Russell Bryant
e489ad7a21
- **Add SPDX license headers to python source files** - **Check for SPDX headers using pre-commit** commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745 Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:18:24 2025 -0500 Add SPDX license headers to python source files This commit adds SPDX license headers to python source files as recommended to the project by the Linux Foundation. These headers provide a concise way that is both human and machine readable for communicating license information for each source file. It helps avoid any ambiguity about the license of the code and can also be easily used by tools to help manage license compliance. The Linux Foundation runs license scans against the codebase to help ensure we are in compliance with the licenses of the code we use, including dependencies. Having these headers in place helps that tool do its job. More information can be found on the SPDX site: - https://spdx.dev/learn/handling-license-info/ Signed-off-by: Russell Bryant <rbryant@redhat.com> commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:36:32 2025 -0500 Check for SPDX headers using pre-commit Signed-off-by: Russell Bryant <rbryant@redhat.com> --------- Signed-off-by: Russell Bryant <rbryant@redhat.com>
352 lines
13 KiB
Python
352 lines
13 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# Adapted from https://huggingface.co/mosaicml/mpt-7b/tree/main
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import math
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from typing import Iterable, List, Optional, Set, Tuple, Union
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import torch
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import torch.nn as nn
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from vllm.attention import Attention, AttentionMetadata
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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_rank,
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get_tensor_model_parallel_world_size)
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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QKVParallelLinear,
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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 import 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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VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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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 vllm.transformers_utils.configs.mpt import MPTConfig
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from .interfaces import SupportsPP
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from .utils import (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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def _get_alibi_slopes(
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total_num_heads: int,
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alibi_bias_max: int,
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) -> torch.Tensor:
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next_power_of_2 = 2**math.ceil(math.log2(total_num_heads))
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m = torch.arange(1, next_power_of_2 + 1, dtype=torch.float32)
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m = m.mul(alibi_bias_max / next_power_of_2)
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slopes = 1.0 / torch.pow(2, m)
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if next_power_of_2 != total_num_heads:
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slopes = torch.concat([slopes[1::2], slopes[::2]])[:total_num_heads]
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return slopes
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class MPTAttention(nn.Module):
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def __init__(
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self,
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config: MPTConfig,
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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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):
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super().__init__()
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self.d_model = config.d_model
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self.total_num_heads = config.n_heads
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self.head_dim = self.d_model // self.total_num_heads
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self.clip_qkv = config.attn_config["clip_qkv"]
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self.qk_ln = config.attn_config["qk_ln"]
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self.alibi_bias_max = config.attn_config["alibi_bias_max"]
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if "kv_n_heads" in config.attn_config:
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self.total_num_kv_heads = config.attn_config['kv_n_heads']
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else:
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self.total_num_kv_heads = self.total_num_heads
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assert not config.attn_config["prefix_lm"]
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assert config.attn_config["alibi"]
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# pylint: disable=invalid-name
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self.Wqkv = QKVParallelLinear(
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self.d_model,
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self.d_model // self.total_num_heads,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=not config.no_bias,
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quant_config=quant_config,
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)
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if self.qk_ln:
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self.q_ln = nn.LayerNorm(self.d_model)
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self.k_ln = nn.LayerNorm(self.d_model)
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self.out_proj = RowParallelLinear(
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self.d_model,
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self.d_model,
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bias=not config.no_bias,
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quant_config=quant_config,
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)
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tp_world_size = get_tensor_model_parallel_world_size()
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assert self.total_num_heads % tp_world_size == 0
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self.num_heads = self.total_num_heads // tp_world_size
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if self.total_num_kv_heads >= tp_world_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_world_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_world_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_world_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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# Create the alibi slopes and slice them.
