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  1. Dockerfile +30 -0
  2. LICENSE.md +13 -0
  3. README.md +101 -18
  4. protein_lm/__init__.py +0 -0
  5. protein_lm/configs/train/base.yaml +43 -0
  6. protein_lm/modeling/__init__.py +0 -0
  7. protein_lm/modeling/getters/__init__.py +0 -0
  8. protein_lm/modeling/getters/collate.py +84 -0
  9. protein_lm/modeling/getters/dataset.py +16 -0
  10. protein_lm/modeling/getters/log.py +12 -0
  11. protein_lm/modeling/getters/mask.py +54 -0
  12. protein_lm/modeling/getters/ptm_dataset.py +212 -0
  13. protein_lm/modeling/getters/scheduler.py +198 -0
  14. protein_lm/modeling/getters/training_args.py +14 -0
  15. protein_lm/modeling/getters/uniref_dataset.py +59 -0
  16. protein_lm/modeling/models/__init__.py +0 -0
  17. protein_lm/modeling/models/libs/causal-conv1d/AUTHORS +1 -0
  18. protein_lm/modeling/models/libs/causal-conv1d/LICENSE +29 -0
  19. protein_lm/modeling/models/libs/causal-conv1d/README.md +1 -0
  20. protein_lm/modeling/models/libs/causal-conv1d/causal_conv1d/__init__.py +3 -0
  21. protein_lm/modeling/models/libs/causal-conv1d/causal_conv1d/causal_conv1d_interface.py +104 -0
  22. protein_lm/modeling/models/libs/causal-conv1d/csrc/causal_conv1d.cpp +333 -0
  23. protein_lm/modeling/models/libs/causal-conv1d/csrc/causal_conv1d.h +53 -0
  24. protein_lm/modeling/models/libs/causal-conv1d/csrc/causal_conv1d_bwd.cu +525 -0
  25. protein_lm/modeling/models/libs/causal-conv1d/csrc/causal_conv1d_common.h +64 -0
  26. protein_lm/modeling/models/libs/causal-conv1d/csrc/causal_conv1d_fwd.cu +350 -0
  27. protein_lm/modeling/models/libs/causal-conv1d/csrc/causal_conv1d_update.cu +96 -0
  28. protein_lm/modeling/models/libs/causal-conv1d/csrc/static_switch.h +25 -0
  29. protein_lm/modeling/models/libs/causal-conv1d/setup.py +264 -0
  30. protein_lm/modeling/models/libs/causal-conv1d/tests/test_causal_conv1d.py +173 -0
  31. protein_lm/modeling/models/libs/mamba/.gitmodules +3 -0
  32. protein_lm/modeling/models/libs/mamba/AUTHORS +2 -0
  33. protein_lm/modeling/models/libs/mamba/LICENSE +201 -0
  34. protein_lm/modeling/models/libs/mamba/README.md +149 -0
  35. protein_lm/modeling/models/libs/mamba/assets/selection.png +0 -0
  36. protein_lm/modeling/models/libs/mamba/benchmarks/benchmark_generation_mamba_simple.py +88 -0
  37. protein_lm/modeling/models/libs/mamba/csrc/selective_scan/reverse_scan.cuh +401 -0
  38. protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan.cpp +497 -0
  39. protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan.h +101 -0
  40. protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_bwd_bf16_complex.cu +9 -0
  41. protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_bwd_bf16_real.cu +9 -0
  42. protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_bwd_fp16_complex.cu +9 -0
  43. protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_bwd_fp16_real.cu +9 -0
  44. protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_bwd_fp32_complex.cu +9 -0
  45. protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_bwd_fp32_real.cu +9 -0
  46. protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_bwd_kernel.cuh +531 -0
  47. protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_common.h +221 -0
  48. protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_fwd_bf16.cu +10 -0
  49. protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_fwd_fp16.cu +10 -0
  50. protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_fwd_fp32.cu +10 -0
Dockerfile ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Use the specified base image
2
+ FROM nvcr.io/nvidia/pytorch:23.12-py3
3
+
4
+ # Set the working directory to your project directory
5
+ WORKDIR ./
6
+
7
+ # Copy the contents of your project into the Docker image
8
+ COPY . .
9
+
10
+ # Create and activate Conda environment
11
+ # RUN conda create --name plm python=3.10
12
+ # SHELL ["conda", "run", "-n", "plm", "/bin/bash", "-c"]
13
+ # RUN conda activate plm
14
+
15
+ # Install Miniconda
16
+ # RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh && \
17
+ # /bin/bash miniconda.sh -b -p /opt/conda && \
18
+ # rm miniconda.sh
19
+ # ENV PATH="/opt/conda/bin:${PATH}"
20
+
21
+ # Install dependencies
22
+ # RUN cd protein_lm/modeling/models/libs/ && pip install -e causal-conv1d && pip install -e mamba && cd ../../../../
23
+ # RUN pip install transformers datasets accelerate evaluate pytest fair-esm biopython deepspeed
24
+ # RUN pip install -e .
25
+ # RUN pip install hydra-core --upgrade
26
+ # RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
27
+ # source "$HOME/.cargo/env"
28
+ # RUN pip install -e protein_lm/tokenizer/rust_trie
29
+
30
+
LICENSE.md ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Copyright 2023 the authors
2
+
3
+ Licensed under the Apache License, Version 2.0 (the "License");
4
+ you may not use this file except in compliance with the License.
5
+ You may obtain a copy of the License at
6
+
7
+ http://www.apache.org/licenses/LICENSE-2.0
8
+
9
+ Unless required by applicable law or agreed to in writing, software
10
+ distributed under the License is distributed on an "AS IS" BASIS,
11
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12
+ See the License for the specific language governing permissions and
13
+ limitations under the License.
README.md CHANGED
@@ -1,18 +1,101 @@
1
- ---
2
- license: cc-by-nc-nd-4.0
3
- extra_gated_prompt: "You agree to share any computational and experimental results (along with any generated sequences) from this model's use with the authors prior to publication or dissemination (pranam.chatterjee@duke.edu)."
4
- extra_gated_fields:
5
- Name: text
6
- Company: text
7
- Country: country
8
- Specific date: date_picker
9
- I want to use this model for:
10
- type: select
11
- options:
12
- - Research
13
- - Education
14
- - label: Other
15
- value: other
16
- I agree not to file patents on any sequences generated by this model: checkbox
17
- I agree to use this model for non-commercial use ONLY: checkbox
18
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # PTM-Mamba
2
+
3
+ A PTM-Aware Protein Language Model with Bidirectional Gated Mamba Blocks.
4
+
5
+ ## Install Enviroment
6
+
7
+ ## Docker
8
+
9
+ Setting up env for mamba could be a pain, alternatively we suggest using docker containers.
10
+
11
+ #### Run container in interactive and detach mode, and mounte project dir to the container workspace.
12
+
13
+ ```
14
+ docker run --gpus all -v $(pwd):/workspace -d -it --name plm_benji nvcr.io/nvidia/pytorch:23.12-py3 /bin/bash && docker attach plm_benji
15
+ ```
16
+
17
+ #### Install pkgs in container
18
+
19
+ ```
20
+ mkdir /root/.cache/torch/hub/checkpoints/ -p; wget -O /root/.cache/torch/hub/checkpoints/esm2_t33_650M_UR50D.pt https://dl.fbaipublicfiles.com/fair-esm/models/esm2_t33_650M_UR50D.pt
21
+ cd protein_lm/modeling/models/libs/ && pip install -e causal-conv1d && pip install -e mamba && cd ../../../../
22
+ pip install transformers datasets accelerate evaluate pytest fair-esm biopython deepspeed wandb
23
+ pip install torch_geometric
24
+ pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cu117.html
25
+ pip install -e .
26
+ pip install hydra-core --upgrade
27
+ curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
28
+ source "$HOME/.cargo/env"
29
+ pip install -e protein_lm/tokenizer/rust_trie
30
+
31
+
32
+ ```
33
+
34
+ ## Data
35
+
36
+ We collect protein sequences and their PTM annotations from Uniprot-Swissprot. The PTM annotations are represented as tokens and used to replaced the corresponding amino acids. The data can be downloaded from [here](https://drive.google.com/file/d/151KUp79tgBxphoIky1-ohyuvzIS1gtNS/view?usp=drive_link). Please place the data on `protein_lm/dataset/`.
37
+
38
+ ## Configs
39
+
40
+ The training and testing configs are `protein_lm/configs`. We provide a basic training config at `protein_lm/configs/train/base.yaml`.
41
+
42
+ ## Training
43
+
44
+ ##### Single-GPU Training
45
+
46
+ ```
47
+ python ./protein_lm/modeling/scripts/train.py +train=base
48
+ ```
49
+
50
+ The commond will use the configs in `protein_lm/configs/train/base.yaml`.
51
+
52
+ ##### Multi-GPU Training
53
+
54
+ We use [Distributed training with 🤗 Accelerate (huggingface.co)](https://huggingface.co/docs/transformers/main/accelerate).
55
+
56
+ ```
57
+ CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 accelerate launch --num_processes=8 --multi_gpu protein_lm/modeling/scripts/train.py +train=base train.report_to='wandb' train.training_arguments.per_device_train_batch_size=256 train.training_arguments.use_esm=True train.training_arguments.save_dir='ckpt/ptm_mamba' train.model.model_type='bidirectional_mamba' train.training_arguments.max_tokens_per_batch=40000
58
+ ```
59
+
60
+ - `report_to='wandb'` tracks the training using wandb.
61
+ - `training_arguments.per_device_train_batch_size=300` sets the max batch size per device when constructing a batch.
62
+ - `training_arguments.max_tokens_per_batch=80000` sets the max num of tokens within a batch. If a batch exceeds the max token limit(depending on the seq len), we will trim the batch. Tune the `per_device_train_batch_size` and ``max_tokens_per_batch`` togather to maximize the memory usage during training. The rule of thumb is setting a large batch size (e.g., 300) while searching for the max num token that fits your GPU memory.
63
+ - `training_arguments.use_esm=True` uses the ESM embedding. By default, we use ESM 650M, and set the `model.esm_embed_dim: 1280` in `base.yaml`. If disabled, the model will use its own embeddings.
64
+ - `training_arguments.save_dir='ckpt/bi_directional_mamba-esm'` where the model ckpts will be saved.
65
+ - `training_arguments.sample_len_ascending=true` is enable by default, samples sequences from short to long during the training.
66
+
67
+ ##### Multi-GPU training with Deepspeed
68
+
69
+ Setup deepspeed with
70
+
71
+ ```
72
+ accelerate config
73
+ ```
74
+
75
+ and answer the questions asked. It will ask whether you want to use a config file for DeepSpeed to which you should answer no. Then answer the following questions to generate a basic DeepSpeed config. Use ZeRo 2 and FP32, which are sufficient for training our ~300M model without introducing overhead. This will generate a config file that will be used automatically to properly set the default options when launching training.
76
+
77
+ ## Inference
78
+
79
+ The inference example is at `protein_lm/modeling/scripts/infer.py.` The model checkpoints can be downloaded from [here](https://drive.google.com/file/d/1x_rKff0xswWU7_ixKYvZvWYZPR23cd8x/view?usp=sharing). The outputs are:
80
+
81
+ ```
82
+ Output = namedtuple("output", ["logits", "hidden_states"])
83
+ ```
84
+
85
+ ```
86
+ from protein_lm.modeling.scripts.infer import PTMMamba
87
+
88
+ ckpt_path = "ckpt/bi_mamba-esm-ptm_token_input/best.ckpt"
89
+ mamba = PTMMamba(ckpt_path,device='cuda:0')
90
+ seq = "M<N-acetylalanine>K"
91
+ output = mamba(seq)
92
+ print(output.logits.shape)
93
+ print(output.hidden_states.shape)
94
+ ```
95
+
96
+ ## Ackonwledgement
97
+
98
+ This project is based on the following codebase. Please give them a star if you like our code.
99
+
100
+ - [OpenBioML/protein-lm-scaling (github.com)](https://github.com/OpenBioML/protein-lm-scaling)
101
+ - [state-spaces/mamba (github.com)](https://github.com/state-spaces/mamba)
protein_lm/__init__.py ADDED
File without changes
protein_lm/configs/train/base.yaml ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ seed: 42
2
+ report_to: "none"
3
+
4
+
5
+ dataset:
6
+ dataset: "ptm"
7
+ dataset_type: "csv"
8
+ dataset_loc: "protein_lm/dataset/ptm_labels.csv"
9
+ subsample_size: null
10
+ split_seed: 2
11
+ val_size: 100
12
+ test_size: 0
13
+ sequence_column_name: "ori_seq"
14
+ max_sequence_length: 1024
15
+ cache_dir: "protein_lm/dataset/cache/ptm"
16
+
17
+
18
+ training_arguments:
19
+ save_dir: "checkpoints/ptm-mamba"
20
+ num_train_epochs: 3000
21
+ lr: 2.0e-4
22
+ per_device_train_batch_size: 256 # 64 if 1 GPU
23
+ resume_from_checkpoint: null
24
+ use_esm: true
25
+ max_tokens_per_batch: 80000 # 80G GPU
26
+ sort_by_seq: true
27
+ sample_len_ascending: true
28
+ log_steps: 100
29
+ max_sequence_length: null
30
+
31
+ model: # default mamba 130M
32
+ model_type: "mamba" # ["mamba", "bidirectional_mamba"]
33
+ d_model: 768
34
+ n_layer: 24
35
+ vocab_size: null # will be set by tokenizer
36
+ ssm_cfg: {}
37
+ rms_norm: true
38
+ residual_in_fp32: true
39
+ fused_add_norm: true
40
+
41
+ esm_embed_dim: 1280
42
+ pretrained_checkpoint: null
43
+
protein_lm/modeling/__init__.py ADDED
File without changes
protein_lm/modeling/getters/__init__.py ADDED
File without changes
protein_lm/modeling/getters/collate.py ADDED
@@ -0,0 +1,84 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ from torch.nn.utils.rnn import pad_sequence
3
+ from torch.utils.data import Sampler
4
+ from omegaconf import DictConfig, OmegaConf
5
+ from functools import partial
6
+
7
+
8
+ def crop_seq(input_ids, max_seq_len):
9
+ """
10
+ randomly crop sequences to max_seq_len
11
+ Args:
12
+ input_ids: tensor of shape (seq_len)
13
+ max_seq_len: int
14
+ """
15
+ seq_len = len(input_ids)
16
+ if seq_len <= max_seq_len:
17
+ return input_ids
18
+ else:
19
+ start_idx = torch.randint(0, seq_len - max_seq_len + 1, (1,)).item()
20
+ return input_ids[start_idx : start_idx + max_seq_len]
21
+
22
+ class DataCollatorWithPadding:
23
+ def __init__(
24
+ self,
25
+ max_tokens,
26
+ tokenizer,
27
+ batch_by_tokens=False,
28
+ max_seq_len=None,
29
+ ) -> None:
30
+ self.max_tokens = max_tokens
31
+ self.tokenizer = tokenizer
32
+ self.batch_by_tokens = batch_by_tokens
33
+ self.max_seq_len = max_seq_len
34
+ self.crop_fn = partial(crop_seq, max_seq_len=max_seq_len) if max_seq_len is not None else lambda x: x
35
+
36
+ def __call__(self, batch):
37
+ """
38
+ generate a batch of data g
39
+ Args:
40
+ batch: list of dictionaries with keys 'input_ids' and 'labels'
41
+
42
+ """
43
+ input_ids = [self.crop_fn(i["input_ids"]) for i in batch]
44
+ input_ids = pad_sequence(
45
+ [torch.tensor(x) for x in input_ids],
46
+ batch_first=True,
47
+ padding_value=self.tokenizer.pad_token_id,
48
+ )
49
+ if self.batch_by_tokens:
50
+ # keep a few sequences to make the total number of tokens in the batch <= max_tokens
51
+ total_tokens = input_ids.numel()
52
+ if total_tokens > self.max_tokens:
53
+ max_num_seq = self.max_tokens // input_ids.shape[-1] + 1
54
+ # randomly select max_num_seq sequences from the batch to keep
55
+ indices = torch.randperm(len(input_ids))[:max_num_seq]
56
+ input_ids = input_ids[indices]
57
+ pad_mask = input_ids != self.tokenizer.pad_token_id
58
+
59
+ return {
60
+ "input_ids": input_ids,
61
+ "pad_mask": pad_mask,
62
+ }
63
+
64
+
65
+ class SequenceLengthSampler(Sampler):
66
+ def __init__(self, dataset, sort=True, sample_len_ascending=True):
67
+ """
68
+ Args:
69
+ dataset: a dataset with keys 'input_ids' and 'labels'
70
+ sample_len_ascending: if True, sample shorter sequences first
71
+ """
72
+ self.dataset = dataset
73
+ self.indices = list(range(len(dataset)))
74
+ if sort is True:
75
+ self.indices.sort(
76
+ key=lambda x: len(dataset[x]["input_ids"]),
77
+ reverse=not sample_len_ascending
78
+ )
79
+
80
+ def __iter__(self):
81
+ return iter(self.indices)
82
+
83
+ def __len__(self):
84
+ return len(self.indices)
protein_lm/modeling/getters/dataset.py ADDED
@@ -0,0 +1,16 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import pandas as pd
2
+ from typing import Dict, Literal, Optional
3
+ from datasets import Dataset, load_dataset
4
+ from datasets.dataset_dict import DatasetDict
5
+ from pydantic import BaseModel
6
+ from protein_lm.modeling.getters.ptm_dataset import get_ptm_dataset
7
+ from protein_lm.modeling.getters.uniref_dataset import get_uniref_dataset
8
+
9
+
10
+ def get_dataset(config_dict: Dict, tokenizer) -> Dataset:
11
+ if config_dict["dataset"] == "ptm":
12
+ return get_ptm_dataset(config_dict, tokenizer)
13
+ elif config_dict["dataset"] == "uniref50":
14
+ return get_uniref_dataset(config_dict, tokenizer)
15
+ else:
16
+ raise ValueError(f"Invalid dataset {config_dict['dataset']}!")
protein_lm/modeling/getters/log.py ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import logging
2
+
3
+
4
+ class TrainLogger:
5
+ def __init__(self, log_file="logfile.log"):
6
+ logging.basicConfig(
7
+ filename=log_file, level=logging.INFO, format="%(asctime)s - %(message)s"
8
+ )
9
+
10
+ def log(self, data):
11
+ data = {k: round(v, 4) if isinstance(v, float) else v for k, v in data.items()}
12
+ logging.info(data)
protein_lm/modeling/getters/mask.py ADDED
@@ -0,0 +1,54 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+
3
+
4
+ class Masker:
5
+ def __init__(self, tokenizer) -> None:
6
+ self.tokenizer = tokenizer
7
+ self.mask_token_id = self.tokenizer.mask_token_id
8
+
9
+ def random_mask(self, input_ids, mask_prob=0.15):
10
+ device = input_ids.device
11
+ mask = (torch.rand(input_ids.shape) < mask_prob).to(device)
12
+ mask = mask & (torch.logical_not(self.tokenizer.is_special_token(input_ids)))
13
+ masked_input_ids = input_ids.clone()
14
+ masked_input_ids[mask] = self.mask_token_id
15
+ return masked_input_ids, mask
16
+
17
+ def mask_ptm_tokens(
18
+ self,
19
+ input_ids,
20
+ ):
21
+ device = input_ids.device
22
+ is_ptm_mask = self.tokenizer.is_ptm_token(input_ids).to(device)
23
+ is_ptm_mask = is_ptm_mask & (
24
+ torch.logical_not(self.tokenizer.is_special_token(input_ids))
25
+ )
26
+ masked_input_ids = input_ids.clone()
27
+ masked_input_ids[is_ptm_mask] = self.mask_token_id
28
+ return masked_input_ids, is_ptm_mask
29
+
30
+ def random_and_ptm_mask(self, input_ids, mask_prob=0.15):
31
+ device = input_ids.device
32
+ mask = (torch.rand(input_ids.shape) < mask_prob).to(device)
33
+ mask = mask & (torch.logical_not(self.tokenizer.is_special_token(input_ids)))
34
+ is_ptm_mask = self.tokenizer.is_ptm_token(input_ids).to(device)
35
+ is_ptm_mask = is_ptm_mask & (
36
+ torch.logical_not(self.tokenizer.is_special_token(input_ids))
37
+ )
38
+ mask = mask | is_ptm_mask
39
+ masked_input_ids = input_ids.clone()
40
+ masked_input_ids[mask] = self.mask_token_id
41
+ return masked_input_ids, mask
42
+
43
+ def random_or_random_and_ptm_mask(
44
+ self, input_ids, ranom_mask_prob=0.15, alternate_prob=0.2
45
+ ):
46
+ """
47
+ alternate between [(1) random mask] and [(2) random mask & ptm mask] by probability alternate_prob
48
+ """
49
+ p = torch.rand(1).item()
50
+ if p < alternate_prob:
51
+ return self.random_mask(input_ids, ranom_mask_prob)
52
+ else:
53
+ return self.random_and_ptm_mask(input_ids, ranom_mask_prob)
54
+
protein_lm/modeling/getters/ptm_dataset.py ADDED
@@ -0,0 +1,212 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import pandas as pd
2
+ from typing import Dict, Literal, Optional
3
+ from datasets import Dataset, load_dataset
4
+ from datasets.dataset_dict import DatasetDict
5
+ from pydantic import BaseModel
6
+
7
+
8
+
9
+
10
+ class DatasetConfig(BaseModel):
11
+ dataset: Literal['ptm', 'uniref50']
12
+ dataset_type: Literal["csv", "huggingface", "fasta"]
13
+
14
+ # The path if local or the huggingface dataset name if huggingface
15
+ dataset_loc: str
16
+
17
+ # sample size to limit to, if any, usually for debugging
18
+ subsample_size: Optional[int] = None
19
+
20
+ """
21
+ Args for splitting into train, val, test
22
+ to be updated once we have more options
23
+ """
24
+ # split seed
25
+ split_seed: Optional[int] = None
26
+ # size of validation dataset
27
+ val_size: int
28
+ # size of test dataset
29
+ test_size: int
30
+
31
+ # name of the column that contains the sequence
32
+ sequence_column_name: str
33
+
34
+ max_sequence_length: Optional[int] = None
35
+ cache_dir: Optional[str] = None
36
+
37
+
38
+ def set_labels(result):
39
+ result["labels"] = result["input_ids"].copy()
40
+ return result
41
+
42
+
43
+ def train_val_test_split(
44
+ dataset_dict: DatasetDict,
45
+ config: DatasetConfig,
46
+ ) -> DatasetDict:
47
+ """
48
+ Given a dictionary of datasets that only contains the split "train",
49
+ optionally subsamples it, and then splits it
50
+ so that it has potentially 3 splits: "train", "val", "test", where
51
+ "val" and "test" splits do not exist if the specified sizes are 0
52
+ """
53
+ assert set(dataset_dict.keys()) == {
54
+ "train"
55
+ }, f"{train_val_test_split.__name__} expects its input to have the keys \
56
+ ['train'] but the input has keys {list(dataset_dict.keys())}"
57
+
58
+ dataset = dataset_dict["train"]
59
+
60
+ val_size = config.val_size
61
+ test_size = config.test_size
62
+
63
+ assert isinstance(
64
+ dataset, Dataset
65
+ ), f"Invalid dataset type {type(dataset)}, only datasets.Dataset allowed"
66
+
67
+ dataset = dataset.shuffle(seed=config.split_seed)
68
+
69
+ if config.subsample_size is not None:
70
+ dataset = dataset.select(range(config.subsample_size))
71
+
72
+ valtest_size = val_size + test_size
73
+
74
+ if valtest_size > 0:
75
+ train_valtest = dataset.train_test_split(
76
+ test_size=val_size + test_size,
77
+ shuffle=False,
78
+ )
79
+ split_dict = {
80
+ "train": train_valtest["train"],
81
+ }
82
+ if test_size > 0 and val_size > 0:
83
+ test_val = train_valtest["test"].train_test_split(
84
+ test_size=test_size,
85
+ shuffle=False,
86
+ )
87
+ split_dict["val"] = test_val["train"]
88
+ split_dict["test"] = test_val["test"]
89
+ elif val_size > 0:
90
+ split_dict["val"] = train_valtest["test"]
91
+ else:
92
+ split_dict["test"] = train_valtest["test"]
93
+ else:
94
+ split_dict = {
95
+ "train": dataset,
96
+ }
97
+
98
+ split_dataset_dict = DatasetDict(split_dict)
99
+ return split_dataset_dict
100
+
101
+
102
+
103
+
104
+ def load_ptm_dataset(df: pd.DataFrame, config: DatasetConfig) -> DatasetDict:
105
+ ds = Dataset.from_pandas(df)
106
+ ds_dict = DatasetDict({"train": ds})
107
+ return train_val_test_split(ds_dict, config)
108
+
109
+
110
+ def create_token_dict_from_dataframe(
111
+ df, seq_col="ori_seq", pos_col="pos", token_col="token"
112
+ ):
113
+ result_dict = {}
114
+
115
+ for index, row in df.iterrows():
116
+ ac_id = row[seq_col]
117
+ pos = row[pos_col]
118
+ token = row[token_col]
119
+
120
+ if ac_id not in result_dict:
121
+ result_dict[ac_id] = {}
122
+
123
+ result_dict[ac_id][pos] = token
124
+
125
+ return result_dict
126
+
127
+
128
+ def subsitute_tokens(sequence_lst, token_dict):
129
+ if isinstance(sequence_lst, list):
130
+ return [
131
+ _substitute_token(sequence, token_dict[sequence])
132
+ for sequence in sequence_lst
133
+ ]
134
+ elif isinstance(sequence_lst, str):
135
+ return _substitute_token(sequence_lst, token_dict[sequence_lst])
136
+
137
+
138
+ def _substitute_token(sequence, token_dict):
139
+ result = list(sequence)
140
+ for position, new_tokens in token_dict.items():
141
+ result[position] = new_tokens
142
+ return "".join(result)
143
+
144
+
145
+ def construct_ptm_seq(
146
+ batch, tokenizer, ptm_token_dict, sequence_column_name, max_sequence_length
147
+ ):
148
+ """
149
+ apply transform to the batch to replace the tokens with the PTM tokens
150
+ """
151
+ batch['wt_seq'] = batch[sequence_column_name]
152
+ batch[sequence_column_name] = subsitute_tokens(
153
+ batch[sequence_column_name], ptm_token_dict
154
+ )
155
+ batch['ptm_seq'] = batch[sequence_column_name]
156
+ batch["input_ids"] = tokenizer(
157
+ batch[sequence_column_name],
158
+ add_special_tokens=True,
159
+ max_sequence_length=max_sequence_length,
160
+ )
161
+ # batch["labels"] = batch["input_ids"]
162
+ return batch
163
+
164
+
165
+ def get_ptm_dataset(config_dict: Dict, tokenizer) -> Dataset:
166
+ config = DatasetConfig(**config_dict)
167
+
168
+ if config.dataset_type == "csv":
169
+ df = pd.read_csv(config.dataset_loc)
170
+ ptm_token_dict = create_token_dict_from_dataframe(df)
171
+ df.drop(df.filter(regex="Unnamed"), axis=1, inplace=True)
172
+ df.drop_duplicates(subset=config.sequence_column_name, inplace=True)
173
+ split_dict = load_ptm_dataset(df, config)
174
+ else:
175
+ raise ValueError(f"Invalid dataset_type {config.dataset_type}!")
176
+
177
+ split_dict = split_dict.map(
178
+ lambda e: construct_ptm_seq(
179
+ batch=e,
180
+ tokenizer=tokenizer,
181
+ ptm_token_dict=ptm_token_dict,
182
+ sequence_column_name=config.sequence_column_name,
183
+ max_sequence_length=config.max_sequence_length,
184
+ ),
185
+ batched=True,
186
+ keep_in_memory=True,
187
+ )
188
+
189
+ return split_dict
190
+
191
+
192
+
193
+ if __name__ == "__main__":
194
+ from protein_lm.tokenizer.tokenizer import PTMTokenizer
195
+ from transformers import DataCollatorWithPadding, default_data_collator
196
+
197
+ tokenizer = PTMTokenizer()
198
+ data_collator = DataCollatorWithPadding(
199
+ tokenizer=tokenizer, padding="longest", max_length=1024, return_tensors="pt"
200
+ )
201
+ config_dict = {
202
+ "dataset_type": "csv",
203
+ "dataset_loc": "protein_lm/dataset/ptm_labels.csv",
204
+ "subsample_size": None,
205
+ "val_size": 0,
206
+ "test_size": 0,
207
+ "sequence_column_name": "ori_seq",
208
+ "max_sequence_length": None,
209
+ "dataset": "ptm",
210
+ }
211
+ dataset = get_ptm_dataset(config_dict, tokenizer)
212
+ samples = dataset["train"][:8]
protein_lm/modeling/getters/scheduler.py ADDED
@@ -0,0 +1,198 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
2
+
3
+ from torch.optim.lr_scheduler import _LRScheduler, CosineAnnealingLR
4
+
5
+
6
+ class ConstantLRScheduler(_LRScheduler):
7
+ def __init__(
8
+ self,
9
+ optimizer,
10
+ last_epoch: int = -1,
11
+ verbose: bool = False,
12
+ init_lr: float = 0.0,
13
+ ):
14
+ """
15
+ This is an implementation of constant learning rate scheduler.
16
+ Args:
17
+ optimizer: Optimizer
18
+
19
+ last_epoch: The index of last epoch. Default: -1
20
+
21
+ verbose: If ``True``, prints a message to stdout for each update. Default: ``False``
22
+
23
+ init_lr: Initial learning rate
24
+ """
25
+
26
+ self.init_lr = init_lr
27
+ super().__init__(optimizer, last_epoch, verbose)
28
+
29
+ def state_dict(self):
30
+ state_dict = {k: v for k, v in self.__dict__.items() if k not in ["optimizer"]}
31
+ return state_dict
32
+
33
+ def load_state_dict(self, state_dict):
34
+ self.__dict__.update(state_dict)
35
+
36
+ def get_lr(self):
37
+ if not self._get_lr_called_within_step:
38
+ raise RuntimeError(
39
+ "To get the last learning rate computed by the scheduler, use "
40
+ "get_last_lr()"
41
+ )
42
+
43
+ return [self.init_lr for group in self.optimizer.param_groups]
44
+
45
+
46
+ class CosineAnnealingLRScheduler(_LRScheduler):
47
+ def __init__(
48
+ self,
49
+ optimizer,
50
+ last_epoch: int = -1,
51
+ verbose: bool = False,
52
+ init_lr: float = 4.0e-5,
53
+ max_lr: float = 4e-4,
54
+ final_lr: float = 4e-5,
55
+ warmup_steps: int = 2000,
56
+ cosine_steps: int = 10000,
57
+ ):
58
+ """
59
+ This is an implementation of cosine annealing learning rate scheduler.
60
+ Args:
61
+ optimizer: Optimizer
62
+
63
+ last_epoch: The index of last epoch. Default: -1
64
+
65
+ verbose: If ``True``, prints a message to stdout for each update. Default: ``False``
66
+
67
+ init_lr: Initial learning rate
68
+
69
+ max_lr: Maximum learning rate after warmup
70
+
71
+ final_lr: Final learning rate after decay
72
+
73
+ warmup_steps: Number of steps for warmup
74
+
75
+ cosine_steps: Number of steps for cosine annealing
76
+ """
77
+
78
+ self.init_lr = init_lr
79
+ self.max_lr = max_lr
80
+ self.final_lr = final_lr
81
+ self.warmup_steps = warmup_steps
82
+ self.cosine_steps = cosine_steps
83
+ super(CosineAnnealingLRScheduler, self).__init__(optimizer, last_epoch, verbose)
84
+
85
+ def state_dict(self):
86
+ state_dict = {k: v for k, v in self.__dict__.items() if k not in ["optimizer"]}
87
+ return state_dict
88
+
89
+ def load_state_dict(self, state_dict):
90
+ self.__dict__.update(state_dict)
91
+
92
+ def get_lr(self):
93
+ if not self._get_lr_called_within_step:
94
+ raise RuntimeError(
95
+ "To get the last learning rate computed by the scheduler, use "
96
+ "get_last_lr()"
97
+ )
98
+
99
+ step_no = self.last_epoch
100
+
101
+ if step_no <= self.warmup_steps:
102
+ lr = self.init_lr + step_no / self.warmup_steps * (
103
+ self.max_lr - self.init_lr
104
+ )
105
+
106
+ else:
107
+ lr = self.final_lr + 0.5 * (self.max_lr - self.final_lr) * (
108
+ 1
109
+ + math.cos(math.pi * (step_no - self.warmup_steps) / self.cosine_steps)
110
+ )
111
+
112
+ return [lr for group in self.optimizer.param_groups]
113
+
114
+
115
+ class Esm2LRScheduler(_LRScheduler):
116
+ def __init__(
117
+ self,
118
+ optimizer,
119
+ last_epoch: int = -1,
120
+ verbose: bool = False,
121
+ init_lr: float = 4e-5,
122
+ max_lr: float = 4e-4,
123
+ final_lr: float = 4e-5,
124
+ warmup_steps: int = 2000,
125
+ start_decay_after_n_steps: int = 500000,
126
+ end_decay_after_n_steps: int = 5000000,
127
+ on_use: bool = True,
128
+ ):
129
+ """
130
+ An implementation of ESM2's learning rate scheduler.
