mirror of
https://github.com/kvcache-ai/ktransformers.git
synced 2026-05-05 23:50:14 +00:00
57d14d22bc
* refactor: move legacy code to archive/ directory - Moved ktransformers, csrc, third_party, merge_tensors to archive/ - Moved build scripts and configurations to archive/ - Kept kt-kernel, KT-SFT, doc, and README files in root - Preserved complete git history for all moved files * refactor: restructure repository to focus on kt-kernel and KT-SFT modules * fix README * fix README * fix README * fix README * docs: add performance benchmarks to kt-kernel section Add comprehensive performance data for kt-kernel to match KT-SFT's presentation: - AMX kernel optimization: 21.3 TFLOPS (3.9× faster than PyTorch) - Prefill phase: up to 20× speedup vs baseline - Decode phase: up to 4× speedup - NUMA optimization: up to 63% throughput improvement - Multi-GPU (8×L20): 227.85 tokens/s total throughput with DeepSeek-R1 FP8 Source: https://lmsys.org/blog/2025-10-22-KTransformers/ This provides users with concrete performance metrics for both core modules, making it easier to understand the capabilities of each component. * refactor: improve kt-kernel performance data with specific hardware and models Replace generic performance descriptions with concrete benchmarks: - Specify exact hardware: 8×L20 GPU + Xeon Gold 6454S, Single/Dual-socket Xeon + AMX - Include specific models: DeepSeek-R1-0528 (FP8), DeepSeek-V3 (671B) - Show detailed metrics: total throughput, output throughput, concurrency details - Match KT-SFT presentation style for consistency This provides users with actionable performance data they can use to evaluate hardware requirements and expected performance for their use cases. * fix README * docs: clean up performance table and improve formatting * add pic for README * refactor: simplify .gitmodules and backup legacy submodules - Remove 7 legacy submodules from root .gitmodules (archive/third_party/*) - Keep only 2 active submodules for kt-kernel (llama.cpp, pybind11) - Backup complete .gitmodules to archive/.gitmodules - Add documentation in archive/README.md for researchers who need legacy submodules This reduces initial clone size by ~500MB and avoids downloading unused dependencies. * refactor: move doc/ back to root directory Keep documentation in root for easier access and maintenance. * refactor: consolidate all images to doc/assets/ - Move kt-kernel/assets/heterogeneous_computing.png to doc/assets/ - Remove KT-SFT/assets/ (images already in doc/assets/) - Update KT-SFT/README.md image references to ../doc/assets/ - Eliminates ~7.9MB image duplication - Centralizes all documentation assets in one location * fix pic path for README
530 lines
No EOL
19 KiB
Python
530 lines
No EOL
19 KiB
Python
"""
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Description :
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Author : Boxin Zhang
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Version : 0.1.0
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Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
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"""
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from torch import nn
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from transformers import ROPE_INIT_FUNCTIONS
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from ktransformers.models.modeling_llama import (
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LlamaRotaryEmbedding,
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LlamaLinearScalingRotaryEmbedding,
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LlamaDynamicNTKScalingRotaryEmbedding,
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)
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from ktransformers.models.modeling_deepseek_v3 import (
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DeepseekV3RotaryEmbedding
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)
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from ktransformers.models.modeling_deepseek import (
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DeepseekV2YarnRotaryEmbedding,
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DeepseekV2RotaryEmbedding,
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yarn_get_mscale,
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yarn_linear_ramp_mask,
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yarn_find_correction_range
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)
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from ktransformers.operators.base_operator import BaseInjectedModule
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from ktransformers.util.custom_loader import GGUFLoader
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from ktransformers.util.utils import InferenceState
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from transformers.configuration_utils import PretrainedConfig
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from ktransformers.models.modeling_smallthinker import SmallthinkerRotaryEmbedding
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from ktransformers.models.modeling_glm4_moe import Glm4MoeRotaryEmbedding
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import torch
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# Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->Qwen2Moe
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class RotaryEmbedding(BaseInjectedModule, DeepseekV2RotaryEmbedding):
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def __init__(
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self,
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key: str,
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gguf_loader: GGUFLoader,
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config: PretrainedConfig,
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orig_module: nn.Module,
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# device: str = "cuda",
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generate_device: str = "cuda",
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prefill_device: str = "cuda",
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**kwargs,
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):
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BaseInjectedModule.__init__(
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self, key, gguf_loader, config, orig_module, prefill_device, generate_device, **kwargs
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)
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self.orig_module.__init__(
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orig_module.dim, orig_module.max_position_embeddings, orig_module.base
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)
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self.generate_device = generate_device
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self.prefill_device = prefill_device
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def load(self):
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self.orig_module.__init__(
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self.orig_module.dim,
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self.orig_module.max_position_embeddings,
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self.orig_module.base,
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self.device,
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)
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class RotaryEmbeddingV3(BaseInjectedModule):
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def __init__(