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tp_rank = get_tensor_model_parallel_rank()
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head_start = tp_rank * self.num_heads
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head_end = (tp_rank + 1) * self.num_heads
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alibi_slopes = _get_alibi_slopes(self.total_num_heads,
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self.alibi_bias_max)
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alibi_slopes = alibi_slopes[head_start:head_end].tolist()
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self.head_dim = self.d_model // self.total_num_heads
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scaling = self.head_dim**-0.5
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self.attn = Attention(self.num_heads,
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self.head_dim,
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scaling,
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alibi_slopes=alibi_slopes,
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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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def forward(
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self,
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position_ids: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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del position_ids # unused.
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qkv, _ = self.Wqkv(hidden_states)
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if self.clip_qkv is not None:
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qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
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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.qk_ln:
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q = self.q_ln(q)
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k = self.k_ln(k)
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attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
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output, _ = self.out_proj(attn_output)
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return output
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class MPTMLP(nn.Module):
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def __init__(
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self,
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config: MPTConfig,
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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hidden_size = config.d_model
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expansion_ratio = config.expansion_ratio
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intermediate_size = expansion_ratio * hidden_size
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self.up_proj = ColumnParallelLinear(
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hidden_size,
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intermediate_size,
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bias=not config.no_bias,
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quant_config=quant_config,
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)
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self.act = get_act_fn("gelu")
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=not config.no_bias,
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quant_config=quant_config,
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x, _ = self.up_proj(x)
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x = self.act(x)
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x, _ = self.down_proj(x)
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return x
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class MPTBlock(nn.Module):
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def __init__(
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self,
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config: MPTConfig,
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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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):
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super().__init__()
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hidden_size = config.d_model
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self.norm_1 = nn.LayerNorm(hidden_size)
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self.attn = MPTAttention(config,
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cache_config,
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quant_config,
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prefix=f"{prefix}.attn")
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self.norm_2 = nn.LayerNorm(hidden_size)
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self.ffn = MPTMLP(config, quant_config)
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def forward(
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self,
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position_ids: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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x = self.norm_1(hidden_states)
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x = self.attn(
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position_ids=position_ids,
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hidden_states=x,
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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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)
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hidden_states = hidden_states + x
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x = self.norm_2(hidden_states)
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x = self.ffn(x)
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hidden_states = hidden_states + x
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return hidden_states
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@support_torch_compile
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class MPTModel(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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assert config.embedding_fraction == 1.0
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assert config.norm_type == "low_precision_layernorm"
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self.wte = VocabParallelEmbedding(
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config.vocab_size,
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config.d_model,
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)
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self.start_layer, self.end_layer, self.blocks = make_layers(
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config.n_layers,
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lambda prefix: MPTBlock(
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config, cache_config, quant_config, prefix=prefix),
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prefix=f"{prefix}.blocks")
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self.norm_f = nn.LayerNorm(config.d_model)
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if config.no_bias:
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for module in self.modules():
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if hasattr(module, "bias") and isinstance(
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module.bias, nn.Parameter):
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# Remove the bias term in Linear and LayerNorm.
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module.register_parameter("bias", None)
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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(["hidden_states"],
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config.d_model))
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.wte(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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position_ids: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors],
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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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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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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for i in range(self.start_layer, self.end_layer):
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block = self.blocks[i]
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hidden_states = block(
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position_ids,
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hidden_states,
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kv_caches[i - self.start_layer],
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attn_metadata,
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)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({"hidden_states": hidden_states})
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hidden_states = self.norm_f(hidden_states)
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return hidden_states
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class MPTForCausalLM(nn.Module, SupportsPP):
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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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self.config = config
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assert config.tie_word_embeddings
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self.quant_config = quant_config
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self.transformer = MPTModel(vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "transformer"))
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self.lm_head = self.transformer.wte
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.sampler = get_sampler()
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self.make_empty_intermediate_tensors = (
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self.transformer.make_empty_intermediate_tensors)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.transformer.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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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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hidden_states = self.transformer(input_ids, positions, kv_caches,
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attn_metadata, 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,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> 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 sample(
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self,
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logits: 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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params_dict = dict(self.named_parameters(remove_duplicate=False))
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loaded_params: Set[str] = set()
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for name, loaded_weight in weights:
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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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if is_pp_missing_parameter(name, self):
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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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return loaded_params
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