131
+ Args:
132
+ optimizer: Optimizer
133
+
134
+ last_epoch: The index of last epoch. Default: -1
135
+
136
+ verbose: If ``True``, prints a message to stdout for each update. Default: ``False``
137
+
138
+ init_lr: Initial learning rate
139
+
140
+ max_lr: Maximum learning rate after warmup
141
+
142
+ final_lr: Final learning rate after decay
143
+
144
+ warmup_steps: Number of steps for warmup
145
+
146
+ start_decay_after_n_steps: Start decay after this number of steps
147
+
148
+ end_decay_after_n_steps: End decay after this number of steps
149
+
150
+ on_use: Whether to use this scheduler. If ``False``, the scheduler will not change the learning rate
151
+ and will only use the ``init_lr``. Default: ``True``
152
+ """
153
+
154
+ self.init_lr = init_lr
155
+ self.max_lr = max_lr
156
+ self.final_lr = final_lr
157
+ self.warmup_steps = warmup_steps
158
+ self.start_decay_after_n_steps = start_decay_after_n_steps
159
+ self.end_decay_after_n_steps = end_decay_after_n_steps
160
+ self.on_use = on_use
161
+ super(Esm2LRScheduler, self).__init__(optimizer, last_epoch, verbose)
162
+
163
+ def state_dict(self):
164
+ state_dict = {k: v for k, v in self.__dict__.items() if k not in ["optimizer"]}
165
+ return state_dict
166
+
167
+ def load_state_dict(self, state_dict):
168
+ self.__dict__.update(state_dict)
169
+
170
+ def get_lr(self):
171
+ if not self._get_lr_called_within_step:
172
+ raise RuntimeError(
173
+ "To get the last learning rate computed by the scheduler, use "
174
+ "get_last_lr()"
175
+ )
176
+
177
+ step_no = self.last_epoch
178
+ if not self.on_use:
179
+ return [base_lr for base_lr in self.base_lrs]
180
+
181
+ if step_no <= self.warmup_steps:
182
+ lr = self.init_lr + step_no / self.warmup_steps * (
183
+ self.max_lr - self.init_lr
184
+ )
185
+
186
+ elif step_no <= self.start_decay_after_n_steps:
187
+ lr = self.max_lr
188
+
189
+ elif step_no <= self.end_decay_after_n_steps:
190
+ portion = (step_no - self.start_decay_after_n_steps) / (
191
+ self.end_decay_after_n_steps - self.start_decay_after_n_steps
192
+ )
193
+ lr = self.max_lr - portion * (self.max_lr - self.final_lr)
194
+
195
+ else:
196
+ lr = self.final_lr
197
+
198
+ return [lr for group in self.optimizer.param_groups]
protein_lm/modeling/getters/training_args.py ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ from typing import Dict
3
+
4
+ from transformers import TrainingArguments
5
+
6
+
7
+ def get_training_args(config_dict: Dict) -> TrainingArguments:
8
+ config = TrainingArguments(**config_dict)
9
+
10
+ if not os.path.isdir(config.output_dir):
11
+ print(f"creating checkpoint directory at {config.output_dir}")
12
+ os.makedirs(config.output_dir)
13
+
14
+ return config
protein_lm/modeling/getters/uniref_dataset.py ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import pandas as pd
3
+
4
+ from Bio import SeqIO
5
+ from typing import Dict, Literal, Optional
6
+ from datasets import Dataset, load_dataset
7
+ from datasets.dataset_dict import DatasetDict
8
+ from typing import Dict, Literal, Optional
9
+ from protein_lm.modeling.getters.ptm_dataset import DatasetConfig, train_val_test_split
10
+
11
+
12
+ def read_fasta_file(fasta_file_path, subsample_size):
13
+ ids = []
14
+ seqs = []
15
+ with open(fasta_file_path, "r") as fasta_file:
16
+ for i, record in enumerate(SeqIO.parse(fasta_file, "fasta")):
17
+ if subsample_size and i >= subsample_size:
18
+ break
19
+ ids.append(record.id)
20
+ seqs.append(str(record.seq))
21
+
22
+ return {"id": ids, "seq": seqs}
23
+
24
+
25
+ def load_uniref_dataset(seq_dict, config) -> DatasetDict:
26
+ ds = Dataset.from_dict(seq_dict)
27
+ ds_dict = DatasetDict({"train": ds})
28
+ return train_val_test_split(ds_dict, config)
29
+
30
+
31
+ def seq2token(batch, tokenizer, sequence_column_name, max_sequence_length):
32
+ batch["input_ids"] = tokenizer(
33
+ batch[sequence_column_name],
34
+ add_special_tokens=True,
35
+ max_sequence_length=max_sequence_length,
36
+ )
37
+ return batch
38
+
39
+
40
+ def get_uniref_dataset(config: Dict, tokenizer) -> Dataset:
41
+ # config = DatasetConfig(**config_dict)
42
+ if config.cache_dir is not None and os.path.exists(config.cache_dir):
43
+ split_dict = DatasetDict.load_from_disk(config.cache_dir)
44
+ return split_dict
45
+ seq_dict = read_fasta_file(config.dataset_loc, config.subsample_size)
46
+ split_dict = load_uniref_dataset(seq_dict, config)
47
+ split_dict = split_dict.map(
48
+ lambda e: seq2token(
49
+ batch=e,
50
+ tokenizer=tokenizer,
51
+ sequence_column_name="seq",
52
+ max_sequence_length=config.max_sequence_length,
53
+ ),
54
+ batched=True,
55
+ )
56
+ if config.cache_dir is not None:
57
+ os.makedirs(config.cache_dir, exist_ok=True)
58
+ split_dict.save_to_disk(config.cache_dir)
59
+ return split_dict
protein_lm/modeling/models/__init__.py ADDED
File without changes
protein_lm/modeling/models/libs/causal-conv1d/AUTHORS ADDED
@@ -0,0 +1 @@
 
 
1
+ Tri Dao, tri@tridao.me
protein_lm/modeling/models/libs/causal-conv1d/LICENSE ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ BSD 3-Clause License
2
+
3
+ Copyright (c) 2022, the respective contributors, as shown by the AUTHORS file.
4
+ All rights reserved.
5
+
6
+ Redistribution and use in source and binary forms, with or without
7
+ modification, are permitted provided that the following conditions are met:
8
+
9
+ * Redistributions of source code must retain the above copyright notice, this
10
+ list of conditions and the following disclaimer.
11
+
12
+ * Redistributions in binary form must reproduce the above copyright notice,
13
+ this list of conditions and the following disclaimer in the documentation
14
+ and/or other materials provided with the distribution.
15
+
16
+ * Neither the name of the copyright holder nor the names of its
17
+ contributors may be used to endorse or promote products derived from
18
+ this software without specific prior written permission.
19
+
20
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
21
+ AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
22
+ IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
23
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
24
+ FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
25
+ DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
26
+ SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
27
+ CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
28
+ OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
29
+ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
protein_lm/modeling/models/libs/causal-conv1d/README.md ADDED
@@ -0,0 +1 @@
 
 
1
+ # Causal depthwise conv1d in CUDA with a PyTorch interface
protein_lm/modeling/models/libs/causal-conv1d/causal_conv1d/__init__.py ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ __version__ = "1.0.0"
2
+
3
+ from causal_conv1d.causal_conv1d_interface import causal_conv1d_fn, causal_conv1d_update
protein_lm/modeling/models/libs/causal-conv1d/causal_conv1d/causal_conv1d_interface.py ADDED
@@ -0,0 +1,104 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) 2023, Tri Dao.
2
+
3
+ import torch
4
+ import torch.nn.functional as F
5
+
6
+
7
+ import causal_conv1d_cuda
8
+
9
+
10
+ class CausalConv1dFn(torch.autograd.Function):
11
+ @staticmethod
12
+ def forward(ctx, x, weight, bias=None, activation=None):
13
+ if activation not in [None, "silu", "swish"]:
14
+ raise NotImplementedError("activation must be None, silu, or swish")
15
+ if x.stride(2) != 1 and x.stride(1) != 1:
16
+ x = x.contiguous()
17
+ bias = bias.contiguous() if bias is not None else None
18
+ ctx.save_for_backward(x, weight, bias)
19
+ ctx.activation = activation in ["silu", "swish"]
20
+ out = causal_conv1d_cuda.causal_conv1d_fwd(x, weight, bias, ctx.activation)
21
+ return out
22
+
23
+ @staticmethod
24
+ def backward(ctx, dout):
25
+ x, weight, bias = ctx.saved_tensors
26
+ if dout.stride(2) != 1 and dout.stride(1) != 1:
27
+ dout = dout.contiguous()
28
+ # The kernel supports passing in a pre-allocated dx (e.g., in case we want to fuse the
29
+ # backward of conv1d with the backward of chunk).
30
+ # Here we just pass in None and dx will be allocated in the C++ code.
31
+ dx, dweight, dbias = causal_conv1d_cuda.causal_conv1d_bwd(
32
+ x, weight, bias, dout, None, ctx.activation
33
+ )
34
+ return dx, dweight, dbias if bias is not None else None, None
35
+
36
+
37
+ def causal_conv1d_fn(x, weight, bias=None, activation=None):
38
+ """
39
+ x: (batch, dim, seqlen)
40
+ weight: (dim, width)
41
+ bias: (dim,)
42
+ activation: either None or "silu" or "swish"
43
+
44
+ out: (batch, dim, seqlen)
45
+ """
46
+ return CausalConv1dFn.apply(x, weight, bias, activation)
47
+
48
+
49
+ def causal_conv1d_ref(x, weight, bias=None, activation=None):
50
+ """
51
+ x: (batch, dim, seqlen)
52
+ weight: (dim, width)
53
+ bias: (dim,)
54
+
55
+ out: (batch, dim, seqlen)
56
+ """
57
+ if activation not in [None, "silu", "swish"]:
58
+ raise NotImplementedError("activation must be None, silu, or swish")
59
+ dtype_in = x.dtype
60
+ x = x.to(weight.dtype)
61
+ seqlen = x.shape[-1]
62
+ dim, width = weight.shape
63
+ out = F.conv1d(x, weight.unsqueeze(1), bias, padding=width - 1, groups=dim)
64
+ out = out[..., :seqlen]
65
+ return (out if activation is None else F.silu(out)).to(dtype=dtype_in)
66
+
67
+
68
+ def causal_conv1d_update(x, conv_state, weight, bias=None, activation=None):
69
+ """
70
+ x: (batch, dim)
71
+ conv_state: (batch, dim, width)
72
+ weight: (dim, width)
73
+ bias: (dim,)
74
+
75
+ out: (batch, dim)
76
+ """
77
+ if activation not in [None, "silu", "swish"]:
78
+ raise NotImplementedError("activation must be None, silu, or swish")
79
+ activation = activation in ["silu", "swish"]
80
+ return causal_conv1d_cuda.causal_conv1d_update(x, conv_state, weight, bias, activation)
81
+
82
+
83
+ def causal_conv1d_update_ref(x, conv_state, weight, bias=None, activation=None):
84
+ """
85
+ x: (batch, dim)
86
+ conv_state: (batch, dim, width)
87
+ weight: (dim, width)
88
+ bias: (dim,)
89
+
90
+ out: (batch, dim)
91
+ """
92
+ if activation not in [None, "silu", "swish"]:
93
+ raise NotImplementedError("activation must be None, silu, or swish")
94
+ dtype_in = x.dtype
95
+ batch, dim = x.shape
96
+ width = weight.shape[1]
97
+ assert conv_state.shape == (batch, dim, width)
98
+ assert weight.shape == (dim, width)
99
+ conv_state.copy_(torch.roll(conv_state, shifts=-1, dims=-1)) # Update state (B D W)
100
+ conv_state[:, :, -1] = x
101
+ out = torch.sum(conv_state * weight, dim=-1) # (B D)
102
+ if bias is not None:
103
+ out += bias
104
+ return (out if activation is None else F.silu(out)).to(dtype=dtype_in)
protein_lm/modeling/models/libs/causal-conv1d/csrc/causal_conv1d.cpp ADDED
@@ -0,0 +1,333 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ #include <ATen/cuda/CUDAContext.h>
6
+ #include <c10/cuda/CUDAGuard.h>
7
+ #include <torch/extension.h>
8
+ #include <vector>
9
+
10
+ #include "causal_conv1d.h"
11
+
12
+ #define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")")
13
+
14
+ #define DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(ITYPE, NAME, ...) \
15
+ if (ITYPE == at::ScalarType::Half) { \
16
+ using input_t = at::Half; \
17
+ __VA_ARGS__(); \
18
+ } else if (ITYPE == at::ScalarType::BFloat16) { \
19
+ using input_t = at::BFloat16; \
20
+ __VA_ARGS__(); \
21
+ } else if (ITYPE == at::ScalarType::Float) { \
22
+ using input_t = float; \
23
+ __VA_ARGS__(); \
24
+ } else { \
25
+ AT_ERROR(#NAME, " not implemented for input type '", toString(ITYPE), "'"); \
26
+ }
27
+
28
+ #define DISPATCH_WTYPE_FLOAT_AND_HALF_AND_BF16(WTYPE, NAME, ...) \
29
+ if (WTYPE == at::ScalarType::Half) { \
30
+ using weight_t = at::Half; \
31
+ __VA_ARGS__(); \
32
+ } else if (WTYPE == at::ScalarType::BFloat16) { \
33
+ using weight_t = at::BFloat16; \
34
+ __VA_ARGS__(); \
35
+ } else if (WTYPE == at::ScalarType::Float) { \
36
+ using weight_t = float; \
37
+ __VA_ARGS__(); \
38
+ } else { \
39
+ AT_ERROR(#NAME, " not implemented for weight type '", toString(WTYPE), "'"); \
40
+ }
41
+
42
+ template<typename input_t, typename weight_t>
43
+ void causal_conv1d_fwd_cuda(ConvParamsBase &params, cudaStream_t stream);
44
+ template <typename input_t, typename weight_t>
45
+ void causal_conv1d_channellast_fwd_cuda(ConvParamsBase &params, cudaStream_t stream);
46
+
47
+ template<typename input_t, typename weight_t>
48
+ void causal_conv1d_bwd_cuda(ConvParamsBwd &params, cudaStream_t stream);
49
+ template<typename input_t, typename weight_t>
50
+ void causal_conv1d_channellast_bwd_cuda(ConvParamsBwd &params, cudaStream_t stream);
51
+
52
+ template<typename input_t, typename weight_t>
53
+ void causal_conv1d_update_cuda(ConvParamsBase &params, cudaStream_t stream);
54
+
55
+ void set_conv_params_fwd(ConvParamsBase &params,
56
+ // sizes
57
+ const size_t batch,
58
+ const size_t dim,
59
+ const size_t seqlen,
60
+ const size_t width,
61
+ // device pointers
62
+ const at::Tensor x,
63
+ const at::Tensor weight,
64
+ const at::Tensor out,
65
+ void* bias_ptr,
66
+ bool silu_activation) {
67
+
68
+ // Reset the parameters
69
+ memset(&params, 0, sizeof(params));
70
+
71
+ params.batch = batch;
72
+ params.dim = dim;
73
+ params.seqlen = seqlen;
74
+ params.width = width;
75
+
76
+ params.silu_activation = silu_activation;
77
+
78
+ // Set the pointers and strides.
79
+ params.x_ptr = x.data_ptr();
80
+ params.weight_ptr = weight.data_ptr();
81
+ params.bias_ptr = bias_ptr;
82
+ params.out_ptr = out.data_ptr();
83
+ // All stride are in elements, not bytes.
84
+ params.x_batch_stride = x.stride(0);
85
+ params.x_c_stride = x.stride(1);
86
+ params.x_l_stride = x.stride(-1);
87
+ params.weight_c_stride = weight.stride(0);
88
+ params.weight_width_stride = weight.stride(1);
89
+ params.out_batch_stride = out.stride(0);
90
+ params.out_c_stride = out.stride(1);
91
+ params.out_l_stride = out.stride(-1);
92
+ }
93
+
94
+
95
+ void set_conv_params_bwd(ConvParamsBwd &params,
96
+ // sizes
97
+ const size_t batch,
98
+ const size_t dim,
99
+ const size_t seqlen,
100
+ const size_t width,
101
+ // device pointers
102
+ const at::Tensor x,
103
+ const at::Tensor weight,
104
+ void* bias_ptr,
105
+ const at::Tensor dout,
106
+ const at::Tensor dx,
107
+ const at::Tensor dweight,
108
+ void* dbias_ptr,
109
+ bool silu_activation) {
110
+ // Pass in "dout" instead of "out", we're not gonna use "out" at all.
111
+ set_conv_params_fwd(params, batch, dim, seqlen, width,
112
+ x, weight, dout, bias_ptr, silu_activation);
113
+
114
+ // Set the pointers and strides.
115
+ params.dout_ptr = dout.data_ptr();
116
+ params.dx_ptr = dx.data_ptr();
117
+ params.dweight_ptr = dweight.data_ptr();
118
+ params.dbias_ptr = dbias_ptr;
119
+ // All stride are in elements, not bytes.
120
+ params.dout_batch_stride = dout.stride(0);
121
+ params.dout_c_stride = dout.stride(1);
122
+ params.dout_l_stride = dout.stride(2);
123
+ params.dweight_c_stride = dweight.stride(0);
124
+ params.dweight_width_stride = dweight.stride(1);
125
+ params.dx_batch_stride = dx.stride(0);
126
+ params.dx_c_stride = dx.stride(1);
127
+ params.dx_l_stride = dx.stride(2);
128
+ }
129
+
130
+ at::Tensor
131
+ causal_conv1d_fwd(const at::Tensor &x, const at::Tensor &weight,
132
+ const c10::optional<at::Tensor> &bias_,
133
+ bool silu_activation) {
134
+ auto input_type = x.scalar_type();
135
+ auto weight_type = weight.scalar_type();
136
+ TORCH_CHECK(input_type == at::ScalarType::Float || input_type == at::ScalarType::Half || input_type == at::ScalarType::BFloat16);
137
+ TORCH_CHECK(weight_type == at::ScalarType::Float || weight_type == at::ScalarType::Half || weight_type == at::ScalarType::BFloat16);
138
+
139
+ TORCH_CHECK(x.is_cuda());
140
+ TORCH_CHECK(weight.is_cuda());
141
+
142
+ const auto sizes = x.sizes();
143
+ const int batch_size = sizes[0];
144
+ const int dim = sizes[1];
145
+ const int seqlen = sizes[2];
146
+ const int width = weight.size(-1);
147
+
148
+ CHECK_SHAPE(x, batch_size, dim, seqlen);
149
+ CHECK_SHAPE(weight, dim, width);
150
+
151
+ TORCH_CHECK(x.stride(2) == 1 || x.stride(1) == 1);
152
+ const bool is_channel_last = x.stride(1) == 1 && x.stride(2) > 1;
153
+
154
+ if (is_channel_last) {
155
+ TORCH_CHECK(dim % 8 == 0, "causal_conv1d only supports channel dimension divisible by 8 for now");
156
+ }
157
+ TORCH_CHECK(width >= 2 && width <= 4, "causal_conv1d only supports width between 2 and 4");
158
+
159
+
160
+ if (bias_.has_value()) {
161
+ auto bias = bias_.value();
162
+ TORCH_CHECK(bias.scalar_type() == weight_type);
163
+ TORCH_CHECK(bias.is_cuda());
164
+ TORCH_CHECK(bias.stride(-1) == 1);
165
+ CHECK_SHAPE(bias, dim);
166
+ }
167
+
168
+ at::Tensor out = torch::empty_like(x);
169
+
170
+ ConvParamsBase params;
171
+ set_conv_params_fwd(params, batch_size, dim, seqlen, width, x, weight, out,
172
+ bias_.has_value() ? bias_.value().data_ptr() : nullptr,
173
+ silu_activation);
174
+
175
+ // Otherwise the kernel will be launched from cuda:0 device
176
+ // Cast to char to avoid compiler warning about narrowing
177
+ at::cuda::CUDAGuard device_guard{(char)x.get_device()};
178
+ auto stream = at::cuda::getCurrentCUDAStream().stream();
179
+ DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(x.scalar_type(), "causal_conv1d_fwd", [&] {
180
+ DISPATCH_WTYPE_FLOAT_AND_HALF_AND_BF16(weight.scalar_type(), "causal_conv1d_fwd", [&] {
181
+ if (!is_channel_last) {
182
+ causal_conv1d_fwd_cuda<input_t, weight_t>(params, stream);
183
+ } else {
184
+ causal_conv1d_channellast_fwd_cuda<input_t, weight_t>(params, stream);
185
+ }
186
+ });
187
+ });
188
+ return out;
189
+ }
190
+
191
+ std::vector<at::Tensor>
192
+ causal_conv1d_bwd(const at::Tensor &x, const at::Tensor &weight,
193
+ const c10::optional<at::Tensor> &bias_,
194
+ at::Tensor &dout,
195
+ c10::optional<at::Tensor> &dx_,
196
+ bool silu_activation) {
197
+ auto input_type = x.scalar_type();
198
+ auto weight_type = weight.scalar_type();
199
+ TORCH_CHECK(input_type == at::ScalarType::Float || input_type == at::ScalarType::Half || input_type == at::ScalarType::BFloat16);
200
+ TORCH_CHECK(weight_type == at::ScalarType::Float || weight_type == at::ScalarType::Half || weight_type == at::ScalarType::BFloat16);
201
+
202
+ TORCH_CHECK(x.is_cuda());
203
+ TORCH_CHECK(weight.is_cuda());
204
+ TORCH_CHECK(dout.is_cuda());
205
+
206
+ const auto sizes = x.sizes();
207
+ const int batch_size = sizes[0];
208
+ const int dim = sizes[1];
209
+ const int seqlen = sizes[2];
210
+ const int width = weight.size(-1);
211
+
212
+ TORCH_CHECK(width >= 2 && width <= 4, "causal_conv1d only supports width between 2 and 4");
213
+
214
+ CHECK_SHAPE(x, batch_size, dim, seqlen);
215
+ CHECK_SHAPE(weight, dim, width);
216
+ CHECK_SHAPE(dout, batch_size, dim, seqlen);
217
+
218
+ TORCH_CHECK(x.stride(2) == 1 || x.stride(1) == 1);
219
+ const bool is_channel_last = x.stride(1) == 1 && x.stride(2) > 1;
220
+ if (!is_channel_last && dout.stride(2) != 1) { dout = dout.contiguous(); }
221
+ if (is_channel_last && dout.stride(1) != 1) { dout = dout.transpose(-1, -2).contiguous().transpose(-1, -2); }
222
+
223
+ if (bias_.has_value()) {
224
+ auto bias = bias_.value();
225
+ TORCH_CHECK(bias.scalar_type() == weight_type);
226
+ TORCH_CHECK(bias.is_cuda());
227
+ TORCH_CHECK(bias.stride(-1) == 1);
228
+ CHECK_SHAPE(bias, dim);
229
+ }
230
+
231
+ at::Tensor dx;
232
+ if (dx_.has_value()) {
233
+ dx = dx_.value();
234
+ TORCH_CHECK(dx.scalar_type() == input_type);
235
+ TORCH_CHECK(dx.is_cuda());
236
+ CHECK_SHAPE(dx, batch_size, dim, seqlen);
237
+ if (!is_channel_last) { TORCH_CHECK(dx.stride(2) == 1); }
238
+ if (is_channel_last) { TORCH_CHECK(dx.stride(1) == 1); }
239
+ } else {
240
+ dx = torch::empty_like(x);
241
+ }
242
+
243
+ // Otherwise the kernel will be launched from cuda:0 device
244
+ // Cast to char to avoid compiler warning about narrowing
245
+ at::cuda::CUDAGuard device_guard{(char)x.get_device()};
246
+
247
+ at::Tensor dweight = torch::zeros_like(weight, weight.options().dtype(at::kFloat));
248
+ at::Tensor dbias;
249
+ if (bias_.has_value()) { dbias = torch::zeros_like(bias_.value(), bias_.value().options().dtype(at::kFloat)); }
250
+
251
+ ConvParamsBwd params;
252
+ set_conv_params_bwd(params, batch_size, dim, seqlen, width,
253
+ x, weight, bias_.has_value() ? bias_.value().data_ptr() : nullptr,
254
+ dout, dx, dweight, bias_.has_value() ? dbias.data_ptr() : nullptr,
255
+ silu_activation);
256
+
257
+ auto stream = at::cuda::getCurrentCUDAStream().stream();
258
+ DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(x.scalar_type(), "causal_conv1d_bwd", [&] {
259
+ DISPATCH_WTYPE_FLOAT_AND_HALF_AND_BF16(weight.scalar_type(), "causal_conv1d_bwd", [&] {
260
+ if (!is_channel_last) {
261
+ causal_conv1d_bwd_cuda<input_t, weight_t>(params, stream);
262
+ } else {
263
+ causal_conv1d_channellast_bwd_cuda<input_t, weight_t>(params, stream);
264
+ }
265
+ });
266
+ });
267
+ return {dx, dweight.to(weight.dtype()), bias_.has_value() ? dbias.to(bias_.value().dtype()) : dbias};
268
+ }
269
+
270
+ at::Tensor
271
+ causal_conv1d_update(const at::Tensor &x,
272
+ const at::Tensor &conv_state,
273
+ const at::Tensor &weight,
274
+ const c10::optional<at::Tensor> &bias_,
275
+ bool silu_activation) {
276
+ auto input_type = x.scalar_type();
277
+ auto weight_type = weight.scalar_type();
278
+ TORCH_CHECK(input_type == at::ScalarType::Float || input_type == at::ScalarType::Half || input_type == at::ScalarType::BFloat16);
279
+ TORCH_CHECK(weight_type == at::ScalarType::Float || weight_type == at::ScalarType::Half || weight_type == at::ScalarType::BFloat16);
280
+ TORCH_CHECK(conv_state.scalar_type() == input_type);
281
+
282
+ TORCH_CHECK(x.is_cuda());
283
+ TORCH_CHECK(conv_state.is_cuda());
284
+ TORCH_CHECK(weight.is_cuda());
285
+
286
+ const auto sizes = x.sizes();
287
+ const int batch_size = sizes[0];
288
+ const int dim = sizes[1];
289
+ const int width = weight.size(-1);
290
+
291
+ CHECK_SHAPE(x, batch_size, dim);
292
+ CHECK_SHAPE(conv_state, batch_size, dim, width);
293
+ CHECK_SHAPE(weight, dim, width);
294
+
295
+ TORCH_CHECK(width >= 2 && width <= 4, "causal_conv1d only supports width between 2 and 4");
296
+
297
+ if (bias_.has_value()) {
298
+ auto bias = bias_.value();
299
+ TORCH_CHECK(bias.scalar_type() == weight_type);
300
+ TORCH_CHECK(bias.is_cuda());
301
+ TORCH_CHECK(bias.stride(-1) == 1);
302
+ CHECK_SHAPE(bias, dim);
303
+ }
304
+
305
+ at::Tensor out = torch::empty_like(x);
306
+
307
+ ConvParamsBase params;
308
+ set_conv_params_fwd(params, batch_size, dim, /*seqlen=*/1, width, x, weight, out,
309
+ bias_.has_value() ? bias_.value().data_ptr() : nullptr,
310
+ silu_activation);
311
+ params.conv_state_ptr = conv_state.data_ptr();
312
+ // All stride are in elements, not bytes.
313
+ params.conv_state_batch_stride = conv_state.stride(0);
314
+ params.conv_state_c_stride = conv_state.stride(1);
315
+ params.conv_state_l_stride = conv_state.stride(2);
316
+
317
+ // Otherwise the kernel will be launched from cuda:0 device
318
+ // Cast to char to avoid compiler warning about narrowing
319
+ at::cuda::CUDAGuard device_guard{(char)x.get_device()};
320
+ auto stream = at::cuda::getCurrentCUDAStream().stream();
321
+ DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(x.scalar_type(), "causal_conv1d_update", [&] {
322
+ DISPATCH_WTYPE_FLOAT_AND_HALF_AND_BF16(weight.scalar_type(), "causal_conv1d_update", [&] {
323
+ causal_conv1d_update_cuda<input_t, weight_t>(params, stream);
324
+ });
325
+ });
326
+ return out;
327
+ }
328
+
329
+ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
330
+ m.def("causal_conv1d_fwd", &causal_conv1d_fwd, "Causal conv1d forward");
331
+ m.def("causal_conv1d_bwd", &causal_conv1d_bwd, "Causal conv1d backward");
332
+ m.def("causal_conv1d_update", &causal_conv1d_update, "Causal conv1d update");
333
+ }
protein_lm/modeling/models/libs/causal-conv1d/csrc/causal_conv1d.h ADDED
@@ -0,0 +1,53 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ #pragma once
6
+
7
+ ////////////////////////////////////////////////////////////////////////////////////////////////////
8
+
9
+ struct ConvParamsBase {
10
+ using index_t = uint32_t;
11
+
12
+ int batch, dim, seqlen, width;
13
+ bool silu_activation;
14
+
15
+ index_t x_batch_stride;
16
+ index_t x_c_stride;
17
+ index_t x_l_stride;
18
+ index_t weight_c_stride;
19
+ index_t weight_width_stride;
20
+ index_t out_batch_stride;
21
+ index_t out_c_stride;
22
+ index_t out_l_stride;
23
+
24
+ index_t conv_state_batch_stride;
25
+ index_t conv_state_c_stride;
26
+ index_t conv_state_l_stride;
27
+
28
+ // Common data pointers.
29
+ void *__restrict__ x_ptr;
30
+ void *__restrict__ weight_ptr;
31
+ void *__restrict__ bias_ptr;
32
+ void *__restrict__ out_ptr;
33
+
34
+ void *__restrict__ conv_state_ptr;
35
+ };
36
+
37
+ struct ConvParamsBwd: public ConvParamsBase {
38
+ index_t dx_batch_stride;
39
+ index_t dx_c_stride;
40
+ index_t dx_l_stride;
41
+ index_t dweight_c_stride;
42
+ index_t dweight_width_stride;
43
+ index_t dout_batch_stride;
44
+ index_t dout_c_stride;
45
+ index_t dout_l_stride;
46
+
47
+ // Common data pointers.
48
+ void *__restrict__ dx_ptr;
49
+ void *__restrict__ dweight_ptr;
50
+ void *__restrict__ dbias_ptr;
51
+ void *__restrict__ dout_ptr;
52
+ };
53
+
protein_lm/modeling/models/libs/causal-conv1d/csrc/causal_conv1d_bwd.cu ADDED
@@ -0,0 +1,525 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ #include <c10/util/BFloat16.h>
6
+ #include <c10/util/Half.h>
7
+ #include <c10/cuda/CUDAException.h> // For C10_CUDA_CHECK and C10_CUDA_KERNEL_LAUNCH_CHECK
8
+
9
+ #include <cub/block/block_load.cuh>
10
+ #include <cub/block/block_store.cuh>
11
+ #include <cub/block/block_reduce.cuh>
12
+
13
+ #include "causal_conv1d.h"
14
+ #include "causal_conv1d_common.h"
15
+ #include "static_switch.h"
16
+
17
+ template<int kNThreads_, int kWidth_, bool kSiluAct_, bool kIsVecLoad_, typename input_t_, typename weight_t_>
18
+ struct Causal_conv1d_bwd_kernel_traits {
19
+ using input_t = input_t_;
20
+ using weight_t = weight_t_;
21
+ static constexpr int kNThreads = kNThreads_;
22
+ static constexpr int kWidth = kWidth_;
23
+ static constexpr bool kSiluAct = kSiluAct_;
24
+ static constexpr int kNBytes = sizeof(input_t);
25
+ static_assert(kNBytes == 2 || kNBytes == 4);
26
+ static constexpr int kNElts = kNBytes == 4 ? 4 : 8;
27
+ static_assert(kWidth <= kNElts);
28
+ // It's possible that we need to do 2 rounds of exchange if input_t is 16 bits
29
+ // (since then we'd have 8 values of float, and each round we can exchange 4 floats).
30
+ static constexpr int kNExchangeRounds = sizeof(float) / sizeof(input_t);
31
+ static constexpr bool kIsVecLoad = kIsVecLoad_;
32
+ using vec_t = typename BytesToType<kNBytes * kNElts>::Type;
33
+ using BlockLoadT = cub::BlockLoad<input_t, kNThreads, kNElts, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
34
+ using BlockLoadVecT = cub::BlockLoad<vec_t, kNThreads, 1, cub::BLOCK_LOAD_DIRECT>;
35
+ using BlockStoreT = cub::BlockStore<input_t, kNThreads, kNElts, cub::BLOCK_STORE_WARP_TRANSPOSE>;
36
+ using BlockStoreVecT = cub::BlockStore<vec_t, kNThreads, 1, cub::BLOCK_STORE_DIRECT>;
37
+ using BlockReduceFloatT = cub::BlockReduce<float, kNThreads>;
38
+ static constexpr int kSmemIOSize = kIsVecLoad
39
+ ? 0
40
+ : std::max({sizeof(typename BlockLoadT::TempStorage), sizeof(typename BlockStoreT::TempStorage)});
41
+ static constexpr int kSmemExchangeSize = kNThreads * kNBytes * kNElts * (!kSiluAct ? 1 : kNExchangeRounds + 1);
42
+ static constexpr int kSmemSize = std::max({kSmemExchangeSize,
43
+ int(sizeof(typename BlockReduceFloatT::TempStorage))}) + (kIsVecLoad ? 0 : kSmemIOSize);
44
+ };
45
+
46
+ template<typename Ktraits>
47
+ __global__ __launch_bounds__(Ktraits::kNThreads)
48
+ void causal_conv1d_bwd_kernel(ConvParamsBwd params) {
49
+ constexpr int kWidth = Ktraits::kWidth;
50
+ constexpr int kNThreads = Ktraits::kNThreads;
51
+ constexpr bool kSiluAct = Ktraits::kSiluAct;
52
+ constexpr int kNElts = Ktraits::kNElts;
53
+ constexpr int kNExchangeRounds = Ktraits::kNExchangeRounds;
54
+ constexpr bool kIsVecLoad = Ktraits::kIsVecLoad;
55
+ using input_t = typename Ktraits::input_t;
56
+ using vec_t = typename Ktraits::vec_t;
57
+ using weight_t = typename Ktraits::weight_t;
58
+
59
+ // Shared memory.