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self,
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key: str,
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gguf_loader: GGUFLoader,
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config: PretrainedConfig,
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orig_module: nn.Module,
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# device: str = "cuda",
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generate_device: str = "cuda",
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prefill_device: str = "cuda",
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**kwargs,
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):
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BaseInjectedModule.__init__(
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self, key, gguf_loader, config, orig_module, prefill_device, generate_device, **kwargs
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)
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self.generate_device = generate_device
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self.prefill_device = prefill_device
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@torch.no_grad()
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def forward(self, x, position_ids):
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# x: [bs, num_attention_heads, seq_len, head_size]
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inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
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position_ids_expanded = position_ids[:, None, :].float()
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# Force float32 since bfloat16 loses precision on long contexts
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# See https://github.com/huggingface/transformers/pull/29285
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device_type = x.device.type
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device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
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with torch.autocast(device_type=device_type, enabled=False):
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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emb = torch.cat((freqs, freqs), dim=-1)
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cos = emb.cos()
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sin = emb.sin()
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return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
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def load(self):
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self._init(
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dim=self.config.qk_rope_head_dim,
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max_position_embeddings=self.config.max_position_embeddings,
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base=self.config.rope_theta,
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device=self.device,
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)
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def _init(self, dim, max_position_embeddings, base, device, scaling_factor=1.0):
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self.scaling_factor = scaling_factor
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self.dim = dim
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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self.inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
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# self.register_buffer("inv_freq", inv_freq, persistent=False)
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# For BC we register cos and sin cached
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self.max_seq_len_cached = max_position_embeddings
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class RotaryEmbeddingV2(BaseInjectedModule, LlamaRotaryEmbedding):
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def __init__(
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self,
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key: str,
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gguf_loader: GGUFLoader,
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config: PretrainedConfig,
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orig_module: nn.Module,
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generate_device: str = "cuda",
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prefill_device: str = "cuda",
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**kwargs,
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):
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BaseInjectedModule.__init__(
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self, key, gguf_loader, config, orig_module, prefill_device, generate_device, **kwargs
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)
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self.orig_module.__init__(
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orig_module.dim,
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orig_module.max_position_embeddings,
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orig_module.base,
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None,
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orig_module.scaling_factor,
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orig_module.rope_type,
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orig_module.config,
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)
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self.generate_device = generate_device
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self.prefill_device = prefill_device
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def load(self):
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self.orig_module.__init__(
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self.orig_module.dim,
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self.orig_module.max_position_embeddings,
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self.orig_module.base,
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self.device,
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self.orig_module.scaling_factor,
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self.orig_module.rope_type,
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self.orig_module.config,
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)
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class YarnRotaryEmbedding(BaseInjectedModule, DeepseekV2YarnRotaryEmbedding):
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def __init__(
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self,
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key: str,
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gguf_loader: GGUFLoader,
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config: PretrainedConfig,
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orig_module: nn.Module,
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# device: str = "cuda",
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generate_device: str = "cuda",
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prefill_device: str = "cuda",
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**kwargs,
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):
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BaseInjectedModule.__init__(
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self, key, gguf_loader, config, orig_module, prefill_device, generate_device, **kwargs
