support command-r (#369)
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ZhouXingg
364
python/sglang/srt/models/commandr.py
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364
python/sglang/srt/models/commandr.py
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# coding=utf-8
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# Copyright 2024 Cohere 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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# This file is based on the LLama model definition file in transformers
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"""PyTorch Cohere model."""
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from typing import List, Optional, Tuple
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn.parameter import Parameter
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from transformers import CohereConfig
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from vllm.model_executor.parallel_utils.parallel_state import (get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size)
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.linear import (LinearMethodBase,
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MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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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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VocabParallelEmbedding)
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from vllm.model_executor.utils import set_weight_attrs
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from vllm.model_executor.weight_utils import (default_weight_loader,
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hf_model_weights_iterator)
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.managers.router.model_runner import InputMetadata
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@torch.compile
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def layer_norm_func(hidden_states, weight, variance_epsilon):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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mean = hidden_states.mean(-1, keepdim=True)
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variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
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hidden_states = (hidden_states - mean) * torch.rsqrt(variance +
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variance_epsilon)
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hidden_states = weight.to(torch.float32) * hidden_states
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return hidden_states.to(input_dtype)
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class LayerNorm(nn.Module):
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def __init__(self, param_shape=None, eps=1e-5):
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super().__init__()
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self.weight = nn.Parameter(torch.ones(param_shape))
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self.variance_epsilon = eps
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set_weight_attrs(self.weight, {"weight_loader": self.weight_loader})
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def forward(self, hidden_states, residuals=None):
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hidden_states = layer_norm_func(hidden_states, self.weight,
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self.variance_epsilon)
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return hidden_states, residuals
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def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
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tp_rank = get_tensor_model_parallel_rank()
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shard_dim = 0 if param.dim() != 1 else None
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param_data = param.data
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if shard_dim is not None:
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shard_size = param_data.shape[shard_dim]
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start_idx = tp_rank * shard_size
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loaded_weight = loaded_weight.narrow(shard_dim, start_idx,
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shard_size)
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assert param_data.shape == loaded_weight.shape
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param_data.copy_(loaded_weight)
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# Copied from transformers.models.llama.modeling_llama.LlamaMLP Llama->Cohere
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class CohereMLP(nn.Module):
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def __init__(
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self,
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config,
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linear_method: Optional[LinearMethodBase] = None,
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):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_up_proj = MergedColumnParallelLinear(
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self.hidden_size,
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[self.intermediate_size] * 2,
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bias=False,
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linear_method=linear_method,
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)
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self.down_proj = RowParallelLinear(
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self.intermediate_size,
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self.hidden_size,
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bias=False,
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linear_method=linear_method,
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)
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self.act_fn = SiluAndMul()
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def forward(self, x):
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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class CohereAttention(nn.Module):
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def __init__(
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self,
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config: CohereConfig,
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layer_id: int = 0,
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linear_method: Optional[LinearMethodBase] = None,
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):
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super().__init__()
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tp_size = get_tensor_model_parallel_world_size()
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self.config = config
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self.attention_dropout = config.attention_dropout
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self.hidden_size = config.hidden_size
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self.total_num_heads = config.num_attention_heads
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self.num_heads = self.total_num_heads // tp_size
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self.head_dim = self.hidden_size // self.total_num_heads
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self.total_num_kv_heads = config.num_key_value_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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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.max_position_embeddings = getattr(
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config, "model_max_length", None) or getattr(
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config, "max_position_embeddings", 8192)
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self.rope_theta = config.rope_theta
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self.rope_scaling = getattr(config, "rope_scaling", None)
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self.use_qk_norm = getattr(config, "use_qk_norm", False)
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self.qkv_proj = QKVParallelLinear(
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self.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=False,
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linear_method=linear_method,
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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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self.hidden_size,
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bias=False,
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linear_method=linear_method,
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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=self.max_position_embeddings,
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base=self.rope_theta,
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rope_scaling=self.rope_scaling,
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is_neox_style=False,
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)
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self.attn = RadixAttention(
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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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layer_id=layer_id,
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)
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if self.use_qk_norm:
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self.q_norm = LayerNorm(param_shape=(self.num_heads,
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self.head_dim),
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eps=config.layer_norm_eps)
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self.k_norm = LayerNorm(param_shape=(self.num_kv_heads,
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self.head_dim),
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eps=config.layer_norm_eps)
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def _apply_qk_norm(self, q, k):
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q = q.view(*q.shape[:-1], -1, self.head_dim)
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k = k.view(*k.shape[:-1], -1, self.head_dim)
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q, _ = self.q_norm(q)
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k, _ = self.k_norm(k)
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q = q.view(*q.shape[:-2], -1)
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k = k.view(*k.shape[:-2], -1)
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return q, k
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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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input_metadata: InputMetadata,
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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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if self.use_qk_norm:
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q, k = self._apply_qk_norm(q, k)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v, input_metadata)
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output, _ = self.o_proj(attn_output)
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return output
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class CohereDecoderLayer(nn.Module):
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def __init__(self,
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config: CohereConfig,
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layer_id: int = 0,
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linear_method: Optional[LinearMethodBase] = None):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = CohereAttention(config, layer_id=layer_id, linear_method=linear_method)
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self.mlp = CohereMLP(config, linear_method=linear_method)
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self.input_layernorm = LayerNorm(param_shape=(config.hidden_size),
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eps=config.layer_norm_eps)
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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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input_metadata: InputMetadata,
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residual: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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# Self Attention
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residual = hidden_states
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hidden_states, residual = self.input_layernorm(hidden_states, residual)
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hidden_states_attention = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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input_metadata=input_metadata,
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)
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hidden_states_mlp = self.mlp(hidden_states)
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# Add everything together
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hidden_states = residual + hidden_states_attention + hidden_states_mlp
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return hidden_states, residual
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class CohereModel(nn.Module):
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def __init__(
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self,
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config: CohereConfig,
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linear_method: Optional[LinearMethodBase] = None,
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):
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super().__init__()
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self.config = config
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self.vocab_size = config.vocab_size
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self.embed_tokens = VocabParallelEmbedding(config.vocab_size,
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config.hidden_size)
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self.layers = nn.ModuleList([
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CohereDecoderLayer(config, i, linear_method=linear_method)
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for i in range(config.num_hidden_layers)
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])
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self.norm = LayerNorm(param_shape=(config.hidden_size),
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eps=config.layer_norm_eps)
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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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input_metadata: InputMetadata,
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) -> torch.Tensor:
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hidden_states = self.embed_tokens(input_ids)
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residual = None
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for i in range(len(self.layers)):
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layer = self.layers[i]
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hidden_states, residual = layer(
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positions,
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hidden_states,
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input_metadata,
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residual,
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)
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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class CohereForCausalLM(nn.Module):
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def __init__(
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self,
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config: CohereConfig,
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linear_method: Optional[LinearMethodBase] = None,
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) -> None:
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super().__init__()
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self.config = config
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self.linear_method = linear_method
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self.logits_processor = LogitsProcessor(config)
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self.model = CohereModel(config, linear_method)
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@torch.no_grad()
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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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input_metadata: InputMetadata,
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) -> torch.Tensor:
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hidden_states = self.model(input_ids, positions, input_metadata,)
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return self.logits_processor(
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input_ids, hidden_states, self.model.embed_tokens.weight, input_metadata
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)
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def load_weights(
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self,
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model_name_or_path: str,
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cache_dir: Optional[str] = None,
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load_format: str = "auto",
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revision: Optional[str] = None,
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):
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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loaded_params = set()
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for name, loaded_weight in hf_model_weights_iterator(
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model_name_or_path, cache_dir, load_format, revision):
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for param_name, shard_name, shard_id in stacked_params_mapping:
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if shard_name not in name:
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continue
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name = name.replace(shard_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# lm_head is not used in vllm as it is tied with embed_token.
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# To prevent errors, skip loading lm_head.weight.
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if "lm_head.weight" in name:
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continue
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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EntryClass = CohereForCausalLM
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