60
+ extern __shared__ char smem_[];
61
+ auto& smem_load = reinterpret_cast<typename Ktraits::BlockLoadT::TempStorage&>(smem_);
62
+ auto& smem_load_vec = reinterpret_cast<typename Ktraits::BlockLoadVecT::TempStorage&>(smem_);
63
+ auto& smem_store = reinterpret_cast<typename Ktraits::BlockStoreT::TempStorage&>(smem_);
64
+ auto& smem_store_vec = reinterpret_cast<typename Ktraits::BlockStoreVecT::TempStorage&>(smem_);
65
+ vec_t *smem_exchange = reinterpret_cast<vec_t *>(smem_ + Ktraits::kSmemIOSize);
66
+ vec_t *smem_exchange_x = reinterpret_cast<vec_t *>(smem_ + Ktraits::kSmemIOSize) + kNThreads * kNExchangeRounds;
67
+ auto& smem_reduce_float = *reinterpret_cast<typename Ktraits::BlockReduceFloatT::TempStorage*>(smem_ + Ktraits::kSmemIOSize);
68
+
69
+ const int tidx = threadIdx.x;
70
+ const int batch_id = blockIdx.x;
71
+ const int dim_id = blockIdx.y;
72
+ input_t *x = reinterpret_cast<input_t *>(params.x_ptr) + batch_id * params.x_batch_stride
73
+ + dim_id * params.x_c_stride;
74
+ weight_t *weight = reinterpret_cast<weight_t *>(params.weight_ptr) + dim_id * params.weight_c_stride;
75
+ input_t *dout = reinterpret_cast<input_t *>(params.dout_ptr) + batch_id * params.dout_batch_stride
76
+ + dim_id * params.dout_c_stride;
77
+ input_t *dx = reinterpret_cast<input_t *>(params.dx_ptr) + batch_id * params.dx_batch_stride
78
+ + dim_id * params.dx_c_stride;
79
+ float *dweight = reinterpret_cast<float *>(params.dweight_ptr) + dim_id * params.dweight_c_stride;
80
+ float bias_val = params.bias_ptr == nullptr ? 0.f : float(reinterpret_cast<weight_t *>(params.bias_ptr)[dim_id]);
81
+
82
+ // Thread kNThreads - 1 will load the first elements of the next chunk so we initialize those to 0.
83
+ if (tidx == 0) {
84
+ if constexpr (!kSiluAct) {
85
+ input_t zeros[kNElts] = {0};
86
+ smem_exchange[0] = reinterpret_cast<vec_t *>(zeros)[0];
87
+ } else {
88
+ float zeros[kNElts] = {0};
89
+ #pragma unroll
90
+ for (int r = 0; r < kNExchangeRounds; ++r) {
91
+ smem_exchange[r * kNThreads] = reinterpret_cast<vec_t *>(zeros)[r];
92
+ }
93
+ }
94
+ }
95
+
96
+ float weight_vals[kWidth];
97
+ #pragma unroll
98
+ for (int i = 0; i < kWidth; ++i) { weight_vals[i] = weight[i * params.weight_width_stride]; }
99
+
100
+ float dweight_vals[kWidth] = {0};
101
+ float dbias_val = 0;
102
+
103
+ constexpr int kChunkSize = kNThreads * kNElts;
104
+ const int n_chunks = (params.seqlen + kChunkSize - 1) / kChunkSize;
105
+ x += (n_chunks - 1) * kChunkSize;
106
+ dout += (n_chunks - 1) * kChunkSize;
107
+ dx += (n_chunks - 1) * kChunkSize;
108
+ for (int chunk = n_chunks - 1; chunk >= 0; --chunk) {
109
+ input_t x_vals_load[2 * kNElts] = {0};
110
+ input_t dout_vals_load[2 * kNElts] = {0};
111
+ if constexpr(kIsVecLoad) {
112
+ Ktraits::BlockLoadVecT(smem_load_vec).Load(reinterpret_cast<vec_t*>(x), *reinterpret_cast<vec_t (*)[1]>(&x_vals_load[kNElts]), (params.seqlen - chunk * kChunkSize) / kNElts);
113
+ Ktraits::BlockLoadVecT(smem_load_vec).Load(reinterpret_cast<vec_t*>(dout), *reinterpret_cast<vec_t (*)[1]>(&dout_vals_load[0]), (params.seqlen - chunk * kChunkSize) / kNElts);
114
+ } else {
115
+ __syncthreads();
116
+ Ktraits::BlockLoadT(smem_load).Load(x, *reinterpret_cast<input_t (*)[kNElts]>(&x_vals_load[kNElts]), params.seqlen - chunk * kChunkSize);
117
+ __syncthreads();
118
+ Ktraits::BlockLoadT(smem_load).Load(dout, *reinterpret_cast<input_t (*)[kNElts]>(&dout_vals_load[0]), params.seqlen - chunk * kChunkSize);
119
+ }
120
+ float dout_vals[2 * kNElts], x_vals[2 * kNElts];
121
+ if constexpr (!kSiluAct) {
122
+ __syncthreads();
123
+ // Thread 0 don't write yet, so that thread kNThreads - 1 can read
124
+ // the first elements of the next chunk.
125
+ if (tidx > 0) { smem_exchange[tidx] = reinterpret_cast<vec_t *>(dout_vals_load)[0]; }
126
+ __syncthreads();
127
+ reinterpret_cast<vec_t *>(dout_vals_load)[1] = smem_exchange[tidx < kNThreads - 1 ? tidx + 1 : 0];
128
+ __syncthreads();
129
+ // Now thread 0 can write the first elements of the current chunk.
130
+ if (tidx == 0) { smem_exchange[tidx] = reinterpret_cast<vec_t *>(dout_vals_load)[0]; }
131
+ #pragma unroll
132
+ for (int i = 0; i < 2 * kNElts; ++i) {
133
+ dout_vals[i] = float(dout_vals_load[i]);
134
+ x_vals[i] = float(x_vals_load[i]);
135
+ }
136
+ } else {
137
+ if (tidx == 0 && chunk > 0) {
138
+ if constexpr(kIsVecLoad) {
139
+ reinterpret_cast<vec_t *>(x_vals_load)[0] = reinterpret_cast<vec_t *>(x)[-1];
140
+ } else {
141
+ #pragma unroll
142
+ for (int i = 0; i < kNElts; ++i) {
143
+ if (chunk * kChunkSize + i < params.seqlen) { x_vals_load[i] = x[-kNElts + i]; }
144
+ }
145
+ }
146
+ }
147
+ __syncthreads();
148
+ smem_exchange_x[tidx] = reinterpret_cast<vec_t *>(x_vals_load)[1];
149
+ __syncthreads();
150
+ if (tidx > 0) { reinterpret_cast<vec_t *>(x_vals_load)[0] = smem_exchange_x[tidx - 1]; }
151
+ #pragma unroll
152
+ for (int i = 0; i < 2 * kNElts; ++i) { x_vals[i] = float(x_vals_load[i]); }
153
+ // Recompute the output
154
+ #pragma unroll
155
+ for (int i = 0; i < kNElts; ++i) {
156
+ float out_val = bias_val;
157
+ #pragma unroll
158
+ for (int w = 0; w < kWidth; ++w) {
159
+ out_val += weight_vals[w] * x_vals[kNElts + i - (kWidth - w - 1)];
160
+ }
161
+ float out_sigmoid_val = 1.0f / (1.0f + expf(-out_val));
162
+ dout_vals[i] = float(dout_vals_load[i]) * out_sigmoid_val
163
+ * (1.0f + out_val * (1.0f - out_sigmoid_val));
164
+ }
165
+ // Exchange the dout_vals. It's possible that we need to do 2 rounds of exchange
166
+ // if input_t is 16 bits (since then we'd have 8 values of float)
167
+ __syncthreads();
168
+ // Thread 0 don't write yet, so that thread kNThreads - 1 can read
169
+ // the first elements of the next chunk.
170
+ if (tidx > 0) {
171
+ #pragma unroll
172
+ for (int r = 0; r < kNExchangeRounds; ++r) {
173
+ smem_exchange[r * kNThreads + tidx] = reinterpret_cast<vec_t *>(dout_vals)[r];
174
+ }
175
+ }
176
+ __syncthreads();
177
+ #pragma unroll
178
+ for (int r = 0; r < kNExchangeRounds; ++r) {
179
+ reinterpret_cast<vec_t *>(dout_vals)[kNExchangeRounds + r]
180
+ = smem_exchange[r * kNThreads + (tidx < kNThreads - 1 ? tidx + 1 : 0)];
181
+ }
182
+ __syncthreads();
183
+ // Now thread 0 can write the first elements of the current chunk.
184
+ if (tidx == 0) {
185
+ #pragma unroll
186
+ for (int r = 0; r < kNExchangeRounds; ++r) {
187
+ smem_exchange[r * kNThreads + tidx] = reinterpret_cast<vec_t *>(dout_vals)[r];
188
+ }
189
+ }
190
+ }
191
+ dout -= kChunkSize;
192
+ x -= kChunkSize;
193
+
194
+ #pragma unroll
195
+ for (int i = 0; i < kNElts; ++i) { dbias_val += dout_vals[i]; }
196
+
197
+ float dx_vals[kNElts] = {0};
198
+ #pragma unroll
199
+ for (int i = 0; i < kNElts; ++i) {
200
+ #pragma unroll
201
+ for (int w = 0; w < kWidth; ++w) {
202
+ dx_vals[i] += weight_vals[w] * dout_vals[i + kWidth - w - 1];
203
+ }
204
+ }
205
+
206
+ input_t dx_vals_store[kNElts];
207
+ #pragma unroll
208
+ for (int i = 0; i < kNElts; ++i) { dx_vals_store[i] = dx_vals[i]; }
209
+ if constexpr(kIsVecLoad) {
210
+ Ktraits::BlockStoreVecT(smem_store_vec).Store(reinterpret_cast<vec_t*>(dx), reinterpret_cast<vec_t (&)[1]>(dx_vals_store), (params.seqlen - chunk * kChunkSize) / kNElts);
211
+ } else {
212
+ Ktraits::BlockStoreT(smem_store).Store(dx, dx_vals_store, params.seqlen - chunk * kChunkSize);
213
+ }
214
+ dx -= kChunkSize;
215
+
216
+ #pragma unroll
217
+ for (int w = 0; w < kWidth; ++w) {
218
+ #pragma unroll
219
+ for (int i = 0; i < kNElts; ++i) {
220
+ dweight_vals[w] += x_vals[kNElts + i] * dout_vals[i + kWidth - w - 1];
221
+ }
222
+ }
223
+ }
224
+
225
+ #pragma unroll
226
+ for (int w = 0; w < kWidth; ++w) {
227
+ __syncthreads();
228
+ dweight_vals[w] = Ktraits::BlockReduceFloatT(smem_reduce_float).Sum(dweight_vals[w]);
229
+ if (tidx == 0) {
230
+ atomicAdd(&reinterpret_cast<float *>(dweight)[w * params.dweight_width_stride], dweight_vals[w]);
231
+ }
232
+ }
233
+ if (params.bias_ptr != nullptr) {
234
+ __syncthreads();
235
+ dbias_val = Ktraits::BlockReduceFloatT(smem_reduce_float).Sum(dbias_val);
236
+ if (tidx == 0) {
237
+ atomicAdd(&reinterpret_cast<float *>(params.dbias_ptr)[dim_id], dbias_val);
238
+ }
239
+ }
240
+ }
241
+
242
+ template<int kNThreads, int kWidth, typename input_t, typename weight_t>
243
+ void causal_conv1d_bwd_launch(ConvParamsBwd &params, cudaStream_t stream) {
244
+ static constexpr int kNElts = sizeof(input_t) == 4 ? 4 : 8;
245
+ BOOL_SWITCH(params.seqlen % kNElts == 0, kIsVecLoad, [&] {
246
+ BOOL_SWITCH(params.silu_activation, kSiluAct, [&] {
247
+ using Ktraits = Causal_conv1d_bwd_kernel_traits<kNThreads, kWidth, kSiluAct, kIsVecLoad, input_t, weight_t>;
248
+ constexpr int kSmemSize = Ktraits::kSmemSize;
249
+ dim3 grid(params.batch, params.dim);
250
+ auto kernel = &causal_conv1d_bwd_kernel<Ktraits>;
251
+ if (kSmemSize >= 48 * 1024) {
252
+ C10_CUDA_CHECK(cudaFuncSetAttribute(
253
+ kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
254
+ }
255
+ kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
256
+ C10_CUDA_KERNEL_LAUNCH_CHECK();
257
+ });
258
+ });
259
+ }
260
+
261
+ template<typename input_t, typename weight_t>
262
+ void causal_conv1d_bwd_cuda(ConvParamsBwd &params, cudaStream_t stream) {
263
+ if (params.width == 2) {
264
+ causal_conv1d_bwd_launch<128, 2, input_t, weight_t>(params, stream);
265
+ } else if (params.width == 3) {
266
+ causal_conv1d_bwd_launch<128, 3, input_t, weight_t>(params, stream);
267
+ } else if (params.width == 4) {
268
+ causal_conv1d_bwd_launch<128, 4, input_t, weight_t>(params, stream);
269
+ }
270
+ }
271
+
272
+ template<int kNThreads_, int kWidth_, int kChunkSizeL_, bool kSiluAct_, bool kIsVecLoad_, typename input_t_, typename weight_t_>
273
+ struct Causal_conv1d_channellast_bwd_kernel_traits {
274
+ // The cache line is 128 bytes, and we try to read 16 bytes per thread.
275
+ // So we have 8 threads per "row", so 32 or 64 elements in the channel dimension.
276
+ // That leaves 4 columns per warp, and so 16 columns per block (assuming each block has 128
277
+ // threads). Each each load is 16 x 32|64 elements in the L x C dimensions.
278
+ using input_t = input_t_;
279
+ using weight_t = weight_t_;
280
+ static constexpr bool kSiluAct = kSiluAct_;
281
+ static constexpr int kNThreads = kNThreads_;
282
+ static_assert(kNThreads % 32 == 0);
283
+ static constexpr int kNWarps = kNThreads / 32;
284
+ static constexpr int kWidth = kWidth_;
285
+ static constexpr int kChunkSizeL = kChunkSizeL_;
286
+ static constexpr int kNBytes = sizeof(input_t);
287
+ static_assert(kNBytes == 2 || kNBytes == 4);
288
+ static constexpr int kNElts = kNBytes == 4 ? 4 : 8;
289
+ static constexpr int kNEltsPerRow = 128 / kNBytes;
290
+ static constexpr int kNThreadsPerRow = kNEltsPerRow / kNElts; // Always 8 for now
291
+ static_assert(kNThreadsPerRow * kNBytes * kNElts == 128);
292
+ static constexpr int kNColsPerWarp = 32 / kNThreadsPerRow; // Always 4 for now
293
+ static_assert(kNColsPerWarp * kNThreadsPerRow == 32);
294
+ static constexpr int kNColsPerLoad = kNColsPerWarp * kNWarps;
295
+ static constexpr int kNLoads = kChunkSizeL / kNColsPerLoad;
296
+ static_assert(kNLoads * kNColsPerLoad == kChunkSizeL);
297
+ static constexpr bool kIsVecLoad = kIsVecLoad_;
298
+ using vec_t = typename BytesToType<kNBytes * kNElts>::Type;
299
+ // using BlockLoadT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
300
+ // using BlockStoreT = cub::BlockStore<input_t, kNThreads, kNItems, cub::BLOCK_STORE_WARP_TRANSPOSE>;
301
+ // static constexpr int kSmemSize = std::max({sizeof(typename BlockLoadT::TempStorage),
302
+ // sizeof(typename BlockStoreT::TempStorage)});
303
+ // static constexpr int kSmemSize = kChunkSizeL * kNEltsPerRow * kNBytes;
304
+ };
305
+
306
+ template<typename Ktraits>
307
+ __global__ __launch_bounds__(Ktraits::kNThreads)
308
+ void causal_conv1d_channellast_bwd_kernel(ConvParamsBwd params) {
309
+ constexpr int kWidth = Ktraits::kWidth;
310
+ constexpr int kNThreads = Ktraits::kNThreads;
311
+ constexpr bool kSiluAct = Ktraits::kSiluAct;
312
+ constexpr int kNElts = Ktraits::kNElts;
313
+ constexpr int kNWarp = Ktraits::kNWarps;
314
+ constexpr int kNThreadsPerC = Ktraits::kNThreadsPerRow;
315
+ constexpr int kLPerLoad = Ktraits::kNColsPerLoad;
316
+ constexpr int kChunkSizeL = Ktraits::kChunkSizeL;
317
+ constexpr int kChunkSizeC = Ktraits::kNEltsPerRow;
318
+ using input_t = typename Ktraits::input_t;
319
+ using vec_t = typename Ktraits::vec_t;
320
+ using weight_t = typename Ktraits::weight_t;
321
+
322
+ // Shared memory.
323
+ __shared__ input_t dout_smem[kChunkSizeL + kWidth - 1][kChunkSizeC + kNElts];
324
+ __shared__ input_t x_smem[kWidth - 1 + kChunkSizeL + kWidth - 1][kChunkSizeC + kNElts];
325
+
326
+ const int tid = threadIdx.x;
327
+ const int l_idx = tid / kNThreadsPerC;
328
+ const int c_idx = tid % kNThreadsPerC;
329
+ const int batch_id = blockIdx.x;
330
+ const int chunk_l_id = blockIdx.y;
331
+ const int chunk_c_id = blockIdx.z;
332
+ input_t *x = reinterpret_cast<input_t *>(params.x_ptr) + batch_id * params.x_batch_stride
333
+ + (chunk_l_id * kChunkSizeL + l_idx) * params.x_l_stride + chunk_c_id * kChunkSizeC + c_idx * kNElts;
334
+ weight_t *weight = reinterpret_cast<weight_t *>(params.weight_ptr)
335
+ + chunk_c_id * kChunkSizeC * params.weight_c_stride;
336
+ input_t *dout = reinterpret_cast<input_t *>(params.dout_ptr) + batch_id * params.dout_batch_stride
337
+ + (chunk_l_id * kChunkSizeL + l_idx) * params.dout_l_stride + chunk_c_id * kChunkSizeC + c_idx * kNElts;
338
+ input_t *dx = reinterpret_cast<input_t *>(params.dx_ptr) + batch_id * params.dx_batch_stride
339
+ + (chunk_l_id * kChunkSizeL + l_idx) * params.dx_l_stride + chunk_c_id * kChunkSizeC + c_idx * kNElts;
340
+ float *dweight = reinterpret_cast<float *>(params.dweight_ptr)
341
+ + chunk_c_id * kChunkSizeC * params.dweight_c_stride;
342
+
343
+ #pragma unroll
344
+ for (int l = 0; l < Ktraits::kNLoads; ++l) {
345
+ input_t dout_vals_load[kNElts] = {0};
346
+ input_t x_vals_load[kNElts] = {0};
347
+ if (chunk_l_id * kChunkSizeL + l * kLPerLoad + l_idx < params.seqlen
348
+ && chunk_c_id * kChunkSizeC + c_idx * kNElts < params.dim) {
349
+ reinterpret_cast<vec_t *>(dout_vals_load)[0] = *reinterpret_cast<vec_t *>(dout + l * kLPerLoad * params.dout_l_stride);
350
+ reinterpret_cast<vec_t *>(x_vals_load)[0] = *reinterpret_cast<vec_t *>(x + l * kLPerLoad * params.x_l_stride);
351
+ }
352
+ reinterpret_cast<vec_t *>(dout_smem[l * kLPerLoad + l_idx])[c_idx] = reinterpret_cast<vec_t *>(dout_vals_load)[0];
353
+ reinterpret_cast<vec_t *>(x_smem[kWidth - 1 + l * kLPerLoad + l_idx])[c_idx] = reinterpret_cast<vec_t *>(x_vals_load)[0];
354
+ }
355
+ // Load the elements from the previous chunk or next chunk that are needed for convolution.
356
+ if (l_idx < kWidth - 1) {
357
+ input_t dout_vals_load[kNElts] = {0};
358
+ input_t x_vals_load[kNElts] = {0};
359
+ if ((chunk_l_id + 1) * kChunkSizeL + l_idx < params.seqlen
360
+ && chunk_c_id * kChunkSizeC + c_idx * kNElts < params.dim) {
361
+ reinterpret_cast<vec_t *>(dout_vals_load)[0] = *reinterpret_cast<vec_t *>(dout + kChunkSizeL * params.dout_l_stride);
362
+ }
363
+ if (chunk_l_id * kChunkSizeL + l_idx - (kWidth - 1) >= 0
364
+ && chunk_l_id * kChunkSizeL + l_idx - (kWidth - 1) < params.seqlen
365
+ && chunk_c_id * kChunkSizeC + c_idx * kNElts < params.dim) {
366
+ reinterpret_cast<vec_t *>(x_vals_load)[0] = *reinterpret_cast<vec_t *>(x - (kWidth - 1) * params.x_l_stride);
367
+ }
368
+ reinterpret_cast<vec_t *>(dout_smem[kChunkSizeL + l_idx])[c_idx] = reinterpret_cast<vec_t *>(dout_vals_load)[0];
369
+ reinterpret_cast<vec_t *>(x_smem[l_idx])[c_idx] = reinterpret_cast<vec_t *>(x_vals_load)[0];
370
+ }
371
+ // Need to load (kWdith - 1) extra x's on the right to recompute the (kChunkSizeL + kWidth - 1) outputs
372
+ if constexpr (kSiluAct) {
373
+ if (l_idx < kWidth - 1) {
374
+ input_t x_vals_load[kNElts] = {0};
375
+ if ((chunk_l_id + 1) * kChunkSizeL + l_idx < params.seqlen
376
+ && chunk_c_id * kChunkSizeC + c_idx * kNElts < params.dim) {
377
+ reinterpret_cast<vec_t *>(x_vals_load)[0] = *reinterpret_cast<vec_t *>(x + kChunkSizeL * params.x_l_stride);
378
+ }
379
+ reinterpret_cast<vec_t *>(x_smem[kWidth - 1 + kChunkSizeL + l_idx])[c_idx] = reinterpret_cast<vec_t *>(x_vals_load)[0];
380
+ }
381
+ }
382
+
383
+ __syncthreads();
384
+
385
+ constexpr int kLPerThread = std::min(kChunkSizeL * kChunkSizeC / kNThreads, kChunkSizeL);
386
+ static_assert(kLPerThread * kNThreads == kChunkSizeL * kChunkSizeC);
387
+ constexpr int kNThreadsPerRow = kChunkSizeL / kLPerThread;
388
+ static_assert(kNThreadsPerRow * kLPerThread == kChunkSizeL);
389
+ // kChunkSizeL, kLPerThread, kNThreadsPerRow should be powers of 2 for simplicity
390
+ static_assert((kChunkSizeL & (kChunkSizeL - 1)) == 0);
391
+ static_assert((kLPerThread & (kLPerThread - 1)) == 0);
392
+ static_assert((kNThreadsPerRow & (kNThreadsPerRow - 1)) == 0);
393
+ static_assert(kNThreadsPerRow <= 32);
394
+
395
+ const int row_idx = tid / kNThreadsPerRow;
396
+ const int col_idx = tid % kNThreadsPerRow;
397
+
398
+ float bias_val = params.bias_ptr == nullptr || chunk_c_id * kChunkSizeC + row_idx >= params.dim ? 0.f : float(reinterpret_cast<weight_t *>(params.bias_ptr)[chunk_c_id * kChunkSizeC + row_idx]);
399
+ float weight_vals[kWidth] = {0};
400
+ if (chunk_c_id * kChunkSizeC + row_idx < params.dim) {
401
+ #pragma unroll
402
+ for (int w = 0; w < kWidth; ++w) {
403
+ weight_vals[w] = weight[row_idx * params.weight_c_stride + w * params.weight_width_stride];
404
+ }
405
+ }
406
+ float dout_vals[kLPerThread + kWidth - 1];
407
+ float x_vals[kWidth - 1 + kLPerThread + kWidth - 1];
408
+ #pragma unroll
409
+ for (int i = 0; i < kWidth - 1 + kLPerThread; ++i) {
410
+ dout_vals[i] = float(dout_smem[col_idx * kLPerThread + i][row_idx]);
411
+ x_vals[i] = float(x_smem[col_idx * kLPerThread + i][row_idx]);
412
+ }
413
+
414
+ if constexpr (kSiluAct) { // Recompute the output
415
+ #pragma unroll
416
+ for (int i = kWidth - 1 + kLPerThread; i < kWidth - 1 + kLPerThread + kWidth - 1; ++i) {
417
+ x_vals[i] = float(x_smem[col_idx * kLPerThread + i][row_idx]);
418
+ }
419
+ #pragma unroll
420
+ for (int i = 0; i < kLPerThread + kWidth - 1; ++i) {
421
+ float out_val = bias_val;
422
+ #pragma unroll
423
+ for (int w = 0; w < kWidth; ++w) { out_val += weight_vals[w] * x_vals[i + w]; }
424
+ float out_val_sigmoid = 1.f / (1.f + expf(-out_val));
425
+ dout_vals[i] *= out_val_sigmoid * (1 + out_val * (1 - out_val_sigmoid));
426
+ }
427
+ }
428
+
429
+ float dweight_vals[kWidth] = {0};
430
+ SumOp<float> sum_op;
431
+ #pragma unroll
432
+ for (int w = 0; w < kWidth; ++w) {
433
+ #pragma unroll
434
+ for (int i = 0; i < kLPerThread; ++i) { dweight_vals[w] += x_vals[i + w] * dout_vals[i]; }
435
+ dweight_vals[w] = Allreduce<kNThreadsPerRow>::run(dweight_vals[w], sum_op);
436
+ if (col_idx == 0 && chunk_c_id * kChunkSizeC + row_idx < params.dim) {
437
+ atomicAdd(&reinterpret_cast<float *>(dweight)[row_idx * params.dweight_c_stride + w * params.dweight_width_stride], dweight_vals[w]);
438
+ }
439
+ }
440
+
441
+ if (params.bias_ptr != nullptr) {
442
+ float dbias_val = 0.f;
443
+ for (int i = 0; i < kLPerThread; ++i) { dbias_val += dout_vals[i]; }
444
+ dbias_val = Allreduce<kNThreadsPerRow>::run(dbias_val, sum_op);
445
+ if (col_idx == 0 && chunk_c_id * kChunkSizeC + row_idx < params.dim) {
446
+ atomicAdd(&reinterpret_cast<float *>(params.dbias_ptr)[chunk_c_id * kChunkSizeC + row_idx], dbias_val);
447
+ }
448
+ }
449
+
450
+ float dx_vals[kLPerThread] = {0};
451
+ #pragma unroll
452
+ for (int i = 0; i < kLPerThread; ++i) {
453
+ #pragma unroll
454
+ for (int w = 0; w < kWidth; ++w) { dx_vals[i] += weight_vals[kWidth - 1 - w] * dout_vals[i + w]; }
455
+ }
456
+ // Since kNThreadsPerRow is a power of 2 and <= 32, we only need syncwarp and not syncthreads.
457
+ __syncwarp();
458
+ #pragma unroll
459
+ for (int i = 0; i < kLPerThread; ++i) { x_smem[col_idx * kLPerThread + i][row_idx] = dx_vals[i]; }
460
+ __syncthreads();
461
+
462
+ #pragma unroll
463
+ for (int l = 0; l < Ktraits::kNLoads; ++l) {
464
+ input_t dx_vals_store[kNElts];
465
+ reinterpret_cast<vec_t *>(dx_vals_store)[0] = reinterpret_cast<vec_t *>(x_smem[l * kLPerLoad + l_idx])[c_idx];
466
+ if (chunk_l_id * kChunkSizeL + l * kLPerLoad + l_idx < params.seqlen
467
+ && chunk_c_id * kChunkSizeC + c_idx * kNElts < params.dim) {
468
+ *reinterpret_cast<vec_t *>(dx + l * kLPerLoad * params.dx_l_stride) = reinterpret_cast<vec_t *>(dx_vals_store)[0];
469
+ }
470
+ }
471
+
472
+ }
473
+
474
+ template<int kNThreads, int kWidth, typename input_t, typename weight_t>
475
+ void causal_conv1d_channellast_bwd_launch(ConvParamsBwd &params, cudaStream_t stream) {
476
+ BOOL_SWITCH(params.silu_activation, kSiluAct, [&] {
477
+ using Ktraits = Causal_conv1d_channellast_bwd_kernel_traits<kNThreads, kWidth, 64, kSiluAct, true, input_t, weight_t>;
478
+ // constexpr int kSmemSize = Ktraits::kSmemSize;
479
+ constexpr int kChunkSizeL = Ktraits::kChunkSizeL;
480
+ constexpr int kChunkSizeC = Ktraits::kNEltsPerRow;
481
+ const int n_chunks_L = (params.seqlen + kChunkSizeL - 1) / kChunkSizeL;
482
+ const int n_chunks_C = (params.dim + kChunkSizeC - 1) / kChunkSizeC;
483
+ dim3 grid(params.batch, n_chunks_L, n_chunks_C);
484
+ dim3 block(Ktraits::kNThreads);
485
+ auto kernel = &causal_conv1d_channellast_bwd_kernel<Ktraits>;
486
+ // if (kSmemSize >= 48 * 1024) {
487
+ // C10_CUDA_CHECK(cudaFuncSetAttribute(
488
+ // kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
489
+ // }
490
+ // kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
491
+ kernel<<<grid, Ktraits::kNThreads, 0, stream>>>(params);
492
+ C10_CUDA_KERNEL_LAUNCH_CHECK();
493
+ });
494
+ }
495
+
496
+ template<typename input_t, typename weight_t>
497
+ void causal_conv1d_channellast_bwd_cuda(ConvParamsBwd &params, cudaStream_t stream) {
498
+ if (params.width == 2) {
499
+ causal_conv1d_channellast_bwd_launch<128, 2, input_t, weight_t>(params, stream);
500
+ } else if (params.width == 3) {
501
+ causal_conv1d_channellast_bwd_launch<128, 3, input_t, weight_t>(params, stream);
502
+ } else if (params.width == 4) {
503
+ causal_conv1d_channellast_bwd_launch<128, 4, input_t, weight_t>(params, stream);
504
+ }
505
+ }
506
+
507
+ template void causal_conv1d_bwd_cuda<float, float>(ConvParamsBwd &params, cudaStream_t stream);
508
+ template void causal_conv1d_bwd_cuda<at::Half, float>(ConvParamsBwd &params, cudaStream_t stream);
509
+ template void causal_conv1d_bwd_cuda<at::BFloat16, float>(ConvParamsBwd &params, cudaStream_t stream);
510
+ template void causal_conv1d_bwd_cuda<float, at::Half>(ConvParamsBwd &params, cudaStream_t stream);
511
+ template void causal_conv1d_bwd_cuda<at::Half, at::Half>(ConvParamsBwd &params, cudaStream_t stream);
512
+ template void causal_conv1d_bwd_cuda<at::BFloat16, at::Half>(ConvParamsBwd &params, cudaStream_t stream);
513
+ template void causal_conv1d_bwd_cuda<float, at::BFloat16>(ConvParamsBwd &params, cudaStream_t stream);
514
+ template void causal_conv1d_bwd_cuda<at::Half, at::BFloat16>(ConvParamsBwd &params, cudaStream_t stream);
515
+ template void causal_conv1d_bwd_cuda<at::BFloat16, at::BFloat16>(ConvParamsBwd &params, cudaStream_t stream);
516
+
517
+ template void causal_conv1d_channellast_bwd_cuda<float, float>(ConvParamsBwd &params, cudaStream_t stream);
518
+ template void causal_conv1d_channellast_bwd_cuda<at::Half, float>(ConvParamsBwd &params, cudaStream_t stream);
519
+ template void causal_conv1d_channellast_bwd_cuda<at::BFloat16, float>(ConvParamsBwd &params, cudaStream_t stream);
520
+ template void causal_conv1d_channellast_bwd_cuda<float, at::Half>(ConvParamsBwd &params, cudaStream_t stream);
521
+ template void causal_conv1d_channellast_bwd_cuda<at::Half, at::Half>(ConvParamsBwd &params, cudaStream_t stream);
522
+ template void causal_conv1d_channellast_bwd_cuda<at::BFloat16, at::Half>(ConvParamsBwd &params, cudaStream_t stream);
523
+ template void causal_conv1d_channellast_bwd_cuda<float, at::BFloat16>(ConvParamsBwd &params, cudaStream_t stream);
524
+ template void causal_conv1d_channellast_bwd_cuda<at::Half, at::BFloat16>(ConvParamsBwd &params, cudaStream_t stream);
525
+ template void causal_conv1d_channellast_bwd_cuda<at::BFloat16, at::BFloat16>(ConvParamsBwd &params, cudaStream_t stream);
protein_lm/modeling/models/libs/causal-conv1d/csrc/causal_conv1d_common.h ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ #pragma once
6
+
7
+ #include <cuda_bf16.h>
8
+ #include <cuda_fp16.h>
9
+
10
+ ////////////////////////////////////////////////////////////////////////////////////////////////////
11
+
12
+ template<int BYTES> struct BytesToType {};
13
+
14
+ template<> struct BytesToType<16> {
15
+ using Type = uint4;
16
+ static_assert(sizeof(Type) == 16);
17
+ };
18
+
19
+ template<> struct BytesToType<8> {
20
+ using Type = uint64_t;
21
+ static_assert(sizeof(Type) == 8);
22
+ };
23
+
24
+ template<> struct BytesToType<4> {
25
+ using Type = uint32_t;
26
+ static_assert(sizeof(Type) == 4);
27
+ };
28
+
29
+ template<> struct BytesToType<2> {
30
+ using Type = uint16_t;
31
+ static_assert(sizeof(Type) == 2);
32
+ };
33
+
34
+ template<> struct BytesToType<1> {
35
+ using Type = uint8_t;
36
+ static_assert(sizeof(Type) == 1);
37
+ };
38
+
39
+ ////////////////////////////////////////////////////////////////////////////////////////////////////
40
+
41
+ template<typename T>
42
+ struct SumOp {
43
+ __device__ inline T operator()(T const & x, T const & y) { return x + y; }
44
+ };
45
+
46
+ template<int THREADS>
47
+ struct Allreduce {
48
+ static_assert(THREADS == 32 || THREADS == 16 || THREADS == 8 || THREADS == 4);
49
+ template<typename T, typename Operator>
50
+ static __device__ inline T run(T x, Operator &op) {
51
+ constexpr int OFFSET = THREADS / 2;
52
+ x = op(x, __shfl_xor_sync(uint32_t(-1), x, OFFSET));
53
+ return Allreduce<OFFSET>::run(x, op);
54
+ }
55
+ };
56
+
57
+ template<>
58
+ struct Allreduce<2> {
59
+ template<typename T, typename Operator>
60
+ static __device__ inline T run(T x, Operator &op) {
61
+ x = op(x, __shfl_xor_sync(uint32_t(-1), x, 1));
62
+ return x;
63
+ }
64
+ };
protein_lm/modeling/models/libs/causal-conv1d/csrc/causal_conv1d_fwd.cu ADDED
@@ -0,0 +1,350 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ #include <c10/util/BFloat16.h>
6
+ #include <c10/util/Half.h>
7
+ #include <c10/cuda/CUDAException.h> // For C10_CUDA_CHECK and C10_CUDA_KERNEL_LAUNCH_CHECK
8
+
9
+ #include <cub/block/block_load.cuh>
10
+ #include <cub/block/block_store.cuh>
11
+
12
+ #include "causal_conv1d.h"
13
+ #include "causal_conv1d_common.h"
14
+ #include "static_switch.h"
15
+
16
+ template<int kNThreads_, int kWidth_, bool kIsVecLoad_, typename input_t_, typename weight_t_>
17
+ struct Causal_conv1d_fwd_kernel_traits {
18
+ using input_t = input_t_;
19
+ using weight_t = weight_t_;
20
+ static constexpr int kNThreads = kNThreads_;
21
+ static constexpr int kWidth = kWidth_;
22
+ static constexpr int kNBytes = sizeof(input_t);
23
+ static_assert(kNBytes == 2 || kNBytes == 4);
24
+ static constexpr int kNElts = kNBytes == 4 ? 4 : 8;
25
+ static_assert(kWidth <= kNElts);
26
+ static constexpr bool kIsVecLoad = kIsVecLoad_;
27
+ using vec_t = typename BytesToType<kNBytes * kNElts>::Type;
28
+ using BlockLoadT = cub::BlockLoad<input_t, kNThreads, kNElts, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
29
+ using BlockLoadVecT = cub::BlockLoad<vec_t, kNThreads, 1, cub::BLOCK_LOAD_DIRECT>;
30
+ using BlockStoreT = cub::BlockStore<input_t, kNThreads, kNElts, cub::BLOCK_STORE_WARP_TRANSPOSE>;
31
+ using BlockStoreVecT = cub::BlockStore<vec_t, kNThreads, 1, cub::BLOCK_STORE_DIRECT>;
32
+ static constexpr int kSmemIOSize = kIsVecLoad
33
+ ? 0
34
+ : std::max({sizeof(typename BlockLoadT::TempStorage), sizeof(typename BlockStoreT::TempStorage)});
35
+ static constexpr int kSmemExchangeSize = kNThreads * kNBytes * kNElts;
36
+ static constexpr int kSmemSize = kSmemIOSize + kSmemExchangeSize;
37
+ };
38
+
39
+ template<typename Ktraits>
40
+ __global__ __launch_bounds__(Ktraits::kNThreads)
41
+ void causal_conv1d_fwd_kernel(ConvParamsBase params) {
42
+ constexpr int kWidth = Ktraits::kWidth;
43
+ constexpr int kNThreads = Ktraits::kNThreads;
44
+ constexpr int kNElts = Ktraits::kNElts;
45
+ constexpr bool kIsVecLoad = Ktraits::kIsVecLoad;
46
+ using input_t = typename Ktraits::input_t;
47
+ using vec_t = typename Ktraits::vec_t;
48
+ using weight_t = typename Ktraits::weight_t;
49
+
50
+ // Shared memory.
