Mixtral8x7b with vLLM

vllm/model_executor/model_loader.py

@@ -33,6 +33,7 @@
     "LlamaForCausalLM": LlamaForCausalLM,
     "LLaMAForCausalLM": LlamaForCausalLM,  # For decapoda-research/llama-*
     "MistralForCausalLM": MistralForCausalLM,
+    "MixtralForCausalLM": MixtralForCausalLM,
     # transformers's mpt class has lower case
     "MptForCausalLM": MPTForCausalLM,
     "MPTForCausalLM": MPTForCausalLM,

vllm/model_executor/models/__init__.py

@@ -10,6 +10,7 @@
 from vllm.model_executor.models.internlm import InternLMForCausalLM
 from vllm.model_executor.models.llama import LlamaForCausalLM
 from vllm.model_executor.models.mistral import MistralForCausalLM
+from vllm.model_executor.models.mixtral import MixtralForCausalLM
 from vllm.model_executor.models.mpt import MPTForCausalLM
 from vllm.model_executor.models.opt import OPTForCausalLM
 from vllm.model_executor.models.phi_1_5 import PhiForCausalLM
@@ -35,5 +36,6 @@
     "PhiForCausalLM",
     "QWenLMHeadModel",
     "MistralForCausalLM",
+    "MixtralForCausalLM",
     "YiForCausalLM",
 ]

vllm/model_executor/models/mixtral.py

@@ -0,0 +1,373 @@
+# 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 Mistral model compatible with HuggingFace weights."""
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import MistralConfig
+
+import torch.nn.functional as F
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention import PagedAttention
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (LinearMethodBase,
+                                               ColumnParallelLinear,
+                                               MergedColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding, ParallelLMHead)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_world_size)
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.model_executor.weight_utils import (default_weight_loader,
+                                              hf_model_weights_iterator)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int
+    ):
+        """
+        Initialize the FeedForward module.
+        Args:
+            dim (int): Input dimension.
+            hidden_dim (int): Hidden dimension of the feedforward layer.
+            multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
+            ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None.
+        Attributes:
+            w1 (ColumnParallelLinear): Linear transformation for the first layer.
+            w2 (RowParallelLinear): Linear transformation for the second layer.
+            w3 (ColumnParallelLinear): Linear transformation for the third layer.
+        """
+        super().__init__()
+
+        self.w1 = ColumnParallelLinear(
+            hidden_size, intermediate_size, bias=False
+        )
+        self.w2 = RowParallelLinear(
+            intermediate_size, hidden_size, bias=False
+        )
+        self.w3 = ColumnParallelLinear(
+            hidden_size, intermediate_size, bias=False
+        )
+
+    def forward(self, x):
+        w1l, _ = self.w1(x)
+        w3l, _ = self.w3(x)
+        w2l, _ = self.w2(F.silu(w1l) * w3l)
+        return w2l
+
+
+
+class MoE(nn.Module):
+    def __init__(
+        self,
+        hidden_size,
+        intermediate_size,
+        num_experts: int = 8,
+        num_experts_per_token: int = 2
+    ):
+        super().__init__()
+        self.experts = nn.ModuleList([FeedForward(hidden_size, intermediate_size) for i in range(num_experts)])
+        self.gate = nn.Linear(hidden_size, num_experts, bias=False)
+        self.num_experts_per_token = num_experts_per_token
+
+    def forward(self, x):
+        orig_shape = x.shape
+        x = x.view(-1, x.shape[-1])
+
+        scores = self.gate(x)
+        expert_weights, expert_indices = torch.topk(scores, self.num_experts_per_token, dim=-1)
+        expert_weights = expert_weights.softmax(dim=-1)
+        flat_expert_indices = expert_indices.view(-1)
+
+        x = x.repeat_interleave(self.num_experts_per_token, dim=0)
+        y = torch.empty_like(x)
+        for i, expert in enumerate(self.experts):
+            y[flat_expert_indices == i] = expert(x[flat_expert_indices == i])
+        y = (y.view(*expert_weights.shape, -1) * expert_weights.unsqueeze(-1)).sum(dim=1)
+        return y.view(*orig_shape)
+
+
+
+class MistralAttention(nn.Module):
+
+    def __init__(self,
+                 hidden_size: int,
+                 num_heads: int,
+                 num_kv_heads: int,
+                 max_position: int = 4096 * 32,
+                 rope_theta: float = 10000,
+                 linear_method: Optional[LinearMethodBase] = None,
+                 sliding_window: Optional[int] = None) -> 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 = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.sliding_window = sliding_window
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size,
+            self.head_dim,
+            self.total_num_heads,
+            self.total_num_kv_heads,
+            bias=False,
+            linear_method=linear_method,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            linear_method=linear_method,
+        )
+
+        self.rotary_emb = get_rope(
+            self.head_dim,
+            rotary_dim=self.head_dim,
+            max_position=max_position,
+            base=self.rope_theta,
+        )
+        self.attn = PagedAttention(self.num_heads,
+                                   self.head_dim,
+                                   self.scaling,
+                                   num_kv_heads=self.num_kv_heads,
+                                   sliding_window=self.sliding_window)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> 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)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata,
+                                cache_event)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class MixtralDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: MistralConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        # Requires transformers > 4.32.0
+        rope_theta = getattr(config, "rope_theta", 10000)
+        self.self_attn = MistralAttention(
+            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,
+            linear_method=linear_method,
+            sliding_window=config.sliding_window)
+        self.mlp = MoE(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            num_experts=config.num_experts,
+            num_experts_per_token=config.num_experts_per_token
+        )
+        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)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+        residual: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        if residual is None:
+            residual = hidden_states
+            hidden_states = self.input_layernorm(hidden_states)
+        else:
+            hidden_states, residual = self.input_layernorm(
+                hidden_states, residual)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+            cache_event=cache_event,
+        )
+
+        # Fully Connected
+        hidden_states, residual = self.post_attention_layernorm(
+            hidden_states, residual)
+        hidden_states = self.mlp(hidden_states)
+        return hidden_states, residual
+
+
+class MixtralModel(nn.Module):
+
+    def __init__(
+        self,
+        config: MistralConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+        )
+        self.layers = nn.ModuleList([
+            MixtralDecoderLayer(config, linear_method)
+            for _ in range(config.num_hidden_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[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        residual = None
+        for i in range(len(self.layers)):
+            cache_event = None if cache_events is None else cache_events[i]
+            layer = self.layers[i]
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                cache_event,
+                residual,
+            )
+        hidden_states, _ = self.norm(hidden_states, residual)
+        return hidden_states
+
+
+class MixtralForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: MistralConfig,
+        linear_method: Optional[LinearMethodBase] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.linear_method = linear_method
+        self.model = MixtralModel(config, linear_method)
+        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> torch.Tensor:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata, cache_events)
+        return hidden_states
+
+    def sample(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> SamplerOutput:
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   sampling_metadata)
+        return next_tokens
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        params_dict = dict(self.named_parameters())
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                param = params_dict[name.replace(weight_name, param_name)]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)