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)
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self.orig_module.__init__(
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orig_module.dim,
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orig_module.max_position_embeddings,
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orig_module.base,
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None, # device
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orig_module.scaling_factor,
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orig_module.original_max_position_embeddings,
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orig_module.beta_fast,
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orig_module.beta_slow,
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orig_module.mscale,
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orig_module.mscale_all_dim,
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)
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self.generate_device = generate_device
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self.prefill_device = prefill_device
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def load(self):
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self.orig_module.__init__(
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self.orig_module.dim,
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self.orig_module.max_position_embeddings,
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self.orig_module.base,
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self.generate_device,
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self.orig_module.scaling_factor,
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self.orig_module.original_max_position_embeddings,
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self.orig_module.beta_fast,
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self.orig_module.beta_slow,
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self.orig_module.mscale,
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self.orig_module.mscale_all_dim,
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)
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# class DeepSeekV3YarnRotaryEmbedding(BaseInjectedModule, DeepseekV3RotaryEmbedding):
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# def __init__(
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# self,
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# key: str,
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# gguf_loader: GGUFLoader,
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# config: PretrainedConfig,
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# orig_module: nn.Module,
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# # device: str = "cuda",
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# generate_device: str = "cuda",
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# prefill_device: str = "cuda",
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# **kwargs,
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# ):
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# BaseInjectedModule.__init__(
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# self, key, gguf_loader, config, orig_module, prefill_device, generate_device, **kwargs
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# )
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# self.generate_device = generate_device
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# self.prefill_device = prefill_device
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# def load(self):
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# # TODO support perlayer prefill
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# self.orig_module.__init__(
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# self.config,
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# device=self.generate_device
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# )
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# return
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class YarnRotaryEmbeddingV3(BaseInjectedModule):
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def __init__(
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self,
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key: str,
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gguf_loader: GGUFLoader,
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config: PretrainedConfig,
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orig_module: nn.Module,
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# device: str = "cuda",
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generate_device: str = "cuda",
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prefill_device: str = "cuda",
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**kwargs,
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):
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BaseInjectedModule.__init__(
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self, key, gguf_loader, config, orig_module, prefill_device, generate_device, **kwargs
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)
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self.generate_device = generate_device
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self.prefill_device = prefill_device
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def load(self):
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kwargs = {
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key: self.config.rope_scaling[key]
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for key in [
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"original_max_position_embeddings",
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"beta_fast",
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"beta_slow",
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"mscale",
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"mscale_all_dim",
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]
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if key in self.config.rope_scaling
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}
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self._init(
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dim=self.config.qk_rope_head_dim,
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max_position_embeddings=self.config.max_position_embeddings,
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base=self.config.rope_theta,
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device=self.device,
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scaling_factor=self.config.rope_scaling["factor"],
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**kwargs,
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)
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@torch.no_grad()
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def forward(self, x, position_ids):
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# x: [bs, num_attention_heads, seq_len, head_size]
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inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
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position_ids_expanded = position_ids[:, None, :].float()
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# Force float32 since bfloat16 loses precision on long contexts
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# See https://github.com/huggingface/transformers/pull/29285