51
+ extern __shared__ char smem_[];
52
+ auto& smem_load = reinterpret_cast<typename Ktraits::BlockLoadT::TempStorage&>(smem_);
53
+ auto& smem_load_vec = reinterpret_cast<typename Ktraits::BlockLoadVecT::TempStorage&>(smem_);
54
+ auto& smem_store = reinterpret_cast<typename Ktraits::BlockStoreT::TempStorage&>(smem_);
55
+ auto& smem_store_vec = reinterpret_cast<typename Ktraits::BlockStoreVecT::TempStorage&>(smem_);
56
+ vec_t *smem_exchange = reinterpret_cast<vec_t *>(smem_ + Ktraits::kSmemIOSize);
57
+
58
+ const int tidx = threadIdx.x;
59
+ const int batch_id = blockIdx.x;
60
+ const int channel_id = blockIdx.y;
61
+ input_t *x = reinterpret_cast<input_t *>(params.x_ptr) + batch_id * params.x_batch_stride
62
+ + channel_id * params.x_c_stride;
63
+ weight_t *weight = reinterpret_cast<weight_t *>(params.weight_ptr) + channel_id * params.weight_c_stride;
64
+ input_t *out = reinterpret_cast<input_t *>(params.out_ptr) + batch_id * params.out_batch_stride
65
+ + channel_id * params.out_c_stride;
66
+ float bias_val = params.bias_ptr == nullptr ? 0.f : float(reinterpret_cast<weight_t *>(params.bias_ptr)[channel_id]);
67
+
68
+ // Thread 0 will load the last elements of the previous chunk, so we initialize those to 0.
69
+ if (tidx == 0) {
70
+ input_t zeros[kNElts] = {0};
71
+ smem_exchange[kNThreads - 1] = reinterpret_cast<vec_t *>(zeros)[0];
72
+ }
73
+
74
+ float weight_vals[kWidth];
75
+ #pragma unroll
76
+ for (int i = 0; i < kWidth; ++i) { weight_vals[i] = float(weight[i * params.weight_width_stride]); }
77
+
78
+ constexpr int kChunkSize = kNThreads * kNElts;
79
+ const int n_chunks = (params.seqlen + kChunkSize - 1) / kChunkSize;
80
+ for (int chunk = 0; chunk < n_chunks; ++chunk) {
81
+ input_t x_vals_load[2 * kNElts] = {0};
82
+ if constexpr(kIsVecLoad) {
83
+ Ktraits::BlockLoadVecT(smem_load_vec).Load(reinterpret_cast<vec_t*>(x), *reinterpret_cast<vec_t (*)[1]>(&x_vals_load[kNElts]), (params.seqlen - chunk * kChunkSize) / kNElts);
84
+ } else {
85
+ __syncthreads();
86
+ Ktraits::BlockLoadT(smem_load).Load(x, *reinterpret_cast<input_t (*)[kNElts]>(&x_vals_load[kNElts]), params.seqlen - chunk * kChunkSize);
87
+ }
88
+ x += kChunkSize;
89
+ __syncthreads();
90
+ // Thread kNThreads - 1 don't write yet, so that thread 0 can read
91
+ // the last elements of the previous chunk.
92
+ if (tidx < kNThreads - 1) { smem_exchange[tidx] = reinterpret_cast<vec_t *>(x_vals_load)[1]; }
93
+ __syncthreads();
94
+ reinterpret_cast<vec_t *>(x_vals_load)[0] = smem_exchange[tidx > 0 ? tidx - 1 : kNThreads - 1];
95
+ __syncthreads();
96
+ // Now thread kNThreads - 1 can write the last elements of the current chunk.
97
+ if (tidx == kNThreads - 1) { smem_exchange[tidx] = reinterpret_cast<vec_t *>(x_vals_load)[1]; }
98
+
99
+ float x_vals[2 * kNElts];
100
+ #pragma unroll
101
+ for (int i = 0; i < 2 * kNElts; ++i) { x_vals[i] = float(x_vals_load[i]); }
102
+
103
+ float out_vals[kNElts];
104
+ #pragma unroll
105
+ for (int i = 0; i < kNElts; ++i) {
106
+ out_vals[i] = bias_val;
107
+ #pragma unroll
108
+ for (int w = 0; w < kWidth; ++w) {
109
+ out_vals[i] += weight_vals[w] * x_vals[kNElts + i - (kWidth - w - 1)];
110
+ }
111
+ }
112
+
113
+ if (params.silu_activation) {
114
+ #pragma unroll
115
+ for (int i = 0; i < kNElts; ++i) {
116
+ out_vals[i] = out_vals[i] / (1 + expf(-out_vals[i]));
117
+ }
118
+ }
119
+
120
+ input_t out_vals_store[kNElts];
121
+ #pragma unroll
122
+ for (int i = 0; i < kNElts; ++i) { out_vals_store[i] = out_vals[i]; }
123
+ if constexpr(kIsVecLoad) {
124
+ Ktraits::BlockStoreVecT(smem_store_vec).Store(reinterpret_cast<vec_t*>(out), reinterpret_cast<vec_t (&)[1]>(out_vals_store), (params.seqlen - chunk * kChunkSize) / kNElts);
125
+ } else {
126
+ Ktraits::BlockStoreT(smem_store).Store(out, out_vals_store, params.seqlen - chunk * kChunkSize);
127
+ }
128
+ out += kChunkSize;
129
+ }
130
+ }
131
+
132
+ template<int kNThreads, int kWidth, typename input_t, typename weight_t>
133
+ void causal_conv1d_fwd_launch(ConvParamsBase &params, cudaStream_t stream) {
134
+ static constexpr int kNElts = sizeof(input_t) == 4 ? 4 : 8;
135
+ BOOL_SWITCH(params.seqlen % kNElts == 0, kIsVecLoad, [&] {
136
+ using Ktraits = Causal_conv1d_fwd_kernel_traits<kNThreads, kWidth, kIsVecLoad, input_t, weight_t>;
137
+ constexpr int kSmemSize = Ktraits::kSmemSize;
138
+ dim3 grid(params.batch, params.dim);
139
+ auto kernel = &causal_conv1d_fwd_kernel<Ktraits>;
140
+ if (kSmemSize >= 48 * 1024) {
141
+ C10_CUDA_CHECK(cudaFuncSetAttribute(
142
+ kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
143
+ }
144
+ kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
145
+ C10_CUDA_KERNEL_LAUNCH_CHECK();
146
+ });
147
+ }
148
+
149
+ template<typename input_t, typename weight_t>
150
+ void causal_conv1d_fwd_cuda(ConvParamsBase &params, cudaStream_t stream) {
151
+ if (params.width == 2) {
152
+ causal_conv1d_fwd_launch<128, 2, input_t, weight_t>(params, stream);
153
+ } else if (params.width == 3) {
154
+ causal_conv1d_fwd_launch<128, 3, input_t, weight_t>(params, stream);
155
+ } else if (params.width == 4) {
156
+ causal_conv1d_fwd_launch<128, 4, input_t, weight_t>(params, stream);
157
+ }
158
+ }
159
+
160
+ template<int kNThreads_, int kWidth_, int kChunkSizeL_, bool kIsVecLoad_, typename input_t_, typename weight_t_>
161
+ struct Causal_conv1d_channellast_fwd_kernel_traits {
162
+ // The cache line is 128 bytes, and we try to read 16 bytes per thread.
163
+ // So we have 8 threads per "row", so 32 or 64 elements in the channel dimension.
164
+ // That leaves 4 columns per warp, and so 16 columns per block (assuming each block has 128
165
+ // threads). Each each load is 16 x 32|64 elements in the L x C dimensions.
166
+ using input_t = input_t_;
167
+ using weight_t = weight_t_;
168
+ static constexpr int kNThreads = kNThreads_;
169
+ static_assert(kNThreads % 32 == 0);
170
+ static constexpr int kNWarps = kNThreads / 32;
171
+ static constexpr int kWidth = kWidth_;
172
+ static constexpr int kChunkSizeL = kChunkSizeL_;
173
+ static constexpr int kNBytes = sizeof(input_t);
174
+ static_assert(kNBytes == 2 || kNBytes == 4);
175
+ static constexpr int kNElts = kNBytes == 4 ? 4 : 8;
176
+ static constexpr int kNEltsPerRow = 128 / kNBytes;
177
+ static constexpr int kNThreadsPerRow = kNEltsPerRow / kNElts; // Always 8 for now
178
+ static_assert(kNThreadsPerRow * kNBytes * kNElts == 128);
179
+ static constexpr int kNColsPerWarp = 32 / kNThreadsPerRow; // Always 4 for now
180
+ static_assert(kNColsPerWarp * kNThreadsPerRow == 32);
181
+ static constexpr int kNColsPerLoad = kNColsPerWarp * kNWarps;
182
+ static constexpr int kNLoads = kChunkSizeL / kNColsPerLoad;
183
+ static_assert(kNLoads * kNColsPerLoad == kChunkSizeL);
184
+ static constexpr bool kIsVecLoad = kIsVecLoad_;
185
+ using vec_t = typename BytesToType<kNBytes * kNElts>::Type;
186
+ // using BlockLoadT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
187
+ // using BlockStoreT = cub::BlockStore<input_t, kNThreads, kNItems, cub::BLOCK_STORE_WARP_TRANSPOSE>;
188
+ // static constexpr int kSmemSize = std::max({sizeof(typename BlockLoadT::TempStorage),
189
+ // sizeof(typename BlockStoreT::TempStorage)});
190
+ // static constexpr int kSmemSize = kChunkSizeL * kNEltsPerRow * kNBytes;
191
+ };
192
+
193
+ template<typename Ktraits>
194
+ __global__ __launch_bounds__(Ktraits::kNThreads)
195
+ void causal_conv1d_channellast_fwd_kernel(ConvParamsBase params) {
196
+ constexpr int kWidth = Ktraits::kWidth;
197
+ constexpr int kNThreads = Ktraits::kNThreads;
198
+ constexpr int kNElts = Ktraits::kNElts;
199
+ constexpr int kNWarp = Ktraits::kNWarps;
200
+ constexpr int kNThreadsPerC = Ktraits::kNThreadsPerRow;
201
+ constexpr int kLPerLoad = Ktraits::kNColsPerLoad;
202
+ constexpr int kChunkSizeL = Ktraits::kChunkSizeL;
203
+ constexpr int kChunkSizeC = Ktraits::kNEltsPerRow;
204
+ using input_t = typename Ktraits::input_t;
205
+ using vec_t = typename Ktraits::vec_t;
206
+ using weight_t = typename Ktraits::weight_t;
207
+
208
+ // Shared memory.
209
+ __shared__ input_t x_smem[kWidth - 1 + kChunkSizeL][kChunkSizeC + kNElts];
210
+
211
+ const int tid = threadIdx.x;
212
+ const int l_idx = tid / kNThreadsPerC;
213
+ const int c_idx = tid % kNThreadsPerC;
214
+ const int batch_id = blockIdx.x;
215
+ const int chunk_l_id = blockIdx.y;
216
+ const int chunk_c_id = blockIdx.z;
217
+ input_t *x = reinterpret_cast<input_t *>(params.x_ptr) + batch_id * params.x_batch_stride
218
+ + (chunk_l_id * kChunkSizeL + l_idx) * params.x_l_stride + chunk_c_id * kChunkSizeC + c_idx * kNElts;
219
+ weight_t *weight = reinterpret_cast<weight_t *>(params.weight_ptr)
220
+ + chunk_c_id * kChunkSizeC * params.weight_c_stride;
221
+ input_t *out = reinterpret_cast<input_t *>(params.out_ptr) + batch_id * params.out_batch_stride
222
+ + (chunk_l_id * kChunkSizeL + l_idx) * params.out_l_stride + chunk_c_id * kChunkSizeC + c_idx * kNElts;
223
+
224
+ #pragma unroll
225
+ for (int l = 0; l < Ktraits::kNLoads; ++l) {
226
+ input_t x_vals_load[kNElts] = {0};
227
+ if (chunk_l_id * kChunkSizeL + l * kLPerLoad + l_idx < params.seqlen
228
+ && chunk_c_id * kChunkSizeC + c_idx * kNElts < params.dim) {
229
+ reinterpret_cast<vec_t *>(x_vals_load)[0] = *reinterpret_cast<vec_t *>(x + l * kLPerLoad * params.x_l_stride);
230
+ }
231
+ reinterpret_cast<vec_t *>(x_smem[kWidth - 1 + l * kLPerLoad + l_idx])[c_idx] = reinterpret_cast<vec_t *>(x_vals_load)[0];
232
+ }
233
+ // Load the elements from the previous chunk that are needed for convolution.
234
+ if (l_idx < kWidth - 1) {
235
+ input_t x_vals_load[kNElts] = {0};
236
+ if (chunk_l_id * kChunkSizeL + l_idx - (kWidth - 1) >= 0
237
+ && chunk_l_id * kChunkSizeL + l_idx - (kWidth - 1) < params.seqlen
238
+ && chunk_c_id * kChunkSizeC + c_idx * kNElts < params.dim) {
239
+ reinterpret_cast<vec_t *>(x_vals_load)[0] = *reinterpret_cast<vec_t *>(x - (kWidth - 1) * params.x_l_stride);
240
+ }
241
+ reinterpret_cast<vec_t *>(x_smem[l_idx])[c_idx] = reinterpret_cast<vec_t *>(x_vals_load)[0];
242
+ }
243
+
244
+ __syncthreads();
245
+
246
+ constexpr int kLPerThread = std::min(kChunkSizeL * kChunkSizeC / kNThreads, kChunkSizeL);
247
+ static_assert(kLPerThread * kNThreads == kChunkSizeL * kChunkSizeC);
248
+ constexpr int kNThreadsPerRow = kChunkSizeL / kLPerThread;
249
+ static_assert(kNThreadsPerRow * kLPerThread == kChunkSizeL);
250
+ // kChunkSizeL, kLPerThread, kNThreadsPerRow should be powers of 2 for simplicity
251
+ static_assert((kChunkSizeL & (kChunkSizeL - 1)) == 0);
252
+ static_assert((kLPerThread & (kLPerThread - 1)) == 0);
253
+ static_assert((kNThreadsPerRow & (kNThreadsPerRow - 1)) == 0);
254
+ static_assert(kNThreadsPerRow <= 32);
255
+
256
+ const int row_idx = tid / kNThreadsPerRow;
257
+ const int col_idx = tid % kNThreadsPerRow;
258
+
259
+ float bias_val = params.bias_ptr == nullptr || chunk_c_id * kChunkSizeC + row_idx >= params.dim ? 0.f : float(reinterpret_cast<weight_t *>(params.bias_ptr)[chunk_c_id * kChunkSizeC + row_idx]);
260
+ float weight_vals[kWidth] = {0};
261
+ if (chunk_c_id + kChunkSizeC + row_idx < params.dim) {
262
+ #pragma unroll
263
+ for (int w = 0; w < kWidth; ++w) {
264
+ weight_vals[w] = weight[row_idx * params.weight_c_stride + w * params.weight_width_stride];
265
+ }
266
+ }
267
+ float x_vals[kWidth - 1 + kLPerThread];
268
+ #pragma unroll
269
+ for (int i = 0; i < kWidth - 1 + kLPerThread; ++i) {
270
+ x_vals[i] = float(x_smem[col_idx * kLPerThread + i][row_idx]);
271
+ }
272
+
273
+ float out_vals[kLPerThread];
274
+ #pragma unroll
275
+ for (int i = 0; i < kLPerThread; ++i) {
276
+ out_vals[i] = bias_val;
277
+ #pragma unroll
278
+ for (int w = 0; w < kWidth; ++w) { out_vals[i] += weight_vals[w] * x_vals[i + w]; }
279
+ if (params.silu_activation) {out_vals[i] = out_vals[i] / (1 + expf(-out_vals[i])); }
280
+ }
281
+
282
+ // Since kNThreadsPerRow is a power of 2 and <= 32, we only need syncwarp and not syncthreads.
283
+ __syncwarp();
284
+ #pragma unroll
285
+ for (int i = 0; i < kLPerThread; ++i) { x_smem[col_idx * kLPerThread + i][row_idx] = out_vals[i]; }
286
+ __syncthreads();
287
+
288
+ #pragma unroll
289
+ for (int l = 0; l < Ktraits::kNLoads; ++l) {
290
+ input_t out_vals_store[kNElts];
291
+ reinterpret_cast<vec_t *>(out_vals_store)[0] = reinterpret_cast<vec_t *>(x_smem[l * kLPerLoad + l_idx])[c_idx];
292
+ if (chunk_l_id * kChunkSizeL + l * kLPerLoad + l_idx < params.seqlen
293
+ && chunk_c_id * kChunkSizeC + c_idx * kNElts < params.dim) {
294
+ *reinterpret_cast<vec_t *>(out + l * kLPerLoad * params.out_l_stride) = reinterpret_cast<vec_t *>(out_vals_store)[0];
295
+ }
296
+ }
297
+
298
+ }
299
+
300
+ template<int kNThreads, int kWidth, typename input_t, typename weight_t>
301
+ void causal_conv1d_channellast_fwd_launch(ConvParamsBase &params, cudaStream_t stream) {
302
+ using Ktraits = Causal_conv1d_channellast_fwd_kernel_traits<kNThreads, kWidth, 64, true, input_t, weight_t>;
303
+ // constexpr int kSmemSize = Ktraits::kSmemSize;
304
+ constexpr int kChunkSizeL = Ktraits::kChunkSizeL;
305
+ constexpr int kChunkSizeC = Ktraits::kNEltsPerRow;
306
+ const int n_chunks_L = (params.seqlen + kChunkSizeL - 1) / kChunkSizeL;
307
+ const int n_chunks_C = (params.dim + kChunkSizeC - 1) / kChunkSizeC;
308
+ // printf("n_chunks_L: %d, n_chunks_C: %d\n", n_chunks_L, n_chunks_C);
309
+ dim3 grid(params.batch, n_chunks_L, n_chunks_C);
310
+ dim3 block(Ktraits::kNThreads);
311
+ auto kernel = &causal_conv1d_channellast_fwd_kernel<Ktraits>;
312
+ // if (kSmemSize >= 48 * 1024) {
313
+ // C10_CUDA_CHECK(cudaFuncSetAttribute(
314
+ // kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
315
+ // }
316
+ // kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
317
+ kernel<<<grid, Ktraits::kNThreads, 0, stream>>>(params);
318
+ C10_CUDA_KERNEL_LAUNCH_CHECK();
319
+ }
320
+
321
+ template<typename input_t, typename weight_t>
322
+ void causal_conv1d_channellast_fwd_cuda(ConvParamsBase &params, cudaStream_t stream) {
323
+ if (params.width == 2) {
324
+ causal_conv1d_channellast_fwd_launch<128, 2, input_t, weight_t>(params, stream);
325
+ } else if (params.width == 3) {
326
+ causal_conv1d_channellast_fwd_launch<128, 3, input_t, weight_t>(params, stream);
327
+ } else if (params.width == 4) {
328
+ causal_conv1d_channellast_fwd_launch<128, 4, input_t, weight_t>(params, stream);
329
+ }
330
+ }
331
+
332
+ template void causal_conv1d_fwd_cuda<float, float>(ConvParamsBase &params, cudaStream_t stream);
333
+ template void causal_conv1d_fwd_cuda<at::Half, float>(ConvParamsBase &params, cudaStream_t stream);
334
+ template void causal_conv1d_fwd_cuda<at::BFloat16, float>(ConvParamsBase &params, cudaStream_t stream);
335
+ template void causal_conv1d_fwd_cuda<float, at::Half>(ConvParamsBase &params, cudaStream_t stream);
336
+ template void causal_conv1d_fwd_cuda<at::Half, at::Half>(ConvParamsBase &params, cudaStream_t stream);
337
+ template void causal_conv1d_fwd_cuda<at::BFloat16, at::Half>(ConvParamsBase &params, cudaStream_t stream);
338
+ template void causal_conv1d_fwd_cuda<float, at::BFloat16>(ConvParamsBase &params, cudaStream_t stream);
339
+ template void causal_conv1d_fwd_cuda<at::Half, at::BFloat16>(ConvParamsBase &params, cudaStream_t stream);
340
+ template void causal_conv1d_fwd_cuda<at::BFloat16, at::BFloat16>(ConvParamsBase &params, cudaStream_t stream);
341
+
342
+ template void causal_conv1d_channellast_fwd_cuda<float, float>(ConvParamsBase &params, cudaStream_t stream);
343
+ template void causal_conv1d_channellast_fwd_cuda<at::Half, float>(ConvParamsBase &params, cudaStream_t stream);
344
+ template void causal_conv1d_channellast_fwd_cuda<at::BFloat16, float>(ConvParamsBase &params, cudaStream_t stream);
345
+ template void causal_conv1d_channellast_fwd_cuda<float, at::Half>(ConvParamsBase &params, cudaStream_t stream);
346
+ template void causal_conv1d_channellast_fwd_cuda<at::Half, at::Half>(ConvParamsBase &params, cudaStream_t stream);
347
+ template void causal_conv1d_channellast_fwd_cuda<at::BFloat16, at::Half>(ConvParamsBase &params, cudaStream_t stream);
348
+ template void causal_conv1d_channellast_fwd_cuda<float, at::BFloat16>(ConvParamsBase &params, cudaStream_t stream);
349
+ template void causal_conv1d_channellast_fwd_cuda<at::Half, at::BFloat16>(ConvParamsBase &params, cudaStream_t stream);
350
+ template void causal_conv1d_channellast_fwd_cuda<at::BFloat16, at::BFloat16>(ConvParamsBase &params, cudaStream_t stream);
protein_lm/modeling/models/libs/causal-conv1d/csrc/causal_conv1d_update.cu ADDED
@@ -0,0 +1,96 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ #include <c10/util/BFloat16.h>
6
+ #include <c10/util/Half.h>
7
+ #include <c10/cuda/CUDAException.h> // For C10_CUDA_CHECK and C10_CUDA_KERNEL_LAUNCH_CHECK
8
+
9
+ #include <cub/block/block_load.cuh>
10
+ #include <cub/block/block_store.cuh>
11
+
12
+ #include "causal_conv1d.h"
13
+ #include "causal_conv1d_common.h"
14
+ #include "static_switch.h"
15
+
16
+ template<int kNThreads_, int kWidth_, typename input_t_, typename weight_t_>
17
+ struct Causal_conv1d_update_kernel_traits {
18
+ using input_t = input_t_;
19
+ using weight_t = weight_t_;
20
+ static constexpr int kNThreads = kNThreads_;
21
+ static constexpr int kWidth = kWidth_;
22
+ static constexpr int kNBytes = sizeof(input_t);
23
+ static_assert(kNBytes == 2 || kNBytes == 4);
24
+ };
25
+
26
+ template<typename Ktraits>
27
+ __global__ __launch_bounds__(Ktraits::kNThreads)
28
+ void causal_conv1d_update_kernel(ConvParamsBase params) {
29
+ constexpr int kWidth = Ktraits::kWidth;
30
+ constexpr int kNThreads = Ktraits::kNThreads;
31
+ using input_t = typename Ktraits::input_t;
32
+ using weight_t = typename Ktraits::weight_t;
33
+
34
+ const int tidx = threadIdx.x;
35
+ const int batch_id = blockIdx.x;
36
+ const int channel_id = blockIdx.y * kNThreads + tidx;
37
+ input_t *x = reinterpret_cast<input_t *>(params.x_ptr) + batch_id * params.x_batch_stride
38
+ + channel_id * params.x_c_stride;
39
+ input_t *conv_state = reinterpret_cast<input_t *>(params.conv_state_ptr) + batch_id * params.conv_state_batch_stride
40
+ + channel_id * params.conv_state_c_stride;
41
+ weight_t *weight = reinterpret_cast<weight_t *>(params.weight_ptr) + channel_id * params.weight_c_stride;
42
+ input_t *out = reinterpret_cast<input_t *>(params.out_ptr) + batch_id * params.out_batch_stride
43
+ + channel_id * params.out_c_stride;
44
+ float bias_val = params.bias_ptr == nullptr || channel_id >= params.dim ? 0.f : float(reinterpret_cast<weight_t *>(params.bias_ptr)[channel_id]);
45
+
46
+ float weight_vals[kWidth] = {0};
47
+ if (channel_id < params.dim) {
48
+ #pragma unroll
49
+ for (int i = 0; i < kWidth; ++i) { weight_vals[i] = float(weight[i * params.weight_width_stride]); }
50
+ }
51
+
52
+ float x_vals[kWidth] = {0};
53
+ if (channel_id < params.dim) {
54
+ #pragma unroll
55
+ for (int i = 0; i < kWidth - 1; ++i) { x_vals[i] = float(conv_state[(i + 1) * params.conv_state_l_stride]); }
56
+ x_vals[kWidth - 1] = float(x[0]);
57
+ #pragma unroll
58
+ for (int i = 0; i < kWidth; ++i) { conv_state[i * params.conv_state_l_stride] = input_t(x_vals[i]); }
59
+ }
60
+
61
+ float out_val = bias_val;
62
+ #pragma unroll
63
+ for (int i = 0; i < kWidth; ++i) { out_val += weight_vals[i] * x_vals[i]; }
64
+ if (params.silu_activation) { out_val = out_val / (1 + expf(-out_val)); }
65
+ if (channel_id < params.dim) { out[0] = input_t(out_val); }
66
+ }
67
+
68
+ template<int kNThreads, int kWidth, typename input_t, typename weight_t>
69
+ void causal_conv1d_update_launch(ConvParamsBase &params, cudaStream_t stream) {
70
+ using Ktraits = Causal_conv1d_update_kernel_traits<kNThreads, kWidth, input_t, weight_t>;
71
+ dim3 grid(params.batch, (params.dim + kNThreads - 1) / kNThreads);
72
+ auto kernel = &causal_conv1d_update_kernel<Ktraits>;
73
+ kernel<<<grid, Ktraits::kNThreads, 0, stream>>>(params);
74
+ C10_CUDA_KERNEL_LAUNCH_CHECK();
75
+ }
76
+
77
+ template<typename input_t, typename weight_t>
78
+ void causal_conv1d_update_cuda(ConvParamsBase &params, cudaStream_t stream) {
79
+ if (params.width == 2) {
80
+ causal_conv1d_update_launch<64, 2, input_t, weight_t>(params, stream);
81
+ } else if (params.width == 3) {
82
+ causal_conv1d_update_launch<64, 3, input_t, weight_t>(params, stream);
83
+ } else if (params.width == 4) {
84
+ causal_conv1d_update_launch<64, 4, input_t, weight_t>(params, stream);
85
+ }
86
+ }
87
+
88
+ template void causal_conv1d_update_cuda<float, float>(ConvParamsBase &params, cudaStream_t stream);
89
+ template void causal_conv1d_update_cuda<at::Half, float>(ConvParamsBase &params, cudaStream_t stream);
90
+ template void causal_conv1d_update_cuda<at::BFloat16, float>(ConvParamsBase &params, cudaStream_t stream);
91
+ template void causal_conv1d_update_cuda<float, at::Half>(ConvParamsBase &params, cudaStream_t stream);
92
+ template void causal_conv1d_update_cuda<at::Half, at::Half>(ConvParamsBase &params, cudaStream_t stream);
93
+ template void causal_conv1d_update_cuda<at::BFloat16, at::Half>(ConvParamsBase &params, cudaStream_t stream);
94
+ template void causal_conv1d_update_cuda<float, at::BFloat16>(ConvParamsBase &params, cudaStream_t stream);
95
+ template void causal_conv1d_update_cuda<at::Half, at::BFloat16>(ConvParamsBase &params, cudaStream_t stream);
96
+ template void causal_conv1d_update_cuda<at::BFloat16, at::BFloat16>(ConvParamsBase &params, cudaStream_t stream);
protein_lm/modeling/models/libs/causal-conv1d/csrc/static_switch.h ADDED
@@ -0,0 +1,25 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ // Inspired by https://github.com/NVIDIA/DALI/blob/main/include/dali/core/static_switch.h
2
+ // and https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Dispatch.h
3
+
4
+ #pragma once
5
+
6
+ /// @param COND - a boolean expression to switch by
7
+ /// @param CONST_NAME - a name given for the constexpr bool variable.
8
+ /// @param ... - code to execute for true and false
9
+ ///
10
+ /// Usage:
11
+ /// ```
12
+ /// BOOL_SWITCH(flag, BoolConst, [&] {
13
+ /// some_function<BoolConst>(...);
14
+ /// });
15
+ /// ```
16
+ #define BOOL_SWITCH(COND, CONST_NAME, ...) \
17
+ [&] { \
18
+ if (COND) { \
19
+ static constexpr bool CONST_NAME = true; \
20
+ return __VA_ARGS__(); \
21
+ } else { \
22
+ static constexpr bool CONST_NAME = false; \
23
+ return __VA_ARGS__(); \
24
+ } \
25
+ }()
protein_lm/modeling/models/libs/causal-conv1d/setup.py ADDED
@@ -0,0 +1,264 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) 2023, Tri Dao.
