position,
base=int(self.rope_theta),
is_neox_style=True,
)
self.attn = Attention(self.num_heads,
self.head_dim,
self.scaling,
num_kv_heads=self.num_kv_heads,
cache_config=cache_config,
quant_config=quant_config)
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
kv_cache: torch.Tensor,
attn_metadata: AttentionMetadata,
) -> torch.Tensor:
qkv, _ = self.qkv_proj(hidden_states)
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
q, k = self.rotary_emb(positions, q, k)
attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
output, _ = self.o_proj(attn_output)
return output
class GraniteMoeDecoderLayer(nn.Module):
def __init__(
self,
config: GraniteMoeConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
) -> None:
super().__init__()
self.hidden_size = config.hidden_size
# Requires transformers > 4.32.0
rope_theta = getattr(config, "rope_theta", 10000)
self.self_attn = GraniteMoeAttention(
hidden_size=self.hidden_size,
num_heads=config.num_attention_heads,
max_position=config.max_position_embeddings,
num_kv_heads=config.num_key_value_heads,
rope_theta=rope_theta,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.self_attn",
attention_multiplier=config.attention_multiplier)
self.block_sparse_moe = GraniteMoeMoE(
num_experts=config.num_local_experts,
top_k=config.num_experts_per_tok,
hidden_size=config.hidden_size,
intermediate_size=config.intermediate_size,
quant_config=quant_config,
prefix=f"{prefix}.block_sparse_moe")
self.input_layernorm = RMSNorm(config.hidden_size,
eps=config.rms_norm_eps)
self.post_attention_layernorm = RMSNorm(config.hidden_size,
eps=config.rms_norm_eps)
self.residual_multiplier = config.residual_multiplier
def forward(
self,
positions: torch.Tensor,
hidden_states: torch.Tensor,
kv_cache: torch.Tensor,
attn_metadata: AttentionMetadata,
) -> torch.Tensor:
# Self Attention
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
hidden_states = self.self_attn(
positions=positions,
hidden_states=hidden_states,
kv_cache=kv_cache,
attn_metadata=attn_metadata,
)
hidden_states = residual + hidden_states * self.residual_multiplier
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.block_sparse_moe(hidden_states)
hidden_states = residual + hidden_states * self.residual_multiplier
return hidden_states
class GraniteMoeModel(nn.Module):
def __init__(
self,
config: GraniteMoeConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
lora_config: Optional[LoRAConfig] = None,
prefix: str = "",
) -> None:
super().__init__()
self.padding_idx = config.pad_token_id
lora_vocab = (lora_config.lora_extra_vocab_size *
(lora_config.max_loras or 1)) if lora_config else 0
self.vocab_size = config.vocab_size + lora_vocab
self.org_vocab_size = config.vocab_size
self.embed_tokens = VocabParallelEmbedding(
self.vocab_size,
config.hidden_size,
org_num_embeddings=config.vocab_size,
)
self.embedding_multiplier = config.embedding_multiplier
self.start_layer, self.end_layer, self.layers = make_layers(
config.num_hidden_layers,
lambda prefix: GraniteMoeDecoderLayer(
config, cache_config, quant_config=quant_config, prefix=prefix
),
prefix=f"{prefix}.layers")
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors],
) -> torch.Tensor:
if get_pp_group().is_first_rank:
hidden_states = self.embed_tokens(input_ids)
hidden_states *= self.embedding_multiplier
residual = None
else:
assert intermediate_tensors is not None
hidden_states = intermediate_tensors["hidden_states"]
residual = intermediate_tensors["residual"]
for i in range(self.start_layer, self.end_layer):
layer = self.layers[i]
hidden_states = layer(positions, hidden_states,
kv_caches[i - self.start_layer],
attn_metadata)
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
class GraniteMoeForCausalLM(nn.Module, SupportsLoRA):
fall_back_to_pt_during_load = False
packed_modules_mapping = {
"qkv_proj": [
"q_proj",
"k_proj",
"v_proj",
],
}
# LoRA specific attributes
supported_lora_modules = [
"qkv_proj",
"o_proj",
"embed_tokens",
"lm_head",
]
embedding_modules = {
"embed_tokens": "input_embeddings",
"lm_head": "output_embeddings",
}
embedding_padding_modules = ["lm_head"]
def __init__(
self,
config: GraniteMoeConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
lora_config: Optional[LoRAConfig] = None,
) -> None:
super().__init__()
self.config = config
self.lora_config = lora_config
self.model = GraniteMoeModel(config,
cache_config,
quant_config,
lora_config=lora_config,
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=quant_config,
)
if config.tie_word_embeddings:
self.lm_head.weight = self.model.embed_tokens.weight
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
config.vocab_size,
scale=1 /
self.config.logits_scaling)
self.sampler = Sampler()
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: List[torch.Tensor],
attn_metadata: AttentionMetadata,
intermediate_tensors: Optional[IntermediateTensors] = None,
) -> torch.Tensor:
hidden_states = self.model(input_ids, positions, kv_caches,
attn_metadata, intermediate_tensors)
return hidden_states
def compute_logits(
self, hidden_states: torch.Tensor,
sampling_metadata: SamplingMetadata) -> Optional[torch.Tensor]:
logits = self.logits_processor(self.lm_head, hidden_states,
sampling_metadata)
return logits
def make_empty_intermediate_tensors(
self, batch_size: int, dtype: torch.dtype,
device: torch.device) -> IntermediateTensors:
return IntermediateTensors({
"hidden_states":
torch.zeros((batch_size, self.config.hidden_size),
dtype=dtype,
device=device),
"residual":
torch.zeros((batch_size, self.config.hidden_size),
dtype=dtype,
device=device),
})
def sample(
self,
logits: Optional[torch.Tensor],
sampling_metadata: SamplingMetadata,
) -> Optional[SamplerOutput]:
next_tokens = self.sampler(logits, sampling_metadata)
return next_tokens
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
new_weights = {}
for n, p in weights:
if n.endswith('.block_sparse_moe.input_linear.weight'):
for e in range(p.size(0)):
w1_name = n.replace(
'.block_sparse_moe.input_linear.weight',
".block_sparse_moe.experts.%d.w1.weight" % e)
w3_name = n.replace(
'.block_sparse_moe.input_linear.weight',
".block_sparse_moe.experts.%d.w3.weight" % e)
w1_param, w3_param = p[e].chunk(2, dim=0)
assert w1_name not in new_weights
assert w3_name not in new_weights
new_weights[w1_name] = w1_param
new_weights[w3_name] = w3_param
elif n.endswith('.block_sparse_moe.output_linear.weight'):
for e in range(p.size(0)):
w2_name = n.replace(
'.block_sparse_moe.output_linear.weight',
".block_sparse_moe.experts.%d.w2.weight" % e)
w2_param = p[e]
assert w2_name not in new_weights
new_weights[w2_name] = w2_param
elif n.endswith('.block_sparse_moe.router.layer.weight'):
gate_name = n.replace('.block_sparse_moe.router.layer.weight',
".block_sparse_moe.gate.weight")
assert gate_name not in new_weights
new_weights[gate_name] = p
elif n == 'lm_head.weight' and self.config.tie_word_embeddings:
pass
else:
new_weights[n] = p
mixtral.MixtralForCausalLM.load_weights(self, new_weights.items())