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device_type = x.device.type
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device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
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with torch.autocast(device_type=device_type, enabled=False):
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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emb = torch.cat((freqs, freqs), dim=-1)
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cos = emb.cos()* self._mscale
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sin = emb.sin()* self._mscale
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return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
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def _init(
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self,
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dim,
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max_position_embeddings=2048,
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base=10000,
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device=None,
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scaling_factor=1.0,
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original_max_position_embeddings=4096,
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beta_fast=32,
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beta_slow=1,
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mscale=1,
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mscale_all_dim=0,
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):
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self.original_max_position_embeddings = original_max_position_embeddings
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self.beta_fast = beta_fast
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self.beta_slow = beta_slow
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self.mscale = mscale
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self.mscale_all_dim = mscale_all_dim
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self.scaling_factor = scaling_factor
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self.dim = dim
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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freq_extra = 1.0 / (
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self.base
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** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
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)
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freq_inter = 1.0 / (
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self.scaling_factor
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* self.base
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** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
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)
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low, high = yarn_find_correction_range(
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self.beta_fast,
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self.beta_slow,
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dim,
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self.base,
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self.original_max_position_embeddings,
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)
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inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to(
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device=device, dtype=torch.float32
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)
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self.inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask
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self._mscale = float(
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yarn_get_mscale(self.scaling_factor, self.mscale)
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/ yarn_get_mscale(self.scaling_factor, self.mscale_all_dim)
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)
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# For BC we register cos and sin cached
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self.max_seq_len_cached = max_position_embeddings
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class DynamicNTKScalingRotaryEmbedding(
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BaseInjectedModule, LlamaDynamicNTKScalingRotaryEmbedding
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):
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def __init__(
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self,
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key: str,
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gguf_loader: GGUFLoader,
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config: PretrainedConfig,
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orig_module: nn.Module,
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prefill_device: str = "cuda",
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generate_device: str = "cuda",
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**kwargs,
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):
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BaseInjectedModule.__init__(
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self, key, gguf_loader, config, orig_module, prefill_device, generate_device, **kwargs
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)
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self.orig_module.__init__(
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orig_module.dim,
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orig_module.max_position_embeddings,
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orig_module.base,
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None, # device
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orig_module.scaling_factor,
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orig_module.rope_type,
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orig_module.config,
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)
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def load(self):
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self.orig_module.__init__(
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self.orig_module.dim,
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self.orig_module.max_position_embeddings,
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self.orig_module.base,
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self.orig_module.device,
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self.orig_module.scaling_factor,
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self.orig_module.rope_type,
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self.orig_module.config,
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)
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class RotaryEmbeddingV4(BaseInjectedModule):
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def __init__(
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self,
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key: str,
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gguf_loader: GGUFLoader,
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config: PretrainedConfig,
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orig_module: nn.Module,
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# device: str = "cuda",
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generate_device: str = "cuda",