2
+ import sys
3
+ import warnings
4
+ import os
5
+ import re
6
+ import ast
7
+ from pathlib import Path
8
+ from packaging.version import parse, Version
9
+ import platform
10
+
11
+ from setuptools import setup, find_packages
12
+ import subprocess
13
+
14
+ import urllib.request
15
+ import urllib.error
16
+ from wheel.bdist_wheel import bdist_wheel as _bdist_wheel
17
+
18
+ import torch
19
+ from torch.utils.cpp_extension import (
20
+ BuildExtension,
21
+ CppExtension,
22
+ CUDAExtension,
23
+ CUDA_HOME,
24
+ )
25
+
26
+
27
+ with open("README.md", "r", encoding="utf-8") as fh:
28
+ long_description = fh.read()
29
+
30
+
31
+ # ninja build does not work unless include_dirs are abs path
32
+ this_dir = os.path.dirname(os.path.abspath(__file__))
33
+
34
+ PACKAGE_NAME = "causal_conv1d"
35
+
36
+ BASE_WHEEL_URL = "https://github.com/Dao-AILab/causal-conv1d/releases/download/{tag_name}/{wheel_name}"
37
+
38
+ # FORCE_BUILD: Force a fresh build locally, instead of attempting to find prebuilt wheels
39
+ # SKIP_CUDA_BUILD: Intended to allow CI to use a simple `python setup.py sdist` run to copy over raw files, without any cuda compilation
40
+ FORCE_BUILD = os.getenv("CAUSAL_CONV1D_FORCE_BUILD", "FALSE") == "TRUE"
41
+ SKIP_CUDA_BUILD = os.getenv("CAUSAL_CONV1D_SKIP_CUDA_BUILD", "FALSE") == "TRUE"
42
+ # For CI, we want the option to build with C++11 ABI since the nvcr images use C++11 ABI
43
+ FORCE_CXX11_ABI = os.getenv("CAUSAL_CONV1D_FORCE_CXX11_ABI", "FALSE") == "TRUE"
44
+
45
+
46
+ def get_platform():
47
+ """
48
+ Returns the platform name as used in wheel filenames.
49
+ """
50
+ if sys.platform.startswith("linux"):
51
+ return "linux_x86_64"
52
+ elif sys.platform == "darwin":
53
+ mac_version = ".".join(platform.mac_ver()[0].split(".")[:2])
54
+ return f"macosx_{mac_version}_x86_64"
55
+ elif sys.platform == "win32":
56
+ return "win_amd64"
57
+ else:
58
+ raise ValueError("Unsupported platform: {}".format(sys.platform))
59
+
60
+
61
+ def get_cuda_bare_metal_version(cuda_dir):
62
+ raw_output = subprocess.check_output(
63
+ [cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True
64
+ )
65
+ output = raw_output.split()
66
+ release_idx = output.index("release") + 1
67
+ bare_metal_version = parse(output[release_idx].split(",")[0])
68
+
69
+ return raw_output, bare_metal_version
70
+
71
+
72
+ def check_if_cuda_home_none(global_option: str) -> None:
73
+ if CUDA_HOME is not None:
74
+ return
75
+ # warn instead of error because user could be downloading prebuilt wheels, so nvcc won't be necessary
76
+ # in that case.
77
+ warnings.warn(
78
+ f"{global_option} was requested, but nvcc was not found. Are you sure your environment has nvcc available? "
79
+ "If you're installing within a container from https://hub.docker.com/r/pytorch/pytorch, "
80
+ "only images whose names contain 'devel' will provide nvcc."
81
+ )
82
+
83
+
84
+ def append_nvcc_threads(nvcc_extra_args):
85
+ return nvcc_extra_args + ["--threads", "4"]
86
+
87
+
88
+ cmdclass = {}
89
+ ext_modules = []
90
+
91
+ if not SKIP_CUDA_BUILD:
92
+ print("\n\ntorch.__version__ = {}\n\n".format(torch.__version__))
93
+ TORCH_MAJOR = int(torch.__version__.split(".")[0])
94
+ TORCH_MINOR = int(torch.__version__.split(".")[1])
95
+
96
+ check_if_cuda_home_none("causal_conv1d")
97
+ # Check, if CUDA11 is installed for compute capability 8.0
98
+ cc_flag = []
99
+ if CUDA_HOME is not None:
100
+ _, bare_metal_version = get_cuda_bare_metal_version(CUDA_HOME)
101
+ if bare_metal_version < Version("11.6"):
102
+ raise RuntimeError(
103
+ "causal_conv1d is only supported on CUDA 11.6 and above. "
104
+ "Note: make sure nvcc has a supported version by running nvcc -V."
105
+ )
106
+
107
+ cc_flag.append("-gencode")
108
+ cc_flag.append("arch=compute_70,code=sm_70")
109
+ cc_flag.append("-gencode")
110
+ cc_flag.append("arch=compute_80,code=sm_80")
111
+ if bare_metal_version >= Version("11.8"):
112
+ cc_flag.append("-gencode")
113
+ cc_flag.append("arch=compute_90,code=sm_90")
114
+
115
+ # HACK: The compiler flag -D_GLIBCXX_USE_CXX11_ABI is set to be the same as
116
+ # torch._C._GLIBCXX_USE_CXX11_ABI
117
+ # https://github.com/pytorch/pytorch/blob/8472c24e3b5b60150096486616d98b7bea01500b/torch/utils/cpp_extension.py#L920
118
+ if FORCE_CXX11_ABI:
119
+ torch._C._GLIBCXX_USE_CXX11_ABI = True
120
+
121
+ ext_modules.append(
122
+ CUDAExtension(
123
+ name="causal_conv1d_cuda",
124
+ sources=[
125
+ "csrc/causal_conv1d.cpp",
126
+ "csrc/causal_conv1d_fwd.cu",
127
+ "csrc/causal_conv1d_bwd.cu",
128
+ "csrc/causal_conv1d_update.cu",
129
+ ],
130
+ extra_compile_args={
131
+ "cxx": ["-O3"],
132
+ "nvcc": append_nvcc_threads(
133
+ [
134
+ "-O3",
135
+ "-U__CUDA_NO_HALF_OPERATORS__",
136
+ "-U__CUDA_NO_HALF_CONVERSIONS__",
137
+ "-U__CUDA_NO_BFLOAT16_OPERATORS__",
138
+ "-U__CUDA_NO_BFLOAT16_CONVERSIONS__",
139
+ "-U__CUDA_NO_BFLOAT162_OPERATORS__",
140
+ "-U__CUDA_NO_BFLOAT162_CONVERSIONS__",
141
+ "--expt-relaxed-constexpr",
142
+ "--expt-extended-lambda",
143
+ "--use_fast_math",
144
+ "--ptxas-options=-v",
145
+ "-lineinfo",
146
+ ]
147
+ + cc_flag
148
+ ),
149
+ },
150
+ include_dirs=[this_dir],
151
+ )
152
+ )
153
+
154
+
155
+ def get_package_version():
156
+ with open(Path(this_dir) / "causal_conv1d" / "__init__.py", "r") as f:
157
+ version_match = re.search(r"^__version__\s*=\s*(.*)$", f.read(), re.MULTILINE)
158
+ public_version = ast.literal_eval(version_match.group(1))
159
+ local_version = os.environ.get("CAUSAL_CONV1D_LOCAL_VERSION")
160
+ if local_version:
161
+ return f"{public_version}+{local_version}"
162
+ else:
163
+ return str(public_version)
164
+
165
+
166
+ def get_wheel_url():
167
+ # Determine the version numbers that will be used to determine the correct wheel
168
+ # We're using the CUDA version used to build torch, not the one currently installed
169
+ # _, cuda_version_raw = get_cuda_bare_metal_version(CUDA_HOME)
170
+ torch_cuda_version = parse(torch.version.cuda)
171
+ torch_version_raw = parse(torch.__version__)
172
+ # For CUDA 11, we only compile for CUDA 11.8, and for CUDA 12 we only compile for CUDA 12.2
173
+ # to save CI time. Minor versions should be compatible.
174
+ torch_cuda_version = parse("11.8") if torch_cuda_version.major == 11 else parse("12.2")
175
+ python_version = f"cp{sys.version_info.major}{sys.version_info.minor}"
176
+ platform_name = get_platform()
177
+ causal_conv1d_version = get_package_version()
178
+ # cuda_version = f"{cuda_version_raw.major}{cuda_version_raw.minor}"
179
+ cuda_version = f"{torch_cuda_version.major}{torch_cuda_version.minor}"
180
+ torch_version = f"{torch_version_raw.major}.{torch_version_raw.minor}"
181
+ cxx11_abi = str(torch._C._GLIBCXX_USE_CXX11_ABI).upper()
182
+
183
+ # Determine wheel URL based on CUDA version, torch version, python version and OS
184
+ wheel_filename = f"{PACKAGE_NAME}-{causal_conv1d_version}+cu{cuda_version}torch{torch_version}cxx11abi{cxx11_abi}-{python_version}-{python_version}-{platform_name}.whl"
185
+ wheel_url = BASE_WHEEL_URL.format(
186
+ tag_name=f"v{causal_conv1d_version}", wheel_name=wheel_filename
187
+ )
188
+ return wheel_url, wheel_filename
189
+
190
+
191
+ class CachedWheelsCommand(_bdist_wheel):
192
+ """
193
+ The CachedWheelsCommand plugs into the default bdist wheel, which is ran by pip when it cannot
194
+ find an existing wheel (which is currently the case for all installs). We use
195
+ the environment parameters to detect whether there is already a pre-built version of a compatible
196
+ wheel available and short-circuits the standard full build pipeline.
197
+ """
198
+
199
+ def run(self):
200
+ if FORCE_BUILD:
201
+ return super().run()
202
+
203
+ wheel_url, wheel_filename = get_wheel_url()
204
+ print("Guessing wheel URL: ", wheel_url)
205
+ try:
206
+ urllib.request.urlretrieve(wheel_url, wheel_filename)
207
+
208
+ # Make the archive
209
+ # Lifted from the root wheel processing command
210
+ # https://github.com/pypa/wheel/blob/cf71108ff9f6ffc36978069acb28824b44ae028e/src/wheel/bdist_wheel.py#LL381C9-L381C85
211
+ if not os.path.exists(self.dist_dir):
212
+ os.makedirs(self.dist_dir)
213
+
214
+ impl_tag, abi_tag, plat_tag = self.get_tag()
215
+ archive_basename = f"{self.wheel_dist_name}-{impl_tag}-{abi_tag}-{plat_tag}"
216
+
217
+ wheel_path = os.path.join(self.dist_dir, archive_basename + ".whl")
218
+ print("Raw wheel path", wheel_path)
219
+ os.rename(wheel_filename, wheel_path)
220
+ except urllib.error.HTTPError:
221
+ print("Precompiled wheel not found. Building from source...")
222
+ # If the wheel could not be downloaded, build from source
223
+ super().run()
224
+
225
+
226
+ setup(
227
+ name=PACKAGE_NAME,
228
+ version=get_package_version(),
229
+ packages=find_packages(
230
+ exclude=(
231
+ "build",
232
+ "csrc",
233
+ "include",
234
+ "tests",
235
+ "dist",
236
+ "docs",
237
+ "benchmarks",
238
+ "causal_conv1d.egg-info",
239
+ )
240
+ ),
241
+ author="Tri Dao",
242
+ author_email="tri@tridao.me",
243
+ description="Causal depthwise conv1d in CUDA, with a PyTorch interface",
244
+ long_description=long_description,
245
+ long_description_content_type="text/markdown",
246
+ url="https://github.com/Dao-AILab/causal-conv1d",
247
+ classifiers=[
248
+ "Programming Language :: Python :: 3",
249
+ "License :: OSI Approved :: BSD License",
250
+ "Operating System :: Unix",
251
+ ],
252
+ ext_modules=ext_modules,
253
+ cmdclass={"bdist_wheel": CachedWheelsCommand, "build_ext": BuildExtension}
254
+ if ext_modules
255
+ else {
256
+ "bdist_wheel": CachedWheelsCommand,
257
+ },
258
+ python_requires=">=3.7",
259
+ install_requires=[
260
+ "torch",
261
+ "packaging",
262
+ "ninja",
263
+ ],
264
+ )
protein_lm/modeling/models/libs/causal-conv1d/tests/test_causal_conv1d.py ADDED
@@ -0,0 +1,173 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (C) 2023, Tri Dao.
2
+
3
+ import math
4
+
5
+ import torch
6
+ import pytest
7
+
8
+ from einops import rearrange
9
+
10
+ from causal_conv1d.causal_conv1d_interface import causal_conv1d_fn, causal_conv1d_ref
11
+ from causal_conv1d.causal_conv1d_interface import causal_conv1d_update, causal_conv1d_update_ref
12
+
13
+
14
+ @pytest.mark.parametrize("channel_last", [False, True])
15
+ # @pytest.mark.parametrize('channel_last', [True])
16
+ @pytest.mark.parametrize("itype", [torch.float32, torch.float16, torch.bfloat16])
17
+ # @pytest.mark.parametrize('itype', [torch.float16])
18
+ @pytest.mark.parametrize("silu_activation", [False, True])
19
+ # @pytest.mark.parametrize('silu_activation', [True])
20
+ @pytest.mark.parametrize("has_bias", [False, True])
21
+ # @pytest.mark.parametrize('has_bias', [True])
22
+ @pytest.mark.parametrize("width", [2, 3, 4])
23
+ # @pytest.mark.parametrize('width', [2])
24
+ @pytest.mark.parametrize(
25
+ "seqlen", [8, 16, 32, 64, 128, 151, 256, 372, 512, 784, 1024, 1134, 2048, 4096]
26
+ )
27
+ # @pytest.mark.parametrize('seqlen', [8, 16, 32, 64, 128, 256, 512, 784, 1024, 2048, 4096])
28
+ # @pytest.mark.parametrize('seqlen', [128])
29
+ def test_causal_conv1d(seqlen, width, has_bias, silu_activation, itype, channel_last):
30
+ device = "cuda"
31
+ rtol, atol = (3e-4, 1e-3) if itype == torch.float32 else (3e-3, 5e-3)
32
+ if itype == torch.bfloat16:
33
+ rtol, atol = 1e-2, 5e-2
34
+ rtolw, atolw = (1e-3, 1e-3)
35
+ # set seed
36
+ torch.random.manual_seed(0)
37
+ batch_size = 2
38
+ # batch_size = 1
39
+ dim = 4096 + 32 # Try dim not divisible by 64
40
+ # dim = 64
41
+ if not channel_last:
42
+ x = torch.randn(batch_size, 4096 + dim + 64, seqlen, device=device, dtype=itype)[:, 4096:4096 + dim, :].requires_grad_()
43
+ else:
44
+ x = rearrange(
45
+ torch.randn(batch_size, seqlen, 4096 + dim + 64, device=device, dtype=itype)[:, :, 4096:4096 + dim], "b s d -> b d s"
46
+ ).requires_grad_()
47
+ weight = torch.randn(dim, width, device=device, dtype=torch.float32, requires_grad=True)
48
+ if has_bias:
49
+ bias = torch.randn(dim, device=device, dtype=torch.float32, requires_grad=True)
50
+ else:
51
+ bias = None
52
+ x_ref = x.detach().clone().requires_grad_()
53
+ weight_ref = weight.detach().clone().requires_grad_()
54
+ bias_ref = bias.detach().clone().requires_grad_() if bias is not None else None
55
+ activation = None if not silu_activation else "silu"
56
+ out = causal_conv1d_fn(x, weight, bias, activation=activation)
57
+ out_ref = causal_conv1d_ref(x_ref, weight_ref, bias_ref, activation=activation)
58
+
59
+ print(f"Output max diff: {(out - out_ref).abs().max().item()}")
60
+ print(f"Output mean diff: {(out - out_ref).abs().mean().item()}")
61
+ assert torch.allclose(out, out_ref, rtol=rtol, atol=atol)
62
+
63
+ g = torch.randn_like(out)
64
+ out_ref.backward(g)
65
+ out.backward(g)
66
+
67
+ print(f"dx max diff: {(x.grad - x_ref.grad).abs().max().item()}")
68
+ print(f"dweight max diff: {(weight.grad - weight_ref.grad).abs().max().item()}")
69
+ if has_bias:
70
+ print(f"dbias max diff: {(bias.grad - bias_ref.grad).abs().max().item()}")
71
+
72
+ assert torch.allclose(x.grad, x_ref.grad.to(dtype=itype), rtol=rtol, atol=atol)
73
+ assert torch.allclose(weight.grad, weight_ref.grad, rtol=rtolw, atol=atolw)
74
+ if has_bias:
75
+ assert torch.allclose(bias.grad, bias_ref.grad, rtol=rtolw, atol=atolw)
76
+
77
+
78
+ @pytest.mark.parametrize("itype", [torch.float32, torch.float16, torch.bfloat16])
79
+ # @pytest.mark.parametrize('itype', [torch.float16])
80
+ @pytest.mark.parametrize("silu_activation", [False, True])
81
+ # @pytest.mark.parametrize('silu_activation', [False])
82
+ @pytest.mark.parametrize("has_bias", [False, True])
83
+ # @pytest.mark.parametrize('has_bias', [True])
84
+ @pytest.mark.parametrize("width", [2, 3, 4])
85
+ # @pytest.mark.parametrize('width', [2])
86
+ @pytest.mark.parametrize("dim", [2048, 2048 + 16, 4096])
87
+ # @pytest.mark.parametrize("dim", [2048])
88
+ def test_causal_conv1d_update(dim, width, has_bias, silu_activation, itype):
89
+ device = "cuda"
90
+ rtol, atol = (3e-4, 1e-3) if itype == torch.float32 else (3e-3, 5e-3)
91
+ if itype == torch.bfloat16:
92
+ rtol, atol = 1e-2, 5e-2
93
+ rtolw, atolw = (1e-3, 1e-3)
94
+ # set seed
95
+ torch.random.manual_seed(0)
96
+ batch_size = 2
97
+ # batch_size = 1
98
+ # dim = 64
99
+ x = torch.randn(batch_size, dim, device=device, dtype=itype)
100
+ conv_state = torch.randn(batch_size, dim, width, device=device, dtype=itype)
101
+ weight = torch.randn(dim, width, device=device, dtype=torch.float32, requires_grad=True)
102
+ if has_bias:
103
+ bias = torch.randn(dim, device=device, dtype=torch.float32, requires_grad=True)
104
+ else:
105
+ bias = None
106
+ conv_state_ref = conv_state.detach().clone()
107
+ activation = None if not silu_activation else "silu"
108
+ out = causal_conv1d_update(x, conv_state, weight, bias, activation=activation)
109
+ out_ref = causal_conv1d_update_ref(x, conv_state_ref, weight, bias, activation=activation)
110
+
111
+ print(f"Output max diff: {(out - out_ref).abs().max().item()}")
112
+ print(f"Output mean diff: {(out - out_ref).abs().mean().item()}")
113
+ assert torch.equal(conv_state, conv_state_ref)
114
+ assert torch.allclose(out, out_ref, rtol=rtol, atol=atol)
115
+
116
+
117
+ # @pytest.mark.parametrize("channel_last", [False, True])
118
+ @pytest.mark.parametrize('channel_last', [True])
119
+ # @pytest.mark.parametrize("itype", [torch.float32, torch.float16, torch.bfloat16])
120
+ @pytest.mark.parametrize('itype', [torch.bfloat16])
121
+ # @pytest.mark.parametrize("silu_activation", [False, True])
122
+ @pytest.mark.parametrize('silu_activation', [True])
123
+ # @pytest.mark.parametrize("has_bias", [False, True])
124
+ @pytest.mark.parametrize('has_bias', [True])
125
+ # @pytest.mark.parametrize("width", [2, 3, 4])
126
+ @pytest.mark.parametrize('width', [4])
127
+ @pytest.mark.parametrize(
128
+ # "seqlen", [8, 16, 32, 64, 128, 151, 256, 372, 512, 784, 1024, 1134, 2048, 4096]
129
+ "seqlen", [2048]
130
+ )
131
+ # @pytest.mark.parametrize('seqlen', [8, 16, 32, 64, 128, 256, 512, 784, 1024, 2048, 4096])
132
+ # @pytest.mark.parametrize('seqlen', [128])
133
+ def test_causal_conv1d_race_condition(seqlen, width, has_bias, silu_activation, itype, channel_last):
134
+ device = "cuda"
135
+ # set seed
136
+ torch.random.manual_seed(0)
137
+ batch_size = 2
138
+ # batch_size = 1
139
+ dim = 4096 + 32 # Try dim not divisible by 64
140
+ # dim = 64
141
+ if not channel_last:
142
+ x = torch.randn(batch_size, 4096 + dim + 64, seqlen, device=device, dtype=itype)[:, 4096:4096 + dim, :].requires_grad_()
143
+ else:
144
+ x = rearrange(
145
+ torch.randn(batch_size, seqlen, 4096 + dim + 64, device=device, dtype=itype)[:, :, 4096:4096 + dim], "b s d -> b d s"
146
+ ).requires_grad_()
147
+ weight = torch.randn(dim, width, device=device, dtype=torch.float32, requires_grad=True)
148
+ if has_bias:
149
+ bias = torch.randn(dim, device=device, dtype=torch.float32, requires_grad=True)
150
+ else:
151
+ bias = None
152
+ activation = None if not silu_activation else "silu"
153
+ out0 = causal_conv1d_fn(x, weight, bias, activation=activation)
154
+ g = torch.randn_like(out0)
155
+ dx0, dw0, db0 = torch.autograd.grad(out0, (x, weight, bias), g)
156
+ dw_atol = 1e-4
157
+ db_atol = 1e-4
158
+
159
+ for i in range(10000):
160
+ out = causal_conv1d_fn(x, weight, bias, activation=activation)
161
+ dx, dw, db = torch.autograd.grad(out, (x, weight, bias), g)
162
+ dw_equal = torch.allclose(dw, dw0, atol=dw_atol)
163
+ # if not dw_equal:
164
+ # breakpoint()
165
+ if has_bias:
166
+ db_equal = torch.allclose(db, db0, atol=db_atol)
167
+ # if not db_equal:
168
+ # breakpoint()
169
+ assert torch.equal(out, out0)
170
+ assert torch.equal(dx, dx0)
171
+ assert dw_equal
172
+ if has_bias:
173
+ assert dw_equal
protein_lm/modeling/models/libs/mamba/.gitmodules ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ [submodule "3rdparty/lm-evaluation-harness"]
2
+ path = 3rdparty/lm-evaluation-harness
3
+ url = https://github.com/EleutherAI/lm-evaluation-harness/
protein_lm/modeling/models/libs/mamba/AUTHORS ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ Tri Dao, tri@tridao.me
2
+ Albert Gu, agu@andrew.cmu.edu
protein_lm/modeling/models/libs/mamba/LICENSE ADDED
@@ -0,0 +1,201 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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protein_lm/modeling/models/libs/mamba/README.md ADDED
@@ -0,0 +1,149 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Mamba
2
+
3
+ ![Mamba](assets/selection.png "Selective State Space")
4
+ > **Mamba: Linear-Time Sequence Modeling with Selective State Spaces**\
5
+ > Albert Gu*, Tri Dao*\
6
+ > Paper: https://arxiv.org/abs/2312.00752
7
+
8
+ ## About
9
+
10
+ Mamba is a new state space model architecture showing promising performance on information-dense data such as language modeling, where previous subquadratic models fall short of Transformers.
11
+ It is based on the line of progress on [structured state space models](https://github.com/state-spaces/s4),
12
+ with an efficient hardware-aware design and implementation in the spirit of [FlashAttention](https://github.com/Dao-AILab/flash-attention).
13
+
14
+ ## Installation
15
+
16
+ - `pip install causal-conv1d`: an efficient implementation of a simple causal Conv1d layer used inside the Mamba block.
17
+ - `pip install mamba-ssm`: the core Mamba package.
18
+
19
+ It can also be built from source with `pip install .` from this repository.
20
+
21
+ If `pip` complains about PyTorch versions, try passing `--no-build-isolation` to `pip`.
22
+
23
+ Other requirements:
24
+ - Linux
25
+ - NVIDIA GPU
26
+ - PyTorch 1.12+
27
+ - CUDA 11.6+
28
+
29
+ ## Usage
30
+
31
+ We expose several levels of interface with the Mamba model.
32
+
33
+ ### Selective SSM
34
+
35
+ Mamba is based on a selective SSM layer, which is the focus of the paper (Section 3; Algorithm 2).
36
+
37
+ Source: [ops/selective_scan_interface.py](mamba_ssm/ops/selective_scan_interface.py).
38
+
39
+ ### Mamba Block
40
+
41
+ The main module of this repository is the Mamba architecture block wrapping the selective SSM.
42
+
43
+ Source: [modules/mamba_simple.py](mamba_ssm/modules/mamba_simple.py).
44
+
45
+ Usage:
46
+ ```
47
+ from mamba_ssm import Mamba
48
+
49
+ batch, length, dim = 2, 64, 16
50
+ x = torch.randn(batch, length, dim).to("cuda")
51
+ model = Mamba(
52
+ # This module uses roughly 3 * expand * d_model^2 parameters
53
+ d_model=dim, # Model dimension d_model
54
+ d_state=16, # SSM state expansion factor
55
+ d_conv=4, # Local convolution width
56
+ expand=2, # Block expansion factor
57
+ ).to("cuda")
58
+ y = model(x)
59
+ assert y.shape == x.shape
60
+ ```
61
+
62
+ ### Mamba Language Model
63
+
64
+ Finally, we provide an example of a complete language model: a deep sequence model backbone (with repeating Mamba blocks) + language model head.
65
+
66
+ Source: [models/mixer_seq_simple.py](mamba_ssm/models/mixer_seq_simple.py).
67
+
68
+ This is an example of how to integrate Mamba into an end-to-end neural network.
69
+ This example is used in the generation scripts below.
70
+
71
+
72
+
73
+ ## Pretrained Models
74
+
75
+ Pretrained models are uploaded to
76
+ [HuggingFace](https://huggingface.co/state-spaces): `mamba-130m`, `mamba-370m`,
77
+ `mamba-790m`, `mamba-1.4b`, `mamba-2.8b`.
78
+
79
+ The models will be autodownloaded by the generation script below.
80
+
81
+ These models were trained on the [Pile](https://huggingface.co/datasets/EleutherAI/pile), and follow the standard model dimensions described by GPT-3 and followed by many open source models:
82
+
83
+ | Parameters | Layers | Model dim. |
84
+ |------------|--------|------------|
85
+ | 130M | 12 | 768 |
86
+ | 370M | 24 | 1024 |
87
+ | 790M | 24 | 1536 |
88
+ | 1.4B | 24 | 2048 |
89
+ | 2.8B | 32 | 2560 |
90
+
91
+ (The layer count of Mamba should be doubled, as two Mamba blocks are needed for each "layer" (MHA block + MLP block) of a Transformer.)
92
+
93
+ Note: these are base models trained only for 300B tokens, without any form of downstream modification (instruction tuning, etc.).
94
+ Performance is expected to be comparable or better than other architectures trained on similar data, but not to match larger or fine-tuned models.
95
+
96
+
97
+ ## Evaluations
98
+
99
+ To run zero-shot evaluations of models (corresponding to Table 3 of the paper),
100
+ we use the
101
+ [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/big-refactor)
102
+ library.
103
+
104
+ 1. Pull the `lm-evaluation-harness` repo by `git submodule update --init
105
+ --recursive`. We use the `big-refactor` branch.
106
+ 2. Install `lm-evaluation-harness`: `pip install -e 3rdparty/lm-evaluation-harness`
107
+ 3. Run evaluation with (more documentation at the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/big-refactor) repo):
108
+ ```
109
+ python evals/lm_harness_eval.py --model mamba --model_args pretrained=state-spaces/mamba-130m --tasks lambada_openai,hellaswag,piqa,arc_easy,arc_challenge,winogrande --device cuda --batch_size 64
110
+ python evals/lm_harness_eval.py --model hf --model_args pretrained=EleutherAI/pythia-160m --tasks lambada_openai,hellaswag,piqa,arc_easy,arc_challenge,winogrande --device cuda --batch_size 64
111
+ ```
112
+
113
+ Note that the result of each task might differ from reported values by 0.1-0.3 due to noise in the evaluation process.
114
+
115
+ ## Inference
116
+
117
+ The script [benchmarks/benchmark_generation_mamba_simple.py](benchmarks/benchmark_generation_mamba_simple.py)
118
+ 1. autoloads a model from the HuggingFace Hub,
119
+ 2. generates completions of a user-specified prompt,
120
+ 3. benchmarks the inference speed of this generation.
121
+
122
+ Other configurable options include the top-p (nucleus sampling) probability, and the softmax temperature.
123
+
124
+ ### Examples
125
+
126
+ To test generation latency (e.g. batch size = 1) with different sampling strategies:
127
+
128
+ ```
129
+ python benchmarks/benchmark_generation_mamba_simple.py --model-name "state-spaces/mamba-2.8b" --prompt "My cat wrote all this CUDA code for a new language model and" --topp 0.9 --temperature 0.5
130
+ python benchmarks/benchmark_generation_mamba_simple.py --model-name "EleutherAI/pythia-2.8b" --prompt "My cat wrote all this CUDA code for a new language model and" --topp 0.9 --temperature 0.5
131
+ ```
132
+
133
+ To test generation throughput with random prompts (e.g. large batch size):
134
+ ```
135
+ python benchmarks/benchmark_generation_mamba_simple.py --model-name "state-spaces/mamba-2.8b" --batch 128
136
+ python benchmarks/benchmark_generation_mamba_simple.py --model-name "EleutherAI/pythia-2.8b" --batch 128
137
+ ```
138
+
139
+ ## Citation
140
+
141
+ If you use this codebase, or otherwise found our work valuable, please cite Mamba:
142
+ ```
143
+ @article{mamba,
144
+ title={Mamba: Linear-Time Sequence Modeling with Selective State Spaces},
145
+ author={Gu, Albert and Dao, Tri},
146
+ journal={arXiv preprint arXiv:2312.00752},
147
+ year={2023}
148
+ }
149
+ ```
protein_lm/modeling/models/libs/mamba/assets/selection.png ADDED
protein_lm/modeling/models/libs/mamba/benchmarks/benchmark_generation_mamba_simple.py ADDED
@@ -0,0 +1,88 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) 2023, Tri Dao, Albert Gu.
2
+
3
+ import argparse
4
+ import time
5
+ import json
6
+
7
+ import torch
8
+ import torch.nn.functional as F
9
+
10
+ from einops import rearrange
11
+
12
+ from transformers import AutoTokenizer, AutoModelForCausalLM
13
+
14
+ from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel
15
+
16
+
17
+ parser = argparse.ArgumentParser(description="Generation benchmarking")
18
+ parser.add_argument("--model-name", type=str, default="state-spaces/mamba-130m")
19
+ parser.add_argument("--prompt", type=str, default=None)
20
+ parser.add_argument("--promptlen", type=int, default=100)
21
+ parser.add_argument("--genlen", type=int, default=100)
22
+ parser.add_argument("--temperature", type=float, default=1.0)
23
+ parser.add_argument("--topk", type=int, default=1)
24
+ parser.add_argument("--topp", type=float, default=1.0)
25
+ parser.add_argument("--batch", type=int, default=1)
26
+ args = parser.parse_args()
27
+
28
+ repeats = 3
29
+ device = "cuda"
30
+ dtype = torch.float16
31
+
32
+ print(f"Loading model {args.model_name}")
33
+ is_mamba = args.model_name.startswith("state-spaces/mamba-") or "mamba" in args.model_name
34
+
35
+ if is_mamba:
36
+ tokenizer = AutoTokenizer.from_pretrained("/home/zhulianghui/VisionProjects/mamba/ckpts/gpt-neox-20b-tokenizer")
37
+ model = MambaLMHeadModel.from_pretrained(args.model_name, device=device, dtype=dtype)
38
+ else:
39
+ tokenizer = AutoTokenizer.from_pretrained(args.model_name)
40
+ model = AutoModelForCausalLM.from_pretrained(args.model_name, device_map={"": device}, torch_dtype=dtype)
41
+ model.eval()
42
+ print(f"Number of parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
43
+
44
+ torch.random.manual_seed(0)
45
+ if args.prompt is None:
46
+ input_ids = torch.randint(1, 1000, (args.batch, args.promptlen), dtype=torch.long, device="cuda")
47
+ attn_mask = torch.ones_like(input_ids, dtype=torch.long, device="cuda")
48
+ else:
49
+ tokens = tokenizer(args.prompt, return_tensors="pt")
50
+ input_ids = tokens.input_ids.to(device=device)
51
+ attn_mask = tokens.attention_mask.to(device=device)
52
+ max_length = input_ids.shape[1] + args.genlen
53
+
54
+ if is_mamba:
55
+ fn = lambda: model.generate(
56
+ input_ids=input_ids,
57
+ max_length=max_length,
58
+ cg=True,
59
+ return_dict_in_generate=True,
60
+ output_scores=True,
61
+ enable_timing=False,
62
+ temperature=args.temperature,
63
+ top_k=args.topk,
64
+ top_p=args.topp,
65
+ )
66
+ else:
67
+ fn = lambda: model.generate(
68
+ input_ids=input_ids,
69
+ attention_mask=attn_mask,
70
+ max_length=max_length,
71
+ return_dict_in_generate=True,
72
+ pad_token_id=tokenizer.eos_token_id,
73
+ do_sample=True,
74
+ temperature=args.temperature,
75
+ top_k=args.topk,
76
+ top_p=args.topp,
77
+ )
78
+ out = fn()
79
+ if args.prompt is not None:
80
+ print(tokenizer.batch_decode(out.sequences.tolist()))
81
+
82
+ torch.cuda.synchronize()
83
+ start = time.time()
84
+ for _ in range(repeats):
85
+ fn()
86
+ torch.cuda.synchronize()
87
+ print(f"Prompt length: {len(input_ids[0])}, generation length: {len(out.sequences[0]) - len(input_ids[0])}")
88
+ print(f"{args.model_name} prompt processing + decoding time: {(time.time() - start) / repeats * 1000:.0f}ms")
protein_lm/modeling/models/libs/mamba/csrc/selective_scan/reverse_scan.cuh ADDED
@@ -0,0 +1,401 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ #pragma once
6
+
7
+ #include <cub/config.cuh>
8
+
9
+ #include <cub/util_ptx.cuh>
10
+ #include <cub/util_type.cuh>
11
+ #include <cub/block/block_raking_layout.cuh>
12
+ // #include <cub/detail/uninitialized_copy.cuh>
13
+ #include "uninitialized_copy.cuh"
14
+
15
+ /**
16
+ * Perform a reverse sequential reduction over \p LENGTH elements of the \p input array. The aggregate is returned.
17
+ */
18
+ template <
19
+ int LENGTH,
20
+ typename T,
21
+ typename ReductionOp>
22
+ __device__ __forceinline__ T ThreadReverseReduce(const T (&input)[LENGTH], ReductionOp reduction_op) {
23
+ static_assert(LENGTH > 0);
24
+ T retval = input[LENGTH - 1];
25
+ #pragma unroll
26
+ for (int i = LENGTH - 2; i >= 0; --i) { retval = reduction_op(retval, input[i]); }
27
+ return retval;
28
+ }
29
+
30
+ /**
31
+ * Perform a sequential inclusive postfix reverse scan over the statically-sized \p input array, seeded with the specified \p postfix. The aggregate is returned.
32
+ */
33
+ template <
34
+ int LENGTH,
35
+ typename T,
36
+ typename ScanOp>
37
+ __device__ __forceinline__ T ThreadReverseScanInclusive(
38
+ const T (&input)[LENGTH],
39
+ T (&output)[LENGTH],
40
+ ScanOp scan_op,
41
+ const T postfix)
42
+ {
43
+ T inclusive = postfix;
44
+ #pragma unroll
45
+ for (int i = LENGTH - 1; i >= 0; --i) {
46
+ inclusive = scan_op(inclusive, input[i]);
47
+ output[i] = inclusive;
48
+ }
49
+ }
50
+
51
+ /**
52
+ * Perform a sequential exclusive postfix reverse scan over the statically-sized \p input array, seeded with the specified \p postfix. The aggregate is returned.
53
+ */
54
+ template <
55
+ int LENGTH,
56
+ typename T,
57
+ typename ScanOp>
58
+ __device__ __forceinline__ T ThreadReverseScanExclusive(
59
+ const T (&input)[LENGTH],
60
+ T (&output)[LENGTH],
61
+ ScanOp scan_op,
62
+ const T postfix)
63
+ {
64
+ // Careful, output maybe be aliased to input
65
+ T exclusive = postfix;
66
+ T inclusive;
67
+ #pragma unroll
68
+ for (int i = LENGTH - 1; i >= 0; --i) {
69
+ inclusive = scan_op(exclusive, input[i]);
70
+ output[i] = exclusive;
71
+ exclusive = inclusive;
72
+ }
73
+ return inclusive;
74
+ }
75
+
76
+
77
+ /**
78
+ * \brief WarpReverseScan provides SHFL-based variants of parallel postfix scan of items partitioned across a CUDA thread warp.
79
+ *
80
+ * LOGICAL_WARP_THREADS must be a power-of-two
81
+ */
82
+ template <
83
+ typename T, ///< Data type being scanned
84
+ int LOGICAL_WARP_THREADS ///< Number of threads per logical warp
85
+ >
86
+ struct WarpReverseScan {
87
+ //---------------------------------------------------------------------
88
+ // Constants and type definitions
89
+ //---------------------------------------------------------------------
90
+
91
+ /// Whether the logical warp size and the PTX warp size coincide
92
+ static constexpr bool IS_ARCH_WARP = (LOGICAL_WARP_THREADS == CUB_WARP_THREADS(0));
93
+ /// The number of warp scan steps
94
+ static constexpr int STEPS = cub::Log2<LOGICAL_WARP_THREADS>::VALUE;
95
+ static_assert(LOGICAL_WARP_THREADS == 1 << STEPS);
96
+
97
+
98
+ //---------------------------------------------------------------------
99
+ // Thread fields
100
+ //---------------------------------------------------------------------
101
+
102
+ /// Lane index in logical warp
103
+ unsigned int lane_id;
104
+
105
+ /// Logical warp index in 32-thread physical warp
106
+ unsigned int warp_id;
107
+
108
+ /// 32-thread physical warp member mask of logical warp
109
+ unsigned int member_mask;
110
+
111
+ //---------------------------------------------------------------------
112
+ // Construction
113
+ //---------------------------------------------------------------------
114
+
115
+ /// Constructor
116
+ explicit __device__ __forceinline__
117
+ WarpReverseScan()
118
+ : lane_id(cub::LaneId())
119
+ , warp_id(IS_ARCH_WARP ? 0 : (lane_id / LOGICAL_WARP_THREADS))
120
+ , member_mask(cub::WarpMask<LOGICAL_WARP_THREADS>(warp_id))
121
+ {
122
+ if (!IS_ARCH_WARP) {
123
+ lane_id = lane_id % LOGICAL_WARP_THREADS;
124
+ }
125
+ }
126
+
127
+
128
+ /// Broadcast
129
+ __device__ __forceinline__ T Broadcast(
130
+ T input, ///< [in] The value to broadcast
131
+ int src_lane) ///< [in] Which warp lane is to do the broadcasting
132
+ {
133
+ return cub::ShuffleIndex<LOGICAL_WARP_THREADS>(input, src_lane, member_mask);
134
+ }
135
+
136
+
137
+ /// Inclusive scan
138
+ template <typename ScanOpT>
139
+ __device__ __forceinline__ void InclusiveReverseScan(
140
+ T input, ///< [in] Calling thread's input item.
141
+ T &inclusive_output, ///< [out] Calling thread's output item. May be aliased with \p input.
142
+ ScanOpT scan_op) ///< [in] Binary scan operator
143
+ {
144
+ inclusive_output = input;
145
+ #pragma unroll
146
+ for (int STEP = 0; STEP < STEPS; STEP++) {
147
+ int offset = 1 << STEP;
148
+ T temp = cub::ShuffleDown<LOGICAL_WARP_THREADS>(
149
+ inclusive_output, offset, LOGICAL_WARP_THREADS - 1, member_mask
150
+ );
151
+ // Perform scan op if from a valid peer
152
+ inclusive_output = static_cast<int>(lane_id) >= LOGICAL_WARP_THREADS - offset
153
+ ? inclusive_output : scan_op(temp, inclusive_output);
154
+ }
155
+ }
156
+
157
+ /// Exclusive scan
158
+ // Get exclusive from inclusive
159
+ template <typename ScanOpT>
160
+ __device__ __forceinline__ void ExclusiveReverseScan(
161
+ T input, ///< [in] Calling thread's input item.
162
+ T &exclusive_output, ///< [out] Calling thread's output item. May be aliased with \p input.
163
+ ScanOpT scan_op, ///< [in] Binary scan operator
164
+ T &warp_aggregate) ///< [out] Warp-wide aggregate reduction of input items.
165
+ {
166
+ T inclusive_output;
167
+ InclusiveReverseScan(input, inclusive_output, scan_op);
168
+ warp_aggregate = cub::ShuffleIndex<LOGICAL_WARP_THREADS>(inclusive_output, 0, member_mask);
169
+ // initial value unknown
170
+ exclusive_output = cub::ShuffleDown<LOGICAL_WARP_THREADS>(
171
+ inclusive_output, 1, LOGICAL_WARP_THREADS - 1, member_mask
172
+ );
173
+ }
174
+
175
+ /**
176
+ * \brief Computes both inclusive and exclusive reverse scans using the specified binary scan functor across the calling warp. Because no initial value is supplied, the \p exclusive_output computed for the last <em>warp-lane</em> is undefined.
177
+ */
178
+ template <typename ScanOpT>
179
+ __device__ __forceinline__ void ReverseScan(
180
+ T input, ///< [in] Calling thread's input item.
181
+ T &inclusive_output, ///< [out] Calling thread's inclusive-scan output item.
182
+ T &exclusive_output, ///< [out] Calling thread's exclusive-scan output item.
183
+ ScanOpT scan_op) ///< [in] Binary scan operator
184
+ {
185
+ InclusiveReverseScan(input, inclusive_output, scan_op);
186
+ // initial value unknown
187
+ exclusive_output = cub::ShuffleDown<LOGICAL_WARP_THREADS>(
188
+ inclusive_output, 1, LOGICAL_WARP_THREADS - 1, member_mask
189
+ );
190
+ }
191
+
192
+ };
193
+
194
+ /**
195
+ * \brief BlockReverseScan provides variants of raking-based parallel postfix scan across a CUDA thread block.
196
+ */
197
+ template <
198
+ typename T, ///< Data type being scanned
199
+ int BLOCK_DIM_X, ///< The thread block length in threads along the X dimension
200
+ bool MEMOIZE=false ///< Whether or not to buffer outer raking scan partials to incur fewer shared memory reads at the expense of higher register pressure
201
+ >
202
+ struct BlockReverseScan {
203
+ //---------------------------------------------------------------------
204
+ // Types and constants
205
+ //---------------------------------------------------------------------
206
+
207
+ /// Constants
208
+ /// The thread block size in threads
209
+ static constexpr int BLOCK_THREADS = BLOCK_DIM_X;
210
+
211
+ /// Layout type for padded thread block raking grid
212
+ using BlockRakingLayout = cub::BlockRakingLayout<T, BLOCK_THREADS>;
213
+ // The number of reduction elements is not a multiple of the number of raking threads for now
214
+ static_assert(BlockRakingLayout::UNGUARDED);
215
+
216
+ /// Number of raking threads
217
+ static constexpr int RAKING_THREADS = BlockRakingLayout::RAKING_THREADS;
218
+ /// Number of raking elements per warp synchronous raking thread
219
+ static constexpr int SEGMENT_LENGTH = BlockRakingLayout::SEGMENT_LENGTH;
220
+ /// Cooperative work can be entirely warp synchronous
221
+ static constexpr bool WARP_SYNCHRONOUS = (int(BLOCK_THREADS) == int(RAKING_THREADS));
222
+
223
+ /// WarpReverseScan utility type
224
+ using WarpReverseScan = WarpReverseScan<T, RAKING_THREADS>;
225
+
226
+ /// Shared memory storage layout type
227
+ struct _TempStorage {
228
+ typename BlockRakingLayout::TempStorage raking_grid; ///< Padded thread block raking grid
229
+ };
230
+
231
+
232
+ /// Alias wrapper allowing storage to be unioned
233
+ struct TempStorage : cub::Uninitialized<_TempStorage> {};
234
+
235
+
236
+ //---------------------------------------------------------------------
237
+ // Per-thread fields
238
+ //---------------------------------------------------------------------
239
+
240
+ // Thread fields
241
+ _TempStorage &temp_storage;
242
+ unsigned int linear_tid;
243
+ T cached_segment[SEGMENT_LENGTH];
244
+
245
+
246
+ //---------------------------------------------------------------------
247
+ // Utility methods
248
+ //---------------------------------------------------------------------
249
+
250
+ /// Performs upsweep raking reduction, returning the aggregate
251
+ template <typename ScanOp>
252
+ __device__ __forceinline__ T Upsweep(ScanOp scan_op) {
253
+ T *smem_raking_ptr = BlockRakingLayout::RakingPtr(temp_storage.raking_grid, linear_tid);
254
+ // Read data into registers
255
+ #pragma unroll
256
+ for (int i = 0; i < SEGMENT_LENGTH; ++i) { cached_segment[i] = smem_raking_ptr[i]; }
257
+ T raking_partial = cached_segment[SEGMENT_LENGTH - 1];
258
+ #pragma unroll
259
+ for (int i = SEGMENT_LENGTH - 2; i >= 0; --i) {
260
+ raking_partial = scan_op(raking_partial, cached_segment[i]);
261
+ }
262
+ return raking_partial;
263
+ }
264
+
265
+
266
+ /// Performs exclusive downsweep raking scan
267
+ template <typename ScanOp>
268
+ __device__ __forceinline__ void ExclusiveDownsweep(
269
+ ScanOp scan_op,
270
+ T raking_partial)
271
+ {
272
+ T *smem_raking_ptr = BlockRakingLayout::RakingPtr(temp_storage.raking_grid, linear_tid);
273
+ // Read data back into registers
274
+ if (!MEMOIZE) {
275
+ #pragma unroll
276
+ for (int i = 0; i < SEGMENT_LENGTH; ++i) { cached_segment[i] = smem_raking_ptr[i]; }
277
+ }
278
+ ThreadReverseScanExclusive(cached_segment, cached_segment, scan_op, raking_partial);
279
+ // Write data back to smem
280
+ #pragma unroll
281
+ for (int i = 0; i < SEGMENT_LENGTH; ++i) { smem_raking_ptr[i] = cached_segment[i]; }
282
+ }
283
+
284
+
285
+ //---------------------------------------------------------------------
286
+ // Constructors
287
+ //---------------------------------------------------------------------
288
+
289
+ /// Constructor
290
+ __device__ __forceinline__ BlockReverseScan(
291
+ TempStorage &temp_storage)
292
+ :
293
+ temp_storage(temp_storage.Alias()),
294
+ linear_tid(cub::RowMajorTid(BLOCK_DIM_X, 1, 1))
295
+ {}
296
+
297
+
298
+ /// Computes an exclusive thread block-wide postfix scan using the specified binary \p scan_op functor. Each thread contributes one input element. the call-back functor \p block_postfix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically postfixes the thread block's scan inputs. Also provides every thread with the block-wide \p block_aggregate of all inputs.
299
+ template <
300
+ typename ScanOp,
301
+ typename BlockPostfixCallbackOp>
302
+ __device__ __forceinline__ void ExclusiveReverseScan(
303
+ T input, ///< [in] Calling thread's input item
304
+ T &exclusive_output, ///< [out] Calling thread's output item (may be aliased to \p input)
305
+ ScanOp scan_op, ///< [in] Binary scan operator
306
+ BlockPostfixCallbackOp &block_postfix_callback_op) ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a thread block-wide postfix to be applied to all inputs.
307
+ {
308
+ if (WARP_SYNCHRONOUS) {
309
+ // Short-circuit directly to warp-synchronous scan
310
+ T block_aggregate;
311
+ WarpReverseScan warp_scan;
312
+ warp_scan.ExclusiveReverseScan(input, exclusive_output, scan_op, block_aggregate);
313
+ // Obtain warp-wide postfix in lane0, then broadcast to other lanes
314
+ T block_postfix = block_postfix_callback_op(block_aggregate);
315
+ block_postfix = warp_scan.Broadcast(block_postfix, 0);
316
+ exclusive_output = linear_tid == BLOCK_THREADS - 1 ? block_postfix : scan_op(block_postfix, exclusive_output);
317
+ } else {
318
+ // Place thread partial into shared memory raking grid
319
+ T *placement_ptr = BlockRakingLayout::PlacementPtr(temp_storage.raking_grid, linear_tid);
320
+ detail::uninitialized_copy(placement_ptr, input);
321
+ cub::CTA_SYNC();
322
+ // Reduce parallelism down to just raking threads
323
+ if (linear_tid < RAKING_THREADS) {
324
+ WarpReverseScan warp_scan;
325
+ // Raking upsweep reduction across shared partials
326
+ T upsweep_partial = Upsweep(scan_op);
327
+ // Warp-synchronous scan
328
+ T exclusive_partial, block_aggregate;
329
+ warp_scan.ExclusiveReverseScan(upsweep_partial, exclusive_partial, scan_op, block_aggregate);
330
+ // Obtain block-wide postfix in lane0, then broadcast to other lanes
331
+ T block_postfix = block_postfix_callback_op(block_aggregate);
332
+ block_postfix = warp_scan.Broadcast(block_postfix, 0);
333
+ // Update postfix with warpscan exclusive partial
334
+ T downsweep_postfix = linear_tid == RAKING_THREADS - 1
335
+ ? block_postfix : scan_op(block_postfix, exclusive_partial);
336
+ // Exclusive raking downsweep scan
337
+ ExclusiveDownsweep(scan_op, downsweep_postfix);
338
+ }
339
+ cub::CTA_SYNC();
340
+ // Grab thread postfix from shared memory
341
+ exclusive_output = *placement_ptr;
342
+
343
+ // // Compute warp scan in each warp.
344
+ // // The exclusive output from the last lane in each warp is invalid.
345
+ // T inclusive_output;
346
+ // WarpReverseScan warp_scan;
347
+ // warp_scan.ReverseScan(input, inclusive_output, exclusive_output, scan_op);
348
+
349
+ // // Compute the warp-wide postfix and block-wide aggregate for each warp. Warp postfix for the last warp is invalid.
350
+ // T block_aggregate;
351
+ // T warp_postfix = ComputeWarpPostfix(scan_op, inclusive_output, block_aggregate);
352
+
353
+ // // Apply warp postfix to our lane's partial
354
+ // if (warp_id != 0) {
355
+ // exclusive_output = scan_op(warp_postfix, exclusive_output);
356
+ // if (lane_id == 0) { exclusive_output = warp_postfix; }
357
+ // }
358
+
359
+ // // Use the first warp to determine the thread block postfix, returning the result in lane0
360
+ // if (warp_id == 0) {
361
+ // T block_postfix = block_postfix_callback_op(block_aggregate);
362
+ // if (lane_id == 0) {
363
+ // // Share the postfix with all threads
364
+ // detail::uninitialized_copy(&temp_storage.block_postfix,
365
+ // block_postfix);
366
+
367
+ // exclusive_output = block_postfix; // The block postfix is the exclusive output for tid0
368
+ // }
369
+ // }
370
+
371
+ // cub::CTA_SYNC();
372
+
373
+ // // Incorporate thread block postfix into outputs
374
+ // T block_postfix = temp_storage.block_postfix;
375
+ // if (linear_tid > 0) { exclusive_output = scan_op(block_postfix, exclusive_output); }
376
+ }
377
+ }
378
+
379
+
380
+ /**
381
+ * \brief Computes an inclusive block-wide postfix scan using the specified binary \p scan_op functor. Each thread contributes an array of consecutive input elements. the call-back functor \p block_postfix_callback_op is invoked by the first warp in the block, and the value returned by <em>lane</em><sub>0</sub> in that warp is used as the "seed" value that logically postfixes the thread block's scan inputs. Also provides every thread with the block-wide \p block_aggregate of all inputs.
382
+ */
383
+ template <
384
+ int ITEMS_PER_THREAD,
385
+ typename ScanOp,
386
+ typename BlockPostfixCallbackOp>
387
+ __device__ __forceinline__ void InclusiveReverseScan(
388
+ T (&input)[ITEMS_PER_THREAD], ///< [in] Calling thread's input items
389
+ T (&output)[ITEMS_PER_THREAD], ///< [out] Calling thread's output items (may be aliased to \p input)
390
+ ScanOp scan_op, ///< [in] Binary scan functor
391
+ BlockPostfixCallbackOp &block_postfix_callback_op) ///< [in-out] <b>[<em>warp</em><sub>0</sub> only]</b> Call-back functor for specifying a block-wide postfix to be applied to the logical input sequence.
392
+ {
393
+ // Reduce consecutive thread items in registers
394
+ T thread_postfix = ThreadReverseReduce(input, scan_op);
395
+ // Exclusive thread block-scan
396
+ ExclusiveReverseScan(thread_postfix, thread_postfix, scan_op, block_postfix_callback_op);
397
+ // Inclusive scan in registers with postfix as seed
398
+ ThreadReverseScanInclusive(input, output, scan_op, thread_postfix);
399
+ }
400
+
401
+ };
protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan.cpp ADDED
@@ -0,0 +1,497 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ #include <ATen/cuda/CUDAContext.h>
6
+ #include <c10/cuda/CUDAGuard.h>
7
+ #include <torch/extension.h>
8
+ #include <vector>
9
+
10
+ #include "selective_scan.h"
11
+
12
+ #define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")")
13
+
14
+ #define DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(ITYPE, NAME, ...) \
15
+ if (ITYPE == at::ScalarType::Half) { \
16
+ using input_t = at::Half; \
17
+ __VA_ARGS__(); \
18
+ } else if (ITYPE == at::ScalarType::BFloat16) { \
19
+ using input_t = at::BFloat16; \
20
+ __VA_ARGS__(); \
21
+ } else if (ITYPE == at::ScalarType::Float) { \
22
+ using input_t = float; \
23
+ __VA_ARGS__(); \
24
+ } else { \
25
+ AT_ERROR(#NAME, " not implemented for input type '", toString(ITYPE), "'"); \
26
+ }
27
+
28
+ #define DISPATCH_WTYPE_FLOAT_AND_HALF_AND_BF16(WTYPE, NAME, ...) \
29
+ if (WTYPE == at::ScalarType::Half) { \
30
+ using weight_t = at::Half; \
31
+ __VA_ARGS__(); \
32
+ } else if (WTYPE == at::ScalarType::BFloat16) { \
33
+ using weight_t = at::BFloat16; \
34
+ __VA_ARGS__(); \
35
+ } else if (WTYPE == at::ScalarType::Float) { \
36
+ using weight_t = float; \
37
+ __VA_ARGS__(); \
38
+ } else { \
39
+ AT_ERROR(#NAME, " not implemented for weight type '", toString(WTYPE), "'"); \
40
+ }
41
+
42
+ #define DISPATCH_WTYPE_FLOAT_AND_COMPLEX(WTYPE, NAME, ...) \
43
+ if (WTYPE == at::ScalarType::Float) { \
44
+ using weight_t = float; \
45
+ __VA_ARGS__(); \
46
+ } else if (WTYPE == at::ScalarType::ComplexFloat) { \
47
+ using weight_t = c10::complex<float>; \
48
+ __VA_ARGS__(); \
49
+ } else { \
50
+ AT_ERROR(#NAME, " not implemented for weight type '", toString(WTYPE), "'"); \
51
+ }
52
+
53
+ template<typename input_t, typename weight_t>
54
+ void selective_scan_fwd_cuda(SSMParamsBase &params, cudaStream_t stream);
55
+
56
+ template <typename input_t, typename weight_t>
57
+ void selective_scan_bwd_cuda(SSMParamsBwd &params, cudaStream_t stream);
58
+
59
+ void set_ssm_params_fwd(SSMParamsBase &params,
60
+ // sizes
61
+ const size_t batch,
62
+ const size_t dim,
63
+ const size_t seqlen,
64
+ const size_t dstate,
65
+ const size_t n_groups,
66
+ const size_t n_chunks,
67
+ const bool is_variable_B,
68
+ const bool is_variable_C,
69
+ // device pointers
70
+ const at::Tensor u,
71
+ const at::Tensor delta,
72
+ const at::Tensor A,
73
+ const at::Tensor B,
74
+ const at::Tensor C,
75
+ const at::Tensor out,
76
+ const at::Tensor z,
77
+ const at::Tensor out_z,
78
+ void* D_ptr,
79
+ void* delta_bias_ptr,
80
+ void* x_ptr,
81
+ bool has_z,
82
+ bool delta_softplus) {
83
+
84
+ // Reset the parameters
85
+ memset(&params, 0, sizeof(params));
86
+
87
+ params.batch = batch;
88
+ params.dim = dim;
89
+ params.seqlen = seqlen;
90
+ params.dstate = dstate;
91
+ params.n_groups = n_groups;
92
+ params.n_chunks = n_chunks;
93
+ params.dim_ngroups_ratio = dim / n_groups;
94
+
95
+ params.delta_softplus = delta_softplus;
96
+
97
+ params.is_variable_B = is_variable_B;
98
+ params.is_variable_C = is_variable_C;
99
+
100
+ // Set the pointers and strides.
101
+ params.u_ptr = u.data_ptr();
102
+ params.delta_ptr = delta.data_ptr();
103
+ params.A_ptr = A.data_ptr();
104
+ params.B_ptr = B.data_ptr();
105
+ params.C_ptr = C.data_ptr();
106
+ params.D_ptr = D_ptr;
107
+ params.delta_bias_ptr = delta_bias_ptr;
108
+ params.out_ptr = out.data_ptr();
109
+ params.x_ptr = x_ptr;
110
+ params.z_ptr = has_z ? z.data_ptr() : nullptr;
111
+ params.out_z_ptr = has_z ? out_z.data_ptr() : nullptr;
112
+ // All stride are in elements, not bytes.
113
+ params.A_d_stride = A.stride(0);
114
+ params.A_dstate_stride = A.stride(1);
115
+ if (!is_variable_B) {
116
+ params.B_d_stride = B.stride(0);
117
+ } else {
118
+ params.B_batch_stride = B.stride(0);
119
+ params.B_group_stride = B.stride(1);
120
+ }
121
+ params.B_dstate_stride = !is_variable_B ? B.stride(1) : B.stride(2);
122
+ if (!is_variable_C) {
123
+ params.C_d_stride = C.stride(0);
124
+ } else {
125
+ params.C_batch_stride = C.stride(0);
126
+ params.C_group_stride = C.stride(1);
127
+ }
128
+ params.C_dstate_stride = !is_variable_C ? C.stride(1) : C.stride(2);
129
+ params.u_batch_stride = u.stride(0);
130
+ params.u_d_stride = u.stride(1);
131
+ params.delta_batch_stride = delta.stride(0);
132
+ params.delta_d_stride = delta.stride(1);
133
+ if (has_z) {
134
+ params.z_batch_stride = z.stride(0);
135
+ params.z_d_stride = z.stride(1);
136
+ params.out_z_batch_stride = out_z.stride(0);
137
+ params.out_z_d_stride = out_z.stride(1);
138
+ }
139
+ params.out_batch_stride = out.stride(0);
140
+ params.out_d_stride = out.stride(1);
141
+ }
142
+
143
+ void set_ssm_params_bwd(SSMParamsBwd &params,
144
+ // sizes
145
+ const size_t batch,
146
+ const size_t dim,
147
+ const size_t seqlen,
148
+ const size_t dstate,
149
+ const size_t n_groups,
150
+ const size_t n_chunks,
151
+ const bool is_variable_B,
152
+ const bool is_variable_C,
153
+ // device pointers
154
+ const at::Tensor u,
155
+ const at::Tensor delta,
156
+ const at::Tensor A,
157
+ const at::Tensor B,
158
+ const at::Tensor C,
159
+ const at::Tensor z,
160
+ const at::Tensor out,
161
+ const at::Tensor out_z,
162
+ void* D_ptr,
163
+ void* delta_bias_ptr,
164
+ void* x_ptr,
165
+ const at::Tensor dout,
166
+ const at::Tensor du,
167
+ const at::Tensor ddelta,
168
+ const at::Tensor dA,
169
+ const at::Tensor dB,
170
+ const at::Tensor dC,
171
+ const at::Tensor dz,
172
+ void* dD_ptr,
173
+ void* ddelta_bias_ptr,
174
+ bool has_z,
175
+ bool delta_softplus,
176
+ bool recompute_out_z) {
177
+ // Pass in "dout" instead of "out", we're not gonna use "out" unless we have z
178
+ set_ssm_params_fwd(params, batch, dim, seqlen, dstate, n_groups, n_chunks, is_variable_B, is_variable_C,
179
+ u, delta, A, B, C, has_z ? out : dout,
180
+ has_z ? z : dout,
181
+ // If not recompute_out_z, pass dout instead of out_z.
182
+ // This won't be used by the bwd kernel
183
+ recompute_out_z ? out_z : dout,
184
+ D_ptr, delta_bias_ptr, x_ptr, has_z, delta_softplus);
185
+ if (!recompute_out_z) { params.out_z_ptr = nullptr; }
186
+
187
+ // Set the pointers and strides.
188
+ params.dout_ptr = dout.data_ptr();
189
+ params.du_ptr = du.data_ptr();
190
+ params.dA_ptr = dA.data_ptr();
191
+ params.dB_ptr = dB.data_ptr();
192
+ params.dC_ptr = dC.data_ptr();
193
+ params.dD_ptr = dD_ptr;
194
+ params.ddelta_ptr = ddelta.data_ptr();
195
+ params.ddelta_bias_ptr = ddelta_bias_ptr;
196
+ params.dz_ptr = has_z ? dz.data_ptr() : nullptr;
197
+ // All stride are in elements, not bytes.