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prefill_device: str = "cuda",
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**kwargs,
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):
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BaseInjectedModule.__init__(
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self, key, gguf_loader, config, orig_module, generate_device, **kwargs
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)
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|
self.generate_device = generate_device
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self.prefill_device = prefill_device
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|
@torch.no_grad()
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def forward(self, x, position_ids):
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# x: [bs, num_attention_heads, seq_len, head_size]
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inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
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position_ids_expanded = position_ids[:, None, :].float()
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# Force float32 since bfloat16 loses precision on long contexts
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|
# See https://github.com/huggingface/transformers/pull/29285
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device_type = x.device.type
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device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
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with torch.autocast(device_type=device_type, enabled=False):
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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emb = torch.cat((freqs, freqs), dim=-1)
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cos = emb.cos()
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sin = emb.sin()
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return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
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def load(self):
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|
self._init(
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dim=self.config.qk_rope_head_dim,
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max_position_embeddings=self.config.max_position_embeddings,
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base=self.config.rope_theta,
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device=self.device,
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)
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def _init(self, dim, max_position_embeddings, base, device, scaling_factor=1.0):
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|
self.scaling_factor = scaling_factor
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|
self.dim = dim
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self.max_position_embeddings = max_position_embeddings
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self.base = base
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self.inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
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# self.register_buffer("inv_freq", inv_freq, persistent=False)
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# For BC we register cos and sin cached
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|
self.max_seq_len_cached = max_position_embeddings
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|
|
class KQwen3MoeRotaryEmbedding(BaseInjectedModule, DeepseekV2RotaryEmbedding):
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|
def __init__(
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|
self,
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|
key: str,
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|
gguf_loader: GGUFLoader,
|
|
config: PretrainedConfig,
|
|
orig_module: nn.Module,
|
|
# device: str = "cuda",
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|
generate_device: str = "cuda",
|
|
prefill_device: str = "cuda",
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|
**kwargs,
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|
):
|
|
BaseInjectedModule.__init__(
|
|
self, key, gguf_loader, config, orig_module, prefill_device, generate_device, **kwargs
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|
)
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|
self.orig_module.__init__(
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|
config,
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|
)
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|
self.generate_device = generate_device
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|
self.prefill_device = prefill_device
|
|
|
|
def load(self):
|
|
self.orig_module.__init__(
|
|
self.orig_module.config
|
|
)
|
|
|
|
|
|
class KSmallthinkerRotaryEmbedding(BaseInjectedModule, SmallthinkerRotaryEmbedding):
|
|
def __init__(
|
|
self,
|
|
key: str,
|
|
gguf_loader: GGUFLoader,
|
|
config: PretrainedConfig,
|
|
orig_module: nn.Module,
|
|
# device: str = "cuda",
|
|
generate_device: str = "cuda",
|
|
prefill_device: str = "cuda",
|
|
**kwargs,
|
|
):
|
|
BaseInjectedModule.__init__(
|
|
self, key, gguf_loader, config, orig_module, prefill_device, generate_device, **kwargs
|
|
)
|
|
self.orig_module.__init__(
|
|
config
|
|
)
|
|
self.generate_device = generate_device
|
|
self.prefill_device = prefill_device
|
|
|
|
def load(self):
|
|
self.orig_module.__init__(
|
|
self.orig_module.config,
|
|
device = self.generate_device,
|
|
)
|
|
|
|
@torch.no_grad()
|
|
def forward(self, x, position_ids):
|
|
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
|
|
position_ids_expanded = position_ids[:, None, :].float()
|
|
|
|
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
|
|
with torch.autocast(device_type=device_type, enabled=False): # Force float32
|
|
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
|
emb = torch.cat((freqs, freqs), dim=-1)
|
|
cos = emb.cos() * self.attention_scaling
|
|
sin = emb.sin() * self.attention_scaling
|
|
|
|
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
|
|
|
class KGlm4MoeRotaryEmbedding(BaseInjectedModule, Glm4MoeRotaryEmbedding):
|
|
def __init__(
|
|
self,
|
|
key: str,
|
|
gguf_loader: GGUFLoader,
|
|
config: PretrainedConfig,
|
|
orig_module: nn.Module,
|
|
# device: str = "cuda",
|
|
generate_device: str = "cuda",
|
|
prefill_device: str = "cuda",
|
|
**kwargs,
|
|
):
|
|
BaseInjectedModule.__init__(
|
|
self, key, gguf_loader, config, orig_module, prefill_device, generate_device, **kwargs
|
|
)
|
|
self.orig_module.__init__(
|
|
config
|
|
)
|
|
self.generate_device = generate_device
|
|
self.prefill_device = prefill_device
|
|
|
|
def load(self):
|
|
self.orig_module.__init__(
|
|
self.orig_module.config,
|
|
device = self.generate_device,
|
|
)
|
|
|
|
@torch.no_grad()
|
|
def forward(self, x, position_ids):
|
|
if "dynamic" in self.rope_type:
|
|
self._dynamic_frequency_update(position_ids, device=x.device)
|
|
# Core RoPE block
|
|
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
|
|
# print(inv_freq_expanded.device)
|
|
position_ids_expanded = position_ids[:, None, :].float()
|
|
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
|
|
device_type = x.device.type
|
|
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
|
|
with torch.autocast(device_type=device_type, enabled=False):
|
|
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
|
emb = torch.cat((freqs, freqs), dim=-1)
|
|
cos = emb.cos() * self.attention_scaling
|
|
sin = emb.sin() * self.attention_scaling
|
|
|
|
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) |