198
+ params.dout_batch_stride = dout.stride(0);
199
+ params.dout_d_stride = dout.stride(1);
200
+ params.dA_d_stride = dA.stride(0);
201
+ params.dA_dstate_stride = dA.stride(1);
202
+ if (!is_variable_B) {
203
+ params.dB_d_stride = dB.stride(0);
204
+ } else {
205
+ params.dB_batch_stride = dB.stride(0);
206
+ params.dB_group_stride = dB.stride(1);
207
+ }
208
+ params.dB_dstate_stride = !is_variable_B ? dB.stride(1) : dB.stride(2);
209
+ if (!is_variable_C) {
210
+ params.dC_d_stride = dC.stride(0);
211
+ } else {
212
+ params.dC_batch_stride = dC.stride(0);
213
+ params.dC_group_stride = dC.stride(1);
214
+ }
215
+ params.dC_dstate_stride = !is_variable_C ? dC.stride(1) : dC.stride(2);
216
+ params.du_batch_stride = du.stride(0);
217
+ params.du_d_stride = du.stride(1);
218
+ params.ddelta_batch_stride = ddelta.stride(0);
219
+ params.ddelta_d_stride = ddelta.stride(1);
220
+ if (has_z) {
221
+ params.dz_batch_stride = dz.stride(0);
222
+ params.dz_d_stride = dz.stride(1);
223
+ }
224
+ }
225
+
226
+ std::vector<at::Tensor>
227
+ selective_scan_fwd(const at::Tensor &u, const at::Tensor &delta,
228
+ const at::Tensor &A, const at::Tensor &B, const at::Tensor &C,
229
+ const c10::optional<at::Tensor> &D_,
230
+ const c10::optional<at::Tensor> &z_,
231
+ const c10::optional<at::Tensor> &delta_bias_,
232
+ bool delta_softplus) {
233
+ auto input_type = u.scalar_type();
234
+ auto weight_type = A.scalar_type();
235
+ TORCH_CHECK(input_type == at::ScalarType::Float || input_type == at::ScalarType::Half || input_type == at::ScalarType::BFloat16);
236
+ TORCH_CHECK(weight_type == at::ScalarType::Float || weight_type == at::ScalarType::ComplexFloat);
237
+
238
+ const bool is_variable_B = B.dim() >= 3;
239
+ const bool is_variable_C = C.dim() >= 3;
240
+ const bool is_complex = weight_type == at::ScalarType::ComplexFloat;
241
+
242
+ TORCH_CHECK(delta.scalar_type() == input_type);
243
+ TORCH_CHECK(B.scalar_type() == (!is_variable_B ? weight_type : input_type));
244
+ TORCH_CHECK(C.scalar_type() == (!is_variable_C ? weight_type : input_type));
245
+
246
+ TORCH_CHECK(u.is_cuda());
247
+ TORCH_CHECK(delta.is_cuda());
248
+ TORCH_CHECK(A.is_cuda());
249
+ TORCH_CHECK(B.is_cuda());
250
+ TORCH_CHECK(C.is_cuda());
251
+
252
+ TORCH_CHECK(u.stride(-1) == 1);
253
+ TORCH_CHECK(delta.stride(-1) == 1);
254
+
255
+ const auto sizes = u.sizes();
256
+ const int batch_size = sizes[0];
257
+ const int dim = sizes[1];
258
+ const int seqlen = sizes[2];
259
+ const int dstate = A.size(1);
260
+ const int n_groups = is_variable_B ? B.size(1) : 1;
261
+
262
+ TORCH_CHECK(dstate <= 256, "selective_scan only supports state dimension <= 256");
263
+
264
+ CHECK_SHAPE(u, batch_size, dim, seqlen);
265
+ CHECK_SHAPE(delta, batch_size, dim, seqlen);
266
+ CHECK_SHAPE(A, dim, dstate);
267
+ if (!is_variable_B) {
268
+ CHECK_SHAPE(B, dim, dstate);
269
+ } else {
270
+ CHECK_SHAPE(B, batch_size, n_groups, dstate, !is_complex ? seqlen : seqlen * 2);
271
+ TORCH_CHECK(B.stride(-1) == 1);
272
+ }
273
+ if (!is_variable_C) {
274
+ CHECK_SHAPE(C, dim, dstate);
275
+ } else {
276
+ CHECK_SHAPE(C, batch_size, n_groups, dstate, !is_complex ? seqlen: seqlen * 2);
277
+ TORCH_CHECK(C.stride(-1) == 1);
278
+ }
279
+
280
+ if (D_.has_value()) {
281
+ auto D = D_.value();
282
+ TORCH_CHECK(D.scalar_type() == at::ScalarType::Float);
283
+ TORCH_CHECK(D.is_cuda());
284
+ TORCH_CHECK(D.stride(-1) == 1);
285
+ CHECK_SHAPE(D, dim);
286
+ }
287
+
288
+ if (delta_bias_.has_value()) {
289
+ auto delta_bias = delta_bias_.value();
290
+ TORCH_CHECK(delta_bias.scalar_type() == at::ScalarType::Float);
291
+ TORCH_CHECK(delta_bias.is_cuda());
292
+ TORCH_CHECK(delta_bias.stride(-1) == 1);
293
+ CHECK_SHAPE(delta_bias, dim);
294
+ }
295
+
296
+ at::Tensor z, out_z;
297
+ const bool has_z = z_.has_value();
298
+ if (has_z) {
299
+ z = z_.value();
300
+ TORCH_CHECK(z.scalar_type() == input_type);
301
+ TORCH_CHECK(z.is_cuda());
302
+ TORCH_CHECK(z.stride(-1) == 1);
303
+ CHECK_SHAPE(z, batch_size, dim, seqlen);
304
+ out_z = torch::empty_like(z);
305
+ }
306
+
307
+ const int n_chunks = (seqlen + 2048 - 1) / 2048;
308
+ // const int n_chunks = (seqlen + 1024 - 1) / 1024;
309
+ // at::Tensor out = torch::empty_like(u);
310
+ // Right now u has BHL layout and delta has HBL layout, and we want out to have HBL layout
311
+ at::Tensor out = torch::empty_like(delta);
312
+ at::Tensor x;
313
+ x = torch::empty({batch_size, dim, n_chunks, dstate * 2}, u.options().dtype(weight_type));
314
+
315
+ SSMParamsBase params;
316
+ set_ssm_params_fwd(params, batch_size, dim, seqlen, dstate, n_groups, n_chunks, is_variable_B, is_variable_C,
317
+ u, delta, A, B, C, out, z, out_z,
318
+ D_.has_value() ? D_.value().data_ptr() : nullptr,
319
+ delta_bias_.has_value() ? delta_bias_.value().data_ptr() : nullptr,
320
+ x.data_ptr(),
321
+ has_z,
322
+ delta_softplus);
323
+
324
+ // Otherwise the kernel will be launched from cuda:0 device
325
+ // Cast to char to avoid compiler warning about narrowing
326
+ at::cuda::CUDAGuard device_guard{(char)u.get_device()};
327
+ auto stream = at::cuda::getCurrentCUDAStream().stream();
328
+ DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(u.scalar_type(), "selective_scan_fwd", [&] {
329
+ DISPATCH_WTYPE_FLOAT_AND_COMPLEX(A.scalar_type(), "selective_scan_fwd", [&] {
330
+ selective_scan_fwd_cuda<input_t, weight_t>(params, stream);
331
+ });
332
+ });
333
+ std::vector<at::Tensor> result = {out, x};
334
+ if (has_z) { result.push_back(out_z); }
335
+ return result;
336
+ }
337
+
338
+ std::vector<at::Tensor>
339
+ selective_scan_bwd(const at::Tensor &u, const at::Tensor &delta,
340
+ const at::Tensor &A, const at::Tensor &B, const at::Tensor &C,
341
+ const c10::optional<at::Tensor> &D_,
342
+ const c10::optional<at::Tensor> &z_,
343
+ const c10::optional<at::Tensor> &delta_bias_,
344
+ const at::Tensor &dout,
345
+ const c10::optional<at::Tensor> &x_,
346
+ const c10::optional<at::Tensor> &out_,
347
+ c10::optional<at::Tensor> &dz_,
348
+ bool delta_softplus,
349
+ bool recompute_out_z) {
350
+ auto input_type = u.scalar_type();
351
+ auto weight_type = A.scalar_type();
352
+ TORCH_CHECK(input_type == at::ScalarType::Float || input_type == at::ScalarType::Half || input_type == at::ScalarType::BFloat16);
353
+ TORCH_CHECK(weight_type == at::ScalarType::Float || weight_type == at::ScalarType::ComplexFloat);
354
+
355
+ const bool is_variable_B = B.dim() >= 3;
356
+ const bool is_variable_C = C.dim() >= 3;
357
+ const bool is_complex = weight_type == at::ScalarType::ComplexFloat;
358
+
359
+ TORCH_CHECK(delta.scalar_type() == input_type);
360
+ TORCH_CHECK(B.scalar_type() == (!is_variable_B ? weight_type : input_type));
361
+ TORCH_CHECK(C.scalar_type() == (!is_variable_C ? weight_type : input_type));
362
+ TORCH_CHECK(dout.scalar_type() == input_type);
363
+
364
+ TORCH_CHECK(u.is_cuda());
365
+ TORCH_CHECK(delta.is_cuda());
366
+ TORCH_CHECK(A.is_cuda());
367
+ TORCH_CHECK(B.is_cuda());
368
+ TORCH_CHECK(C.is_cuda());
369
+ TORCH_CHECK(dout.is_cuda());
370
+
371
+ TORCH_CHECK(u.stride(-1) == 1);
372
+ TORCH_CHECK(delta.stride(-1) == 1);
373
+ TORCH_CHECK(dout.stride(-1) == 1);
374
+
375
+ const auto sizes = u.sizes();
376
+ const int batch_size = sizes[0];
377
+ const int dim = sizes[1];
378
+ const int seqlen = sizes[2];
379
+ const int dstate = A.size(1);
380
+ const int n_groups = is_variable_B ? B.size(1) : 1;
381
+
382
+ TORCH_CHECK(dstate <= 256, "selective_scan only supports state dimension <= 256");
383
+
384
+ CHECK_SHAPE(u, batch_size, dim, seqlen);
385
+ CHECK_SHAPE(delta, batch_size, dim, seqlen);
386
+ CHECK_SHAPE(A, dim, dstate);
387
+ if (!is_variable_B) {
388
+ CHECK_SHAPE(B, dim, dstate);
389
+ } else {
390
+ CHECK_SHAPE(B, batch_size, n_groups, dstate, !is_complex ? seqlen : seqlen * 2);
391
+ TORCH_CHECK(B.stride(-1) == 1);
392
+ }
393
+ if (!is_variable_C) {
394
+ CHECK_SHAPE(C, dim, dstate);
395
+ } else {
396
+ CHECK_SHAPE(C, batch_size, n_groups, dstate, !is_complex ? seqlen: seqlen * 2);
397
+ TORCH_CHECK(C.stride(-1) == 1);
398
+ }
399
+ CHECK_SHAPE(dout, batch_size, dim, seqlen);
400
+
401
+ if (D_.has_value()) {
402
+ auto D = D_.value();
403
+ TORCH_CHECK(D.scalar_type() == at::ScalarType::Float);
404
+ TORCH_CHECK(D.is_cuda());
405
+ TORCH_CHECK(D.stride(-1) == 1);
406
+ CHECK_SHAPE(D, dim);
407
+ }
408
+
409
+ if (delta_bias_.has_value()) {
410
+ auto delta_bias = delta_bias_.value();
411
+ TORCH_CHECK(delta_bias.scalar_type() == at::ScalarType::Float);
412
+ TORCH_CHECK(delta_bias.is_cuda());
413
+ TORCH_CHECK(delta_bias.stride(-1) == 1);
414
+ CHECK_SHAPE(delta_bias, dim);
415
+ }
416
+
417
+ at::Tensor z, out, dz, out_z;
418
+ const bool has_z = z_.has_value();
419
+ if (has_z) {
420
+ z = z_.value();
421
+ TORCH_CHECK(z.scalar_type() == input_type);
422
+ TORCH_CHECK(z.is_cuda());
423
+ TORCH_CHECK(z.stride(-1) == 1);
424
+ CHECK_SHAPE(z, batch_size, dim, seqlen);
425
+
426
+ TORCH_CHECK(out_.has_value());
427
+ out = out_.value();
428
+ TORCH_CHECK(out.scalar_type() == input_type);
429
+ TORCH_CHECK(out.is_cuda());
430
+ TORCH_CHECK(out.stride(-1) == 1);
431
+ CHECK_SHAPE(out, batch_size, dim, seqlen);
432
+
433
+ if (dz_.has_value()) {
434
+ dz = dz_.value();
435
+ TORCH_CHECK(dz.scalar_type() == input_type);
436
+ TORCH_CHECK(dz.is_cuda());
437
+ TORCH_CHECK(dz.stride(-1) == 1);
438
+ CHECK_SHAPE(dz, batch_size, dim, seqlen);
439
+ } else {
440
+ dz = torch::empty_like(z);
441
+ }
442
+ if (recompute_out_z) {
443
+ out_z = torch::empty_like(out);
444
+ }
445
+ }
446
+
447
+ const int n_chunks = (seqlen + 2048 - 1) / 2048;
448
+ // const int n_chunks = (seqlen + 1024 - 1) / 1024;
449
+ if (n_chunks > 1) { TORCH_CHECK(x_.has_value()); }
450
+ if (x_.has_value()) {
451
+ auto x = x_.value();
452
+ TORCH_CHECK(x.scalar_type() == weight_type);
453
+ TORCH_CHECK(x.is_cuda());
454
+ TORCH_CHECK(x.is_contiguous());
455
+ CHECK_SHAPE(x, batch_size, dim, n_chunks, 2 * dstate);
456
+ }
457
+
458
+ at::Tensor du = torch::empty_like(u);
459
+ at::Tensor ddelta = torch::empty_like(delta);
460
+ at::Tensor dA = torch::zeros_like(A);
461
+ at::Tensor dB = !is_variable_B ? torch::zeros_like(B) : torch::zeros_like(B, B.options().dtype(torch::kFloat32));
462
+ at::Tensor dC = !is_variable_C ? torch::zeros_like(C) : torch::zeros_like(C, C.options().dtype(torch::kFloat32));
463
+ at::Tensor dD;
464
+ if (D_.has_value()) { dD = torch::zeros_like(D_.value()); }
465
+ at::Tensor ddelta_bias;
466
+ if (delta_bias_.has_value()) { ddelta_bias = torch::zeros_like(delta_bias_.value()); }
467
+
468
+ SSMParamsBwd params;
469
+ set_ssm_params_bwd(params, batch_size, dim, seqlen, dstate, n_groups, n_chunks, is_variable_B, is_variable_C,
470
+ u, delta, A, B, C, z, out, out_z,
471
+ D_.has_value() ? D_.value().data_ptr() : nullptr,
472
+ delta_bias_.has_value() ? delta_bias_.value().data_ptr() : nullptr,
473
+ x_.has_value() ? x_.value().data_ptr() : nullptr,
474
+ dout, du, ddelta, dA, dB, dC, dz,
475
+ D_.has_value() ? dD.data_ptr() : nullptr,
476
+ delta_bias_.has_value() ? ddelta_bias.data_ptr() : nullptr,
477
+ has_z, delta_softplus, recompute_out_z);
478
+
479
+ // Otherwise the kernel will be launched from cuda:0 device
480
+ // Cast to char to avoid compiler warning about narrowing
481
+ at::cuda::CUDAGuard device_guard{(char)u.get_device()};
482
+ auto stream = at::cuda::getCurrentCUDAStream().stream();
483
+ DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(u.scalar_type(), "selective_scan_bwd", [&] {
484
+ DISPATCH_WTYPE_FLOAT_AND_COMPLEX(A.scalar_type(), "selective_scan_bwd", [&] {
485
+ selective_scan_bwd_cuda<input_t, weight_t>(params, stream);
486
+ });
487
+ });
488
+ std::vector<at::Tensor> result = {du, ddelta, dA, dB.to(B.dtype()), dC.to(C.dtype()), dD, ddelta_bias};
489
+ if (has_z) { result.push_back(dz); }
490
+ if (recompute_out_z) { result.push_back(out_z); }
491
+ return result;
492
+ }
493
+
494
+ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
495
+ m.def("fwd", &selective_scan_fwd, "Selective scan forward");
496
+ m.def("bwd", &selective_scan_bwd, "Selective scan backward");
497
+ }
protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan.h ADDED
@@ -0,0 +1,101 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ #pragma once
6
+
7
+ ////////////////////////////////////////////////////////////////////////////////////////////////////
8
+
9
+ struct SSMScanParamsBase {
10
+ using index_t = uint32_t;
11
+
12
+ int batch, seqlen, n_chunks;
13
+ index_t a_batch_stride;
14
+ index_t b_batch_stride;
15
+ index_t out_batch_stride;
16
+
17
+ // Common data pointers.
18
+ void *__restrict__ a_ptr;
19
+ void *__restrict__ b_ptr;
20
+ void *__restrict__ out_ptr;
21
+ void *__restrict__ x_ptr;
22
+ };
23
+
24
+ ////////////////////////////////////////////////////////////////////////////////////////////////////
25
+
26
+ struct SSMParamsBase {
27
+ using index_t = uint32_t;
28
+
29
+ int batch, dim, seqlen, dstate, n_groups, n_chunks;
30
+ int dim_ngroups_ratio;
31
+ bool is_variable_B;
32
+ bool is_variable_C;
33
+
34
+ bool delta_softplus;
35
+
36
+ index_t A_d_stride;
37
+ index_t A_dstate_stride;
38
+ index_t B_batch_stride;
39
+ index_t B_d_stride;
40
+ index_t B_dstate_stride;
41
+ index_t B_group_stride;
42
+ index_t C_batch_stride;
43
+ index_t C_d_stride;
44
+ index_t C_dstate_stride;
45
+ index_t C_group_stride;
46
+ index_t u_batch_stride;
47
+ index_t u_d_stride;
48
+ index_t delta_batch_stride;
49
+ index_t delta_d_stride;
50
+ index_t z_batch_stride;
51
+ index_t z_d_stride;
52
+ index_t out_batch_stride;
53
+ index_t out_d_stride;
54
+ index_t out_z_batch_stride;
55
+ index_t out_z_d_stride;
56
+
57
+ // Common data pointers.
58
+ void *__restrict__ A_ptr;
59
+ void *__restrict__ B_ptr;
60
+ void *__restrict__ C_ptr;
61
+ void *__restrict__ D_ptr;
62
+ void *__restrict__ u_ptr;
63
+ void *__restrict__ delta_ptr;
64
+ void *__restrict__ delta_bias_ptr;
65
+ void *__restrict__ out_ptr;
66
+ void *__restrict__ x_ptr;
67
+ void *__restrict__ z_ptr;
68
+ void *__restrict__ out_z_ptr;
69
+ };
70
+
71
+ struct SSMParamsBwd: public SSMParamsBase {
72
+ index_t dout_batch_stride;
73
+ index_t dout_d_stride;
74
+ index_t dA_d_stride;
75
+ index_t dA_dstate_stride;
76
+ index_t dB_batch_stride;
77
+ index_t dB_group_stride;
78
+ index_t dB_d_stride;
79
+ index_t dB_dstate_stride;
80
+ index_t dC_batch_stride;
81
+ index_t dC_group_stride;
82
+ index_t dC_d_stride;
83
+ index_t dC_dstate_stride;
84
+ index_t du_batch_stride;
85
+ index_t du_d_stride;
86
+ index_t dz_batch_stride;
87
+ index_t dz_d_stride;
88
+ index_t ddelta_batch_stride;
89
+ index_t ddelta_d_stride;
90
+
91
+ // Common data pointers.
92
+ void *__restrict__ dout_ptr;
93
+ void *__restrict__ dA_ptr;
94
+ void *__restrict__ dB_ptr;
95
+ void *__restrict__ dC_ptr;
96
+ void *__restrict__ dD_ptr;
97
+ void *__restrict__ du_ptr;
98
+ void *__restrict__ dz_ptr;
99
+ void *__restrict__ ddelta_ptr;
100
+ void *__restrict__ ddelta_bias_ptr;
101
+ };
protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_bwd_bf16_complex.cu ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ // Split into multiple files to compile in paralell
6
+
7
+ #include "selective_scan_bwd_kernel.cuh"
8
+
9
+ template void selective_scan_bwd_cuda<at::BFloat16, complex_t>(SSMParamsBwd &params, cudaStream_t stream);
protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_bwd_bf16_real.cu ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ // Split into multiple files to compile in paralell
6
+
7
+ #include "selective_scan_bwd_kernel.cuh"
8
+
9
+ template void selective_scan_bwd_cuda<at::BFloat16, float>(SSMParamsBwd &params, cudaStream_t stream);
protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_bwd_fp16_complex.cu ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ // Split into multiple files to compile in paralell
6
+
7
+ #include "selective_scan_bwd_kernel.cuh"
8
+
9
+ template void selective_scan_bwd_cuda<at::Half, complex_t>(SSMParamsBwd &params, cudaStream_t stream);
protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_bwd_fp16_real.cu ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ // Split into multiple files to compile in paralell
6
+
7
+ #include "selective_scan_bwd_kernel.cuh"
8
+
9
+ template void selective_scan_bwd_cuda<at::Half, float>(SSMParamsBwd &params, cudaStream_t stream);
protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_bwd_fp32_complex.cu ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ // Split into multiple files to compile in paralell
6
+
7
+ #include "selective_scan_bwd_kernel.cuh"
8
+
9
+ template void selective_scan_bwd_cuda<float, complex_t>(SSMParamsBwd &params, cudaStream_t stream);
protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_bwd_fp32_real.cu ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ // Split into multiple files to compile in paralell
6
+
7
+ #include "selective_scan_bwd_kernel.cuh"
8
+
9
+ template void selective_scan_bwd_cuda<float, float>(SSMParamsBwd &params, cudaStream_t stream);
protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_bwd_kernel.cuh ADDED
@@ -0,0 +1,531 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ #pragma once
6
+
7
+ #include <c10/util/BFloat16.h>
8
+ #include <c10/util/Half.h>
9
+ #include <c10/cuda/CUDAException.h> // For C10_CUDA_CHECK and C10_CUDA_KERNEL_LAUNCH_CHECK
10
+ #include <ATen/cuda/Atomic.cuh> // For atomicAdd on complex
11
+
12
+ #include <cub/block/block_load.cuh>
13
+ #include <cub/block/block_store.cuh>
14
+ #include <cub/block/block_scan.cuh>
15
+ #include <cub/block/block_reduce.cuh>
16
+
17
+ #include "selective_scan.h"
18
+ #include "selective_scan_common.h"
19
+ #include "reverse_scan.cuh"
20
+ #include "static_switch.h"
21
+
22
+ template<typename scalar_t> __device__ __forceinline__ scalar_t conj(scalar_t x);
23
+ template<> __device__ __forceinline__ float conj<float>(float x) { return x; }
24
+ template<> __device__ __forceinline__ complex_t conj<complex_t>(complex_t x) { return std::conj(x); }
25
+
26
+ template<int kNThreads_, int kNItems_, bool kIsEvenLen_, bool kIsVariableB_, bool kIsVariableC_,
27
+ bool kDeltaSoftplus_, bool kHasZ_, typename input_t_, typename weight_t_>
28
+ struct Selective_Scan_bwd_kernel_traits {
29
+ static_assert(kNItems_ % 4 == 0);
30
+ using input_t = input_t_;
31
+ using weight_t = weight_t_;
32
+ static constexpr int kNThreads = kNThreads_;
33
+ static constexpr int kNItems = kNItems_;
34
+ static constexpr int kNBytes = sizeof(input_t);
35
+ static_assert(kNBytes == 2 || kNBytes == 4);
36
+ static constexpr int kNElts = kNBytes == 4 ? 4 : std::min(8, kNItems);
37
+ static_assert(kNItems % kNElts == 0);
38
+ static constexpr int kNLoads = kNItems / kNElts;
39
+ static constexpr bool kIsComplex = std::is_same_v<weight_t, complex_t>;
40
+ static constexpr bool kIsEvenLen = kIsEvenLen_;
41
+ static constexpr bool kIsVariableB = kIsVariableB_;
42
+ static constexpr bool kIsVariableC = kIsVariableC_;
43
+ static constexpr bool kDeltaSoftplus = kDeltaSoftplus_;
44
+ static constexpr bool kHasZ = kHasZ_;
45
+ // Setting MinBlocksPerMP to be 3 (instead of 2) for 128 threads with float improves occupancy.
46
+ // For complex this would lead to massive register spilling, so we keep it at 2.
47
+ static constexpr int kMinBlocks = kNThreads == 128 && !kIsComplex ? 3 : 2;
48
+ using vec_t = typename BytesToType<kNBytes * kNElts>::Type;
49
+ using scan_t = std::conditional_t<!kIsComplex, float2, float4>;
50
+ using BlockLoadT = cub::BlockLoad<input_t, kNThreads, kNItems, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
51
+ using BlockLoadVecT = cub::BlockLoad<vec_t, kNThreads, kNLoads, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
52
+ using BlockLoadWeightT = cub::BlockLoad<input_t, kNThreads, !kIsComplex ? kNItems : kNItems * 2, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
53
+ using BlockLoadWeightVecT = cub::BlockLoad<vec_t, kNThreads, !kIsComplex ? kNLoads : kNLoads * 2, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
54
+ using BlockStoreT = cub::BlockStore<input_t, kNThreads, kNItems, cub::BLOCK_STORE_WARP_TRANSPOSE>;
55
+ using BlockStoreVecT = cub::BlockStore<vec_t, kNThreads, kNLoads, cub::BLOCK_STORE_WARP_TRANSPOSE>;
56
+ // using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_RAKING_MEMOIZE>;
57
+ using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_RAKING>;
58
+ // using BlockScanT = cub::BlockScan<scan_t, kNThreads, cub::BLOCK_SCAN_WARP_SCANS>;
59
+ using BlockReverseScanT = BlockReverseScan<scan_t, kNThreads>;
60
+ using BlockReduceT = cub::BlockReduce<scan_t, kNThreads>;
61
+ using BlockReduceFloatT = cub::BlockReduce<float, kNThreads>;
62
+ using BlockReduceComplexT = cub::BlockReduce<complex_t, kNThreads>;
63
+ using BlockExchangeT = cub::BlockExchange<float, kNThreads, !kIsComplex ? kNItems : kNItems * 2>;
64
+ static constexpr int kSmemIOSize = std::max({sizeof(typename BlockLoadT::TempStorage),
65
+ sizeof(typename BlockLoadVecT::TempStorage),
66
+ (int(kIsVariableB) + int(kIsVariableC)) * sizeof(typename BlockLoadWeightT::TempStorage),
67
+ (int(kIsVariableB) + int(kIsVariableC)) * sizeof(typename BlockLoadWeightVecT::TempStorage),
68
+ sizeof(typename BlockStoreT::TempStorage),
69
+ sizeof(typename BlockStoreVecT::TempStorage)});
70
+ static constexpr int kSmemExchangeSize = (int(kIsVariableB) + int(kIsVariableC)) * sizeof(typename BlockExchangeT::TempStorage);
71
+ static constexpr int kSmemReduceSize = sizeof(typename BlockReduceT::TempStorage);
72
+ static constexpr int kSmemSize = kSmemIOSize + kSmemExchangeSize + kSmemReduceSize + sizeof(typename BlockScanT::TempStorage) + sizeof(typename BlockReverseScanT::TempStorage);
73
+ };
74
+
75
+ template<typename Ktraits>
76
+ __global__ __launch_bounds__(Ktraits::kNThreads, Ktraits::kMinBlocks)
77
+ void selective_scan_bwd_kernel(SSMParamsBwd params) {
78
+ constexpr bool kIsComplex = Ktraits::kIsComplex;
79
+ constexpr bool kIsVariableB = Ktraits::kIsVariableB;
80
+ constexpr bool kIsVariableC = Ktraits::kIsVariableC;
81
+ constexpr bool kDeltaSoftplus = Ktraits::kDeltaSoftplus;
82
+ constexpr bool kHasZ = Ktraits::kHasZ;
83
+ constexpr int kNThreads = Ktraits::kNThreads;
84
+ constexpr int kNItems = Ktraits::kNItems;
85
+ using input_t = typename Ktraits::input_t;
86
+ using weight_t = typename Ktraits::weight_t;
87
+ using scan_t = typename Ktraits::scan_t;
88
+
89
+ // Shared memory.
90
+ extern __shared__ char smem_[];
91
+ // cast to lvalue reference of expected type
92
+ // char *smem_loadstorescan = smem_ + 2 * MAX_DSTATE * sizeof(weight_t);
93
+ // auto& smem_load = reinterpret_cast<typename BlockLoadT::TempStorage&>(smem_ + 2 * MAX_DSTATE * sizeof(weight_t));
94
+ // auto& smem_load = reinterpret_cast<typename BlockLoadT::TempStorage&>(smem_loadstorescan);
95
+ auto& smem_load = reinterpret_cast<typename Ktraits::BlockLoadT::TempStorage&>(smem_);
96
+ auto& smem_load_weight = reinterpret_cast<typename Ktraits::BlockLoadWeightT::TempStorage&>(smem_);
97
+ auto& smem_load_weight1 = *reinterpret_cast<typename Ktraits::BlockLoadWeightT::TempStorage*>(smem_ + sizeof(typename Ktraits::BlockLoadWeightT::TempStorage));
98
+ auto& smem_store = reinterpret_cast<typename Ktraits::BlockStoreT::TempStorage&>(smem_);
99
+ auto& smem_exchange = *reinterpret_cast<typename Ktraits::BlockExchangeT::TempStorage*>(smem_ + Ktraits::kSmemIOSize);
100
+ auto& smem_exchange1 = *reinterpret_cast<typename Ktraits::BlockExchangeT::TempStorage*>(smem_ + Ktraits::kSmemIOSize + sizeof(typename Ktraits::BlockExchangeT::TempStorage));
101
+ auto& smem_reduce = *reinterpret_cast<typename Ktraits::BlockReduceT::TempStorage*>(reinterpret_cast<char *>(&smem_exchange) + Ktraits::kSmemExchangeSize);
102
+ auto& smem_reduce_float = *reinterpret_cast<typename Ktraits::BlockReduceFloatT::TempStorage*>(&smem_reduce);
103
+ auto& smem_reduce_complex = *reinterpret_cast<typename Ktraits::BlockReduceComplexT::TempStorage*>(&smem_reduce);
104
+ auto& smem_scan = *reinterpret_cast<typename Ktraits::BlockScanT::TempStorage*>(reinterpret_cast<char *>(&smem_reduce) + Ktraits::kSmemReduceSize);
105
+ auto& smem_reverse_scan = *reinterpret_cast<typename Ktraits::BlockReverseScanT::TempStorage*>(reinterpret_cast<char *>(&smem_scan) + sizeof(typename Ktraits::BlockScanT::TempStorage));
106
+ weight_t *smem_delta_a = reinterpret_cast<weight_t *>(smem_ + Ktraits::kSmemSize);
107
+ scan_t *smem_running_postfix = reinterpret_cast<scan_t *>(smem_delta_a + 2 * MAX_DSTATE + kNThreads);
108
+ weight_t *smem_da = reinterpret_cast<weight_t *>(smem_running_postfix + MAX_DSTATE);
109
+ weight_t *smem_dbc = reinterpret_cast<weight_t *>(smem_da + MAX_DSTATE);
110
+
111
+ const int batch_id = blockIdx.x;
112
+ const int dim_id = blockIdx.y;
113
+ const int group_id = dim_id / (params.dim_ngroups_ratio);
114
+ input_t *u = reinterpret_cast<input_t *>(params.u_ptr) + batch_id * params.u_batch_stride
115
+ + dim_id * params.u_d_stride;
116
+ input_t *delta = reinterpret_cast<input_t *>(params.delta_ptr) + batch_id * params.delta_batch_stride
117
+ + dim_id * params.delta_d_stride;
118
+ input_t *dout = reinterpret_cast<input_t *>(params.dout_ptr) + batch_id * params.dout_batch_stride
119
+ + dim_id * params.dout_d_stride;
120
+ weight_t *A = reinterpret_cast<weight_t *>(params.A_ptr) + dim_id * params.A_d_stride;
121
+ weight_t *B = reinterpret_cast<weight_t *>(params.B_ptr) + dim_id * params.B_d_stride;
122
+ input_t *Bvar = reinterpret_cast<input_t *>(params.B_ptr) + batch_id * params.B_batch_stride + group_id * params.B_group_stride;
123
+ weight_t *C = reinterpret_cast<weight_t *>(params.C_ptr) + dim_id * params.C_d_stride;
124
+ input_t *Cvar = reinterpret_cast<input_t *>(params.C_ptr) + batch_id * params.C_batch_stride + group_id * params.C_group_stride;
125
+ weight_t *dA = reinterpret_cast<weight_t *>(params.dA_ptr) + dim_id * params.dA_d_stride;
126
+ weight_t *dB = reinterpret_cast<weight_t *>(params.dB_ptr)
127
+ + (!kIsVariableB ? dim_id * params.dB_d_stride : batch_id * (!kIsComplex ? params.dB_batch_stride : params.dB_batch_stride / 2) + group_id * params.dB_group_stride);
128
+ weight_t *dC = reinterpret_cast<weight_t *>(params.dC_ptr)
129
+ + (!kIsVariableC ? dim_id * params.dC_d_stride : batch_id * (!kIsComplex ? params.dC_batch_stride : params.dC_batch_stride / 2) + group_id * params.dC_group_stride);
130
+ float *dD = params.dD_ptr == nullptr ? nullptr : reinterpret_cast<float *>(params.dD_ptr) + dim_id;
131
+ float D_val = params.D_ptr == nullptr ? 0 : reinterpret_cast<float *>(params.D_ptr)[dim_id];
132
+ float *ddelta_bias = params.ddelta_bias_ptr == nullptr ? nullptr : reinterpret_cast<float *>(params.ddelta_bias_ptr) + dim_id;
133
+ float delta_bias = params.delta_bias_ptr == nullptr ? 0 : reinterpret_cast<float *>(params.delta_bias_ptr)[dim_id];
134
+ scan_t *x = params.x_ptr == nullptr
135
+ ? nullptr
136
+ : reinterpret_cast<scan_t *>(params.x_ptr) + (batch_id * params.dim + dim_id) * (params.n_chunks) * params.dstate;
137
+ float dD_val = 0;
138
+ float ddelta_bias_val = 0;
139
+
140
+ constexpr int kChunkSize = kNThreads * kNItems;
141
+ u += (params.n_chunks - 1) * kChunkSize;
142
+ delta += (params.n_chunks - 1) * kChunkSize;
143
+ dout += (params.n_chunks - 1) * kChunkSize;
144
+ Bvar += (params.n_chunks - 1) * kChunkSize * (!kIsComplex ? 1 : 2);
145
+ Cvar += (params.n_chunks - 1) * kChunkSize * (!kIsComplex ? 1 : 2);
146
+ for (int chunk = params.n_chunks - 1; chunk >= 0; --chunk) {
147
+ input_t u_vals[kNItems];
148
+ input_t delta_vals_load[kNItems];
149
+ input_t dout_vals_load[kNItems];
150
+ __syncthreads();
151
+ load_input<Ktraits>(u, u_vals, smem_load, params.seqlen - chunk * kChunkSize);
152
+ u -= kChunkSize;
153
+ __syncthreads();
154
+ load_input<Ktraits>(delta, delta_vals_load, smem_load, params.seqlen - chunk * kChunkSize);
155
+ // Will reload delta at the same location if kDeltaSoftplus
156
+ if constexpr (!kDeltaSoftplus) { delta -= kChunkSize; }
157
+ __syncthreads();
158
+ load_input<Ktraits>(dout, dout_vals_load, smem_load, params.seqlen - chunk * kChunkSize);
159
+ dout -= kChunkSize;
160
+
161
+ float dout_vals[kNItems], delta_vals[kNItems];
162
+ #pragma unroll
163
+ for (int i = 0; i < kNItems; ++i) {
164
+ dout_vals[i] = float(dout_vals_load[i]);
165
+ delta_vals[i] = float(delta_vals_load[i]) + delta_bias;
166
+ if constexpr (kDeltaSoftplus) {
167
+ delta_vals[i] = delta_vals[i] <= 20.f ? log1pf(expf(delta_vals[i])) : delta_vals[i];
168
+ }
169
+ }
170
+
171
+ if constexpr (kHasZ) {
172
+ input_t *z = reinterpret_cast<input_t *>(params.z_ptr) + batch_id * params.z_batch_stride
173
+ + dim_id * params.z_d_stride + chunk * kChunkSize;
174
+ input_t *out = reinterpret_cast<input_t *>(params.out_ptr) + batch_id * params.out_batch_stride
175
+ + dim_id * params.out_d_stride + chunk * kChunkSize;
176
+ input_t *dz = reinterpret_cast<input_t *>(params.dz_ptr) + batch_id * params.dz_batch_stride
177
+ + dim_id * params.dz_d_stride + chunk * kChunkSize;
178
+ input_t z_vals[kNItems], out_vals[kNItems];
179
+ __syncthreads();
180
+ load_input<Ktraits>(z, z_vals, smem_load, params.seqlen - chunk * kChunkSize);
181
+ __syncthreads();
182
+ load_input<Ktraits>(out, out_vals, smem_load, params.seqlen - chunk * kChunkSize);
183
+ float dz_vals[kNItems], z_silu_vals[kNItems];
184
+ #pragma unroll
185
+ for (int i = 0; i < kNItems; ++i) {
186
+ float z_val = z_vals[i];
187
+ float z_sigmoid_val = 1.0f / (1.0f + expf(-z_val));
188
+ z_silu_vals[i] = z_val * z_sigmoid_val;
189
+ dz_vals[i] = dout_vals[i] * float(out_vals[i]) * z_sigmoid_val
190
+ * (1.0f + z_val * (1.0f - z_sigmoid_val));
191
+ dout_vals[i] *= z_silu_vals[i];
192
+ }
193
+ __syncthreads();
194
+ store_output<Ktraits>(dz, dz_vals, smem_store, params.seqlen - chunk * kChunkSize);
195
+ if (params.out_z_ptr != nullptr) { // Recompute and store out_z
196
+ float out_z_vals[kNItems];
197
+ #pragma unroll
198
+ for (int i = 0; i < kNItems; ++i) { out_z_vals[i] = float(out_vals[i]) * z_silu_vals[i]; }
199
+ // if (blockIdx.x == 0 && blockIdx.y == 0 && threadIdx.x == 0) {
200
+ // printf("out_val=%f, z_silu_val = %f, out_z_val = %f\n", float(out_vals[0]), z_silu_vals[0], out_z_vals[0]);
201
+ // }
202
+ input_t *out_z = reinterpret_cast<input_t *>(params.out_z_ptr) + batch_id * params.out_z_batch_stride
203
+ + dim_id * params.out_z_d_stride + chunk * kChunkSize;
204
+ __syncthreads();
205
+ store_output<Ktraits>(out_z, out_z_vals, smem_store, params.seqlen - chunk * kChunkSize);
206
+ }
207
+ }
208
+
209
+ float du_vals[kNItems];
210
+ #pragma unroll
211
+ for (int i = 0; i < kNItems; ++i) { du_vals[i] = D_val * dout_vals[i]; }
212
+ #pragma unroll
213
+ for (int i = 0; i < kNItems; ++i) { dD_val += dout_vals[i] * float(u_vals[i]); }
214
+
215
+ float ddelta_vals[kNItems] = {0};
216
+ __syncthreads();
217
+ for (int state_idx = 0; state_idx < params.dstate; ++state_idx) {
218
+ const weight_t A_val = A[state_idx * params.A_dstate_stride];
219
+ // Multiply the real part of A with LOG2E so we can use exp2f instead of expf.
220
+ weight_t A_scaled;
221
+ constexpr float kLog2e = M_LOG2E;
222
+ if constexpr (!kIsComplex) {
223
+ A_scaled = A_val * kLog2e;
224
+ } else {
225
+ A_scaled = complex_t(A_val.real_ * kLog2e, A_val.imag_);
226
+ }
227
+ weight_t B_val, C_val;
228
+ weight_t B_vals[kNItems], C_vals[kNItems];
229
+ if constexpr (!kIsVariableB) {
230
+ B_val = B[state_idx * params.B_dstate_stride];
231
+ } else {
232
+ load_weight<Ktraits>(Bvar + state_idx * params.B_dstate_stride, B_vals,
233
+ smem_load_weight, (params.seqlen - chunk * kChunkSize) * (!kIsComplex ? 1 : 2));
234
+ }
235
+ if constexpr (!kIsVariableC) {
236
+ C_val = C[state_idx * params.C_dstate_stride];
237
+ } else {
238
+ auto &smem_load_weight_C = !kIsVariableB ? smem_load_weight : smem_load_weight1;
239
+ load_weight<Ktraits>(Cvar + state_idx * params.C_dstate_stride, C_vals,
240
+ smem_load_weight_C, (params.seqlen - chunk * kChunkSize) * (!kIsComplex ? 1 : 2));
241
+ }
242
+ // const weight_t A_val = smem_a[state_idx];
243
+ scan_t thread_data[kNItems], thread_reverse_data[kNItems];
244
+ if constexpr (!kIsComplex) {
245
+ #pragma unroll
246
+ for (int i = 0; i < kNItems; ++i) {
247
+ const float delta_a_exp = exp2f(delta_vals[i] * A_scaled);
248
+ thread_data[i] = make_float2(delta_a_exp, !kIsVariableB ? delta_vals[i] * float(u_vals[i]) : delta_vals[i] * float(u_vals[i]) * B_vals[i]);
249
+ if (i == 0) {
250
+ smem_delta_a[threadIdx.x == 0 ? state_idx + (chunk % 2) * MAX_DSTATE : threadIdx.x + 2 * MAX_DSTATE] = delta_a_exp;
251
+ } else {
252
+ thread_reverse_data[i - 1].x = delta_a_exp;
253
+ }
254
+ thread_reverse_data[i].y = dout_vals[i] *
255
+ (!kIsVariableC
256
+ ? (!kIsVariableB ? B_val * C_val : C_val)
257
+ : (!kIsVariableB ? B_val * C_vals[i] : C_vals[i]));
258
+ }
259
+ __syncthreads();
260
+ thread_reverse_data[kNItems - 1].x = threadIdx.x == kNThreads - 1
261
+ ? (chunk == params.n_chunks - 1 ? 1.f : smem_delta_a[state_idx + ((chunk + 1) % 2) * MAX_DSTATE])
262
+ : smem_delta_a[threadIdx.x + 1 + 2 * MAX_DSTATE];
263
+ // Initialize running total
264
+ scan_t running_prefix = chunk > 0 && threadIdx.x % 32 == 0 ? x[(chunk - 1) * params.dstate + state_idx] : make_float2(1.f, 0.f);
265
+ SSMScanPrefixCallbackOp<weight_t> prefix_op(running_prefix);
266
+ Ktraits::BlockScanT(smem_scan).InclusiveScan(
267
+ thread_data, thread_data, SSMScanOp<weight_t>(), prefix_op
268
+ );
269
+ scan_t running_postfix = chunk < params.n_chunks - 1 && threadIdx.x % 32 == 0 ? smem_running_postfix[state_idx] : make_float2(1.f, 0.f);
270
+ SSMScanPrefixCallbackOp<weight_t> postfix_op(running_postfix);
271
+ Ktraits::BlockReverseScanT(smem_reverse_scan).InclusiveReverseScan(
272
+ thread_reverse_data, thread_reverse_data, SSMScanOp<weight_t>(), postfix_op
273
+ );
274
+ if (threadIdx.x == 0) { smem_running_postfix[state_idx] = postfix_op.running_prefix; }
275
+ weight_t dA_val = 0, dBC_val = 0;
276
+ weight_t dB_vals[kNItems], dC_vals[kNItems];
277
+ #pragma unroll
278
+ for (int i = 0; i < kNItems; ++i) {
279
+ const float dx = thread_reverse_data[i].y;
280
+ const float ddelta_u = !kIsVariableB ? dx : dx * B_vals[i];
281
+ du_vals[i] += ddelta_u * delta_vals[i];
282
+ const float a = thread_data[i].y - (!kIsVariableB ? delta_vals[i] * float(u_vals[i]) : delta_vals[i] * float(u_vals[i]) * B_vals[i]);
283
+ ddelta_vals[i] += ddelta_u * float(u_vals[i]) + dx * A_val * a;
284
+ dA_val += dx * delta_vals[i] * a;
285
+ if constexpr (!kIsVariableB || !kIsVariableC) {
286
+ if constexpr (!kIsVariableB) { // dBC_val is dB_val
287
+ dBC_val += dout_vals[i] * (!kIsVariableC ? thread_data[i].y : thread_data[i].y * C_vals[i]);
288
+ } else { // dBC_val is dC_val
289
+ dBC_val += dout_vals[i] * thread_data[i].y;
290
+ }
291
+ }
292
+ if constexpr (kIsVariableB) { dB_vals[i] = dx * delta_vals[i] * float(u_vals[i]); }
293
+ if constexpr (kIsVariableC) {
294
+ dC_vals[i] = dout_vals[i] * (!kIsVariableB ? thread_data[i].y * B_val : thread_data[i].y);
295
+ }
296
+ }
297
+ // Block-exchange to make the atomicAdd's coalesced, otherwise they're much slower
298
+ if constexpr (kIsVariableB || kIsVariableC) {
299
+ if constexpr (kIsVariableB) {
300
+ Ktraits::BlockExchangeT(smem_exchange).BlockedToStriped(dB_vals, dB_vals);
301
+ }
302
+ if constexpr (kIsVariableC) {
303
+ auto &smem_exchange_C = !kIsVariableB ? smem_exchange : smem_exchange1;
304
+ Ktraits::BlockExchangeT(smem_exchange_C).BlockedToStriped(dC_vals, dC_vals);
305
+ }
306
+ const int seqlen_remaining = params.seqlen - chunk * kChunkSize - threadIdx.x;
307
+ weight_t *dB_cur = dB + state_idx * params.dB_dstate_stride + chunk * kChunkSize + threadIdx.x;
308
+ weight_t *dC_cur = dC + state_idx * params.dC_dstate_stride + chunk * kChunkSize + threadIdx.x;
309
+ #pragma unroll
310
+ for (int i = 0; i < kNItems; ++i) {
311
+ if (i * kNThreads < seqlen_remaining) {
312
+ if constexpr (kIsVariableB) { gpuAtomicAdd(dB_cur + i * kNThreads, dB_vals[i]); }
313
+ if constexpr (kIsVariableC) { gpuAtomicAdd(dC_cur + i * kNThreads, dC_vals[i]); }
314
+ }
315
+ }
316
+ }
317
+ if constexpr (!kIsVariableB || !kIsVariableC) {
318
+ float2 dA_dBC_val = make_float2(dA_val, dBC_val);
319
+ dA_dBC_val = Ktraits::BlockReduceT(smem_reduce).Sum(dA_dBC_val);
320
+ dA_val = dA_dBC_val.x;
321
+ if (threadIdx.x == 0) {
322
+ smem_dbc[state_idx] = chunk == params.n_chunks - 1 ? dA_dBC_val.y : dA_dBC_val.y + smem_dbc[state_idx];
323
+ }
324
+ } else {
325
+ dA_val = Ktraits::BlockReduceFloatT(smem_reduce_float).Sum(dA_val);
326
+ }
327
+ if (threadIdx.x == 0) {
328
+ smem_da[state_idx] = chunk == params.n_chunks - 1 ? dA_val : dA_val + smem_da[state_idx];
329
+ }
330
+ } else {
331
+ #pragma unroll
332
+ for (int i = 0; i < kNItems; ++i) {
333
+ // Pytorch's implementation of complex exp (which calls thrust) is very slow
334
+ complex_t delta_a_exp = cexp2f(delta_vals[i] * A_scaled);
335
+ weight_t B_delta_u_val = !kIsVariableB ? delta_vals[i] * float(u_vals[i]) : B_vals[i] * delta_vals[i] * float(u_vals[i]);
336
+ thread_data[i] = make_float4(delta_a_exp.real_, delta_a_exp.imag_, B_delta_u_val.real_, B_delta_u_val.imag_);
337
+ if (i == 0) {
338
+ smem_delta_a[threadIdx.x == 0 ? state_idx + (chunk % 2) * MAX_DSTATE : threadIdx.x + 2 * MAX_DSTATE] = delta_a_exp;
339
+ } else {
340
+ thread_reverse_data[i - 1].x = delta_a_exp.real_;
341
+ thread_reverse_data[i - 1].y = -delta_a_exp.imag_;
342
+ }
343
+ complex_t dout_BC = 2 * dout_vals[i]
344
+ * conj(!kIsVariableC
345
+ ? (!kIsVariableB ? B_val * C_val : C_val)
346
+ : (!kIsVariableB ? B_val * C_vals[i] : C_vals[i]));
347
+ thread_reverse_data[i].z = dout_BC.real_;
348
+ thread_reverse_data[i].w = dout_BC.imag_;
349
+ }
350
+ __syncthreads();
351
+ complex_t delta_a_exp = threadIdx.x == kNThreads - 1
352
+ ? (chunk == params.n_chunks - 1 ? 1.f : smem_delta_a[state_idx + ((chunk + 1) % 2) * MAX_DSTATE])
353
+ : smem_delta_a[threadIdx.x + 1 + 2 * MAX_DSTATE];
354
+ thread_reverse_data[kNItems - 1].x = delta_a_exp.real_;
355
+ thread_reverse_data[kNItems - 1].y = -delta_a_exp.imag_;
356
+ // Initialize running total
357
+ scan_t running_prefix = chunk > 0 && threadIdx.x % 32 == 0 ? x[(chunk - 1) * params.dstate + state_idx] : make_float4(1.f, 0.f, 0.f, 0.f);
358
+ SSMScanPrefixCallbackOp<weight_t> prefix_op(running_prefix);
359
+ Ktraits::BlockScanT(smem_scan).InclusiveScan(
360
+ thread_data, thread_data, SSMScanOp<weight_t>(), prefix_op
361
+ );
362
+ scan_t running_postfix = chunk < params.n_chunks - 1 && threadIdx.x % 32 == 0 ? smem_running_postfix[state_idx] : make_float4(1.f, 0.f, 0.f, 0.f);
363
+ SSMScanPrefixCallbackOp<weight_t> postfix_op(running_postfix);
364
+ Ktraits::BlockReverseScanT(smem_reverse_scan).InclusiveReverseScan(
365
+ thread_reverse_data, thread_reverse_data, SSMScanOp<weight_t>(), postfix_op
366
+ );
367
+ if (threadIdx.x == 0) { smem_running_postfix[state_idx] = postfix_op.running_prefix; }
368
+ weight_t dA_val = 0, dBC_val = 0;
369
+ weight_t dB_vals[kNItems], dC_vals[kNItems];
370
+ #pragma unroll
371
+ for (int i = 0; i < kNItems; ++i) {
372
+ complex_t x = complex_t(thread_data[i].z, thread_data[i].w);
373
+ complex_t dx = complex_t(thread_reverse_data[i].z, thread_reverse_data[i].w);
374
+ float ddelta_u = !kIsVariableB ? dx.real_ : (dx * conj(B_vals[i])).real_;
375
+ if constexpr (!kIsVariableB || !kIsVariableC) {
376
+ if constexpr (!kIsVariableB) { // dBC_val is dB_val
377
+ dBC_val += (2 * dout_vals[i]) * conj(!kIsVariableC ? x : x * C_vals[i]);
378
+ } else { // dBC_val is dC_val
379
+ dBC_val += (2 * dout_vals[i]) * conj(x);
380
+ }
381
+ }
382
+ const complex_t a_conj = conj(x - (!kIsVariableB ? delta_vals[i] * float(u_vals[i]) : delta_vals[i] * float(u_vals[i]) * B_vals[i]));
383
+ du_vals[i] += ddelta_u * delta_vals[i];
384
+ ddelta_vals[i] += ddelta_u * float(u_vals[i]) + (dx * conj(A_val) * a_conj).real_;
385
+ dA_val += delta_vals[i] * dx * a_conj;
386
+ if constexpr (kIsVariableB) { dB_vals[i] = dx * delta_vals[i] * float(u_vals[i]); }
387
+ if constexpr (kIsVariableC) {
388
+ dC_vals[i] = (2 * dout_vals[i]) * conj(!kIsVariableB ? x * B_val : x);
389
+ }
390
+ }
391
+ // Block-exchange to make the atomicAdd's coalesced, otherwise they're much slower
392
+ if constexpr (kIsVariableB || kIsVariableC) {
393
+ float dB_vals_f[kNItems * 2], dC_vals_f[kNItems * 2];
394
+ if constexpr (kIsVariableB) {
395
+ #pragma unroll
396
+ for (int i = 0; i < kNItems; ++i) {
397
+ dB_vals_f[i * 2] = dB_vals[i].real_;
398
+ dB_vals_f[i * 2 + 1] = dB_vals[i].imag_;
399
+ }
400
+ Ktraits::BlockExchangeT(smem_exchange).BlockedToStriped(dB_vals_f, dB_vals_f);
401
+ }
402
+ if constexpr (kIsVariableC) {
403
+ #pragma unroll
404
+ for (int i = 0; i < kNItems; ++i) {
405
+ dC_vals_f[i * 2] = dC_vals[i].real_;
406
+ dC_vals_f[i * 2 + 1] = dC_vals[i].imag_;
407
+ }
408
+ auto &smem_exchange_C = !kIsVariableB ? smem_exchange : smem_exchange1;
409
+ Ktraits::BlockExchangeT(smem_exchange_C).BlockedToStriped(dC_vals_f, dC_vals_f);
410
+ }
411
+ const int seqlen_remaining = (params.seqlen - chunk * kChunkSize) * 2 - threadIdx.x;
412
+ float *dB_cur = reinterpret_cast<float *>(dB) + state_idx * params.dB_dstate_stride + chunk * kChunkSize * 2 + threadIdx.x;
413
+ float *dC_cur = reinterpret_cast<float *>(dC) + state_idx * params.dC_dstate_stride + chunk * kChunkSize * 2 + threadIdx.x;
414
+ #pragma unroll
415
+ for (int i = 0; i < kNItems * 2; ++i) {
416
+ if (i * kNThreads < seqlen_remaining) {
417
+ if constexpr (kIsVariableB) { gpuAtomicAdd(dB_cur + i * kNThreads, dB_vals_f[i]); }
418
+ if constexpr (kIsVariableC) { gpuAtomicAdd(dC_cur + i * kNThreads, dC_vals_f[i]); }
419
+ }
420
+ }
421
+ }
422
+ if constexpr (!kIsVariableB || !kIsVariableC) {
423
+ float4 dA_dBC_val = make_float4(dA_val.real_, dA_val.imag_, dBC_val.real_, dBC_val.imag_);
424
+ dA_dBC_val = Ktraits::BlockReduceT(smem_reduce).Sum(dA_dBC_val);
425
+ dA_val = complex_t(dA_dBC_val.x, dA_dBC_val.y);
426
+ dBC_val = complex_t(dA_dBC_val.z, dA_dBC_val.w);
427
+ if (threadIdx.x == 0) {
428
+ smem_dbc[state_idx] = chunk == params.n_chunks - 1 ? dBC_val : dBC_val + smem_dbc[state_idx];
429
+ }
430
+ } else {
431
+ dA_val = Ktraits::BlockReduceComplexT(smem_reduce_complex).Sum(dA_val);
432
+ }
433
+ if (threadIdx.x == 0) {
434
+ smem_da[state_idx] = chunk == params.n_chunks - 1 ? dA_val : dA_val + smem_da[state_idx];
435
+ }
436
+ }
437
+ }
438
+
439
+ if constexpr (kDeltaSoftplus) {
440
+ __syncthreads();
441
+ input_t delta_vals_load[kNItems];
442
+ load_input<Ktraits>(delta, delta_vals_load, smem_load, params.seqlen - chunk * kChunkSize);
443
+ delta -= kChunkSize;
444
+ #pragma unroll
445
+ for (int i = 0; i < kNItems; ++i) {
446
+ float delta_val = float(delta_vals_load[i]) + delta_bias;
447
+ float delta_val_neg_exp = expf(-delta_val);
448
+ ddelta_vals[i] = delta_val <= 20.f
449
+ ? ddelta_vals[i] / (1.f + delta_val_neg_exp)
450
+ : ddelta_vals[i];
451
+ }
452
+ }
453
+ for (int i = 0; i < kNItems; ++i) { ddelta_bias_val += ddelta_vals[i]; }
454
+
455
+ input_t *du = reinterpret_cast<input_t *>(params.du_ptr) + batch_id * params.du_batch_stride
456
+ + dim_id * params.du_d_stride + chunk * kChunkSize;
457
+ input_t *ddelta = reinterpret_cast<input_t *>(params.ddelta_ptr) + batch_id * params.ddelta_batch_stride
458
+ + dim_id * params.ddelta_d_stride + chunk * kChunkSize;
459
+ __syncthreads();
460
+ store_output<Ktraits>(du, du_vals, smem_store, params.seqlen - chunk * kChunkSize);
461
+ __syncthreads();
462
+ store_output<Ktraits>(ddelta, ddelta_vals, smem_store, params.seqlen - chunk * kChunkSize);
463
+
464
+ Bvar -= kChunkSize * (!kIsComplex ? 1 : 2);
465
+ Cvar -= kChunkSize * (!kIsComplex ? 1 : 2);
466
+ }
467
+ if (params.dD_ptr != nullptr) {
468
+ dD_val = Ktraits::BlockReduceFloatT(smem_reduce_float).Sum(dD_val);
469
+ if (threadIdx.x == 0) { gpuAtomicAdd(dD, dD_val); }
470
+ }
471
+ if (params.ddelta_bias_ptr != nullptr) {
472
+ __syncthreads();
473
+ ddelta_bias_val = Ktraits::BlockReduceFloatT(smem_reduce_float).Sum(ddelta_bias_val);
474
+ if (threadIdx.x == 0) { gpuAtomicAdd(ddelta_bias, ddelta_bias_val); }
475
+ }
476
+ for (int state_idx = threadIdx.x; state_idx < params.dstate; state_idx += blockDim.x) {
477
+ gpuAtomicAdd(&(dA[state_idx * params.dA_dstate_stride]), smem_da[state_idx]);
478
+ weight_t dBC_val;
479
+ if (!kIsVariableB || !kIsVariableC) { dBC_val = smem_dbc[state_idx]; }
480
+ if constexpr (!kIsVariableB) {
481
+ gpuAtomicAdd(&(dB[state_idx * params.dB_dstate_stride]),
482
+ !kIsVariableC ? dBC_val * conj(C[state_idx * params.C_dstate_stride]) : dBC_val);
483
+ }
484
+ if constexpr (!kIsVariableC) {
485
+ gpuAtomicAdd(&(dC[state_idx * params.dC_dstate_stride]),
486
+ !kIsVariableB ? dBC_val * conj(B[state_idx * params.B_dstate_stride]) : dBC_val);
487
+ }
488
+ }
489
+ }
490
+
491
+ template<int kNThreads, int kNItems, typename input_t, typename weight_t>
492
+ void selective_scan_bwd_launch(SSMParamsBwd &params, cudaStream_t stream) {
493
+ BOOL_SWITCH(params.seqlen % (kNThreads * kNItems) == 0, kIsEvenLen, [&] {
494
+ BOOL_SWITCH(params.is_variable_B, kIsVariableB, [&] {
495
+ BOOL_SWITCH(params.is_variable_C, kIsVariableC, [&] {
496
+ BOOL_SWITCH(params.delta_softplus, kDeltaSoftplus, [&] {
497
+ BOOL_SWITCH(params.z_ptr != nullptr , kHasZ, [&] {
498
+ using Ktraits = Selective_Scan_bwd_kernel_traits<kNThreads, kNItems, kIsEvenLen, kIsVariableB, kIsVariableC, kDeltaSoftplus, kHasZ, input_t, weight_t>;
499
+ // using Ktraits = Selective_Scan_bwd_kernel_traits<kNThreads, kNItems, true, kIsVariableB, kIsVariableC, kDeltaSoftplus, kHasZ, input_t, weight_t>;
500
+ // TODO: check this
501
+ constexpr int kSmemSize = Ktraits::kSmemSize + MAX_DSTATE * sizeof(typename Ktraits::scan_t) + (kNThreads + 4 * MAX_DSTATE) * sizeof(typename Ktraits::weight_t);
502
+ // printf("smem_size = %d\n", kSmemSize);
503
+ dim3 grid(params.batch, params.dim);
504
+ auto kernel = &selective_scan_bwd_kernel<Ktraits>;
505
+ if (kSmemSize >= 48 * 1024) {
506
+ C10_CUDA_CHECK(cudaFuncSetAttribute(
507
+ kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, kSmemSize));
508
+ }
509
+ kernel<<<grid, Ktraits::kNThreads, kSmemSize, stream>>>(params);
510
+ C10_CUDA_KERNEL_LAUNCH_CHECK();
511
+ });
512
+ });
513
+ });
514
+ });
515
+ });
516
+ }
517
+
518
+ template<typename input_t, typename weight_t>
519
+ void selective_scan_bwd_cuda(SSMParamsBwd &params, cudaStream_t stream) {
520
+ if (params.seqlen <= 128) {
521
+ selective_scan_bwd_launch<32, 4, input_t, weight_t>(params, stream);
522
+ } else if (params.seqlen <= 256) {
523
+ selective_scan_bwd_launch<32, 8, input_t, weight_t>(params, stream);
524
+ } else if (params.seqlen <= 512) {
525
+ selective_scan_bwd_launch<32, 16, input_t, weight_t>(params, stream);
526
+ } else if (params.seqlen <= 1024) {
527
+ selective_scan_bwd_launch<64, 16, input_t, weight_t>(params, stream);
528
+ } else {
529
+ selective_scan_bwd_launch<128, 16, input_t, weight_t>(params, stream);
530
+ }
531
+ }
protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_common.h ADDED
@@ -0,0 +1,221 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ #pragma once
6
+
7
+ #include <cuda_bf16.h>
8
+ #include <cuda_fp16.h>
9
+ #include <c10/util/complex.h> // For scalar_value_type
10
+
11
+ #define MAX_DSTATE 256
12
+
13
+ using complex_t = c10::complex<float>;
14
+
15
+ inline __device__ float2 operator+(const float2 & a, const float2 & b){
16
+ return {a.x + b.x, a.y + b.y};
17
+ }
18
+
19
+ inline __device__ float3 operator+(const float3 &a, const float3 &b) {
20
+ return {a.x + b.x, a.y + b.y, a.z + b.z};
21
+ }
22
+
23
+ inline __device__ float4 operator+(const float4 & a, const float4 & b){
24
+ return {a.x + b.x, a.y + b.y, a.z + b.z, a.w + b.w};
25
+ }
26
+
27
+ ////////////////////////////////////////////////////////////////////////////////////////////////////
28
+
29
+ template<int BYTES> struct BytesToType {};
30
+
31
+ template<> struct BytesToType<16> {
32
+ using Type = uint4;
33
+ static_assert(sizeof(Type) == 16);
34
+ };
35
+
36
+ template<> struct BytesToType<8> {
37
+ using Type = uint64_t;
38
+ static_assert(sizeof(Type) == 8);
39
+ };
40
+
41
+ template<> struct BytesToType<4> {
42
+ using Type = uint32_t;
43
+ static_assert(sizeof(Type) == 4);
44
+ };
45
+
46
+ template<> struct BytesToType<2> {
47
+ using Type = uint16_t;
48
+ static_assert(sizeof(Type) == 2);
49
+ };
50
+
51
+ template<> struct BytesToType<1> {
52
+ using Type = uint8_t;
53
+ static_assert(sizeof(Type) == 1);
54
+ };
55
+
56
+ ////////////////////////////////////////////////////////////////////////////////////////////////////
57
+
58
+ template<typename scalar_t, int N>
59
+ struct Converter{
60
+ static inline __device__ void to_float(const scalar_t (&src)[N], float (&dst)[N]) {
61
+ #pragma unroll
62
+ for (int i = 0; i < N; ++i) { dst[i] = src[i]; }
63
+ }
64
+ };
65
+
66
+ template<int N>
67
+ struct Converter<at::Half, N>{
68
+ static inline __device__ void to_float(const at::Half (&src)[N], float (&dst)[N]) {
69
+ static_assert(N % 2 == 0);
70
+ auto &src2 = reinterpret_cast<const half2 (&)[N / 2]>(src);
71
+ auto &dst2 = reinterpret_cast<float2 (&)[N / 2]>(dst);
72
+ #pragma unroll
73
+ for (int i = 0; i < N / 2; ++i) { dst2[i] = __half22float2(src2[i]); }
74
+ }
75
+ };
76
+
77
+ #if __CUDA_ARCH__ >= 800
78
+ template<int N>
79
+ struct Converter<at::BFloat16, N>{
80
+ static inline __device__ void to_float(const at::BFloat16 (&src)[N], float (&dst)[N]) {
81
+ static_assert(N % 2 == 0);
82
+ auto &src2 = reinterpret_cast<const nv_bfloat162 (&)[N / 2]>(src);
83
+ auto &dst2 = reinterpret_cast<float2 (&)[N / 2]>(dst);
84
+ #pragma unroll
85
+ for (int i = 0; i < N / 2; ++i) { dst2[i] = __bfloat1622float2(src2[i]); }
86
+ }
87
+ };
88
+ #endif
89
+
90
+ ////////////////////////////////////////////////////////////////////////////////////////////////////
91
+
92
+ // From https://stackoverflow.com/questions/9860711/cucomplex-h-and-exp
93
+ // and https://forums.developer.nvidia.com/t/complex-number-exponential-function/24696
94
+ __device__ __forceinline__ complex_t cexp2f(complex_t z) {
95
+ float t = exp2f(z.real_);
96
+ float c, s;
97
+ sincosf(z.imag_, &s, &c);
98
+ return complex_t(c * t, s * t);
99
+ }
100
+
101
+ __device__ __forceinline__ complex_t cexpf(complex_t z) {
102
+ float t = expf(z.real_);
103
+ float c, s;
104
+ sincosf(z.imag_, &s, &c);
105
+ return complex_t(c * t, s * t);
106
+ }
107
+
108
+ template<typename scalar_t> struct SSMScanOp;
109
+
110
+ template<>
111
+ struct SSMScanOp<float> {
112
+ __device__ __forceinline__ float2 operator()(const float2 &ab0, const float2 &ab1) const {
113
+ return make_float2(ab1.x * ab0.x, ab1.x * ab0.y + ab1.y);
114
+ }
115
+ };
116
+
117
+ template<>
118
+ struct SSMScanOp<complex_t> {
119
+ __device__ __forceinline__ float4 operator()(const float4 &ab0, const float4 &ab1) const {
120
+ complex_t a0 = complex_t(ab0.x, ab0.y);
121
+ complex_t b0 = complex_t(ab0.z, ab0.w);
122
+ complex_t a1 = complex_t(ab1.x, ab1.y);
123
+ complex_t b1 = complex_t(ab1.z, ab1.w);
124
+ complex_t out_a = a1 * a0;
125
+ complex_t out_b = a1 * b0 + b1;
126
+ return make_float4(out_a.real_, out_a.imag_, out_b.real_, out_b.imag_);
127
+ }
128
+ };
129
+
130
+ // A stateful callback functor that maintains a running prefix to be applied
131
+ // during consecutive scan operations.
132
+ template <typename scalar_t> struct SSMScanPrefixCallbackOp {
133
+ using scan_t = std::conditional_t<std::is_same_v<scalar_t, float>, float2, float4>;
134
+ scan_t running_prefix;
135
+ // Constructor
136
+ __device__ SSMScanPrefixCallbackOp(scan_t running_prefix_) : running_prefix(running_prefix_) {}
137
+ // Callback operator to be entered by the first warp of threads in the block.
138
+ // Thread-0 is responsible for returning a value for seeding the block-wide scan.
139
+ __device__ scan_t operator()(scan_t block_aggregate) {
140
+ scan_t old_prefix = running_prefix;
141
+ running_prefix = SSMScanOp<scalar_t>()(running_prefix, block_aggregate);
142
+ return old_prefix;
143
+ }
144
+ };
145
+
146
+ ////////////////////////////////////////////////////////////////////////////////////////////////////
147
+
148
+ template<typename Ktraits>
149
+ inline __device__ void load_input(typename Ktraits::input_t *u,
150
+ typename Ktraits::input_t (&u_vals)[Ktraits::kNItems],
151
+ typename Ktraits::BlockLoadT::TempStorage &smem_load,
152
+ int seqlen) {
153
+ if constexpr (Ktraits::kIsEvenLen) {
154
+ auto& smem_load_vec = reinterpret_cast<typename Ktraits::BlockLoadVecT::TempStorage&>(smem_load);
155
+ using vec_t = typename Ktraits::vec_t;
156
+ Ktraits::BlockLoadVecT(smem_load_vec).Load(
157
+ reinterpret_cast<vec_t*>(u),
158
+ reinterpret_cast<vec_t(&)[Ktraits::kNLoads]>(u_vals)
159
+ );
160
+ } else {
161
+ Ktraits::BlockLoadT(smem_load).Load(u, u_vals, seqlen, 0.f);
162
+ }
163
+ }
164
+
165
+ template<typename Ktraits>
166
+ inline __device__ void load_weight(typename Ktraits::input_t *Bvar,
167
+ typename Ktraits::weight_t (&B_vals)[Ktraits::kNItems],
168
+ typename Ktraits::BlockLoadWeightT::TempStorage &smem_load_weight,
169
+ int seqlen) {
170
+ constexpr int kNItems = Ktraits::kNItems;
171
+ if constexpr (!Ktraits::kIsComplex) {
172
+ typename Ktraits::input_t B_vals_load[kNItems];
173
+ if constexpr (Ktraits::kIsEvenLen) {
174
+ auto& smem_load_weight_vec = reinterpret_cast<typename Ktraits::BlockLoadWeightVecT::TempStorage&>(smem_load_weight);
175
+ using vec_t = typename Ktraits::vec_t;
176
+ Ktraits::BlockLoadWeightVecT(smem_load_weight_vec).Load(
177
+ reinterpret_cast<vec_t*>(Bvar),
178
+ reinterpret_cast<vec_t(&)[Ktraits::kNLoads]>(B_vals_load)
179
+ );
180
+ } else {
181
+ Ktraits::BlockLoadWeightT(smem_load_weight).Load(Bvar, B_vals_load, seqlen, 0.f);
182
+ }
183
+ // #pragma unroll
184
+ // for (int i = 0; i < kNItems; ++i) { B_vals[i] = B_vals_load[i]; }
185
+ Converter<typename Ktraits::input_t, kNItems>::to_float(B_vals_load, B_vals);
186
+ } else {
187
+ typename Ktraits::input_t B_vals_load[kNItems * 2];
188
+ if constexpr (Ktraits::kIsEvenLen) {
189
+ auto& smem_load_weight_vec = reinterpret_cast<typename Ktraits::BlockLoadWeightVecT::TempStorage&>(smem_load_weight);
190
+ using vec_t = typename Ktraits::vec_t;
191
+ Ktraits::BlockLoadWeightVecT(smem_load_weight_vec).Load(
192
+ reinterpret_cast<vec_t*>(Bvar),
193
+ reinterpret_cast<vec_t(&)[Ktraits::kNLoads * 2]>(B_vals_load)
194
+ );
195
+ } else {
196
+ Ktraits::BlockLoadWeightT(smem_load_weight).Load(Bvar, B_vals_load, seqlen, 0.f);
197
+ }
198
+ #pragma unroll
199
+ for (int i = 0; i < kNItems; ++i) { B_vals[i] = complex_t(B_vals_load[i * 2], B_vals_load[i * 2 + 1]); }
200
+ }
201
+ }
202
+
203
+ template<typename Ktraits>
204
+ inline __device__ void store_output(typename Ktraits::input_t *out,
205
+ const float (&out_vals)[Ktraits::kNItems],
206
+ typename Ktraits::BlockStoreT::TempStorage &smem_store,
207
+ int seqlen) {
208
+ typename Ktraits::input_t write_vals[Ktraits::kNItems];
209
+ #pragma unroll
210
+ for (int i = 0; i < Ktraits::kNItems; ++i) { write_vals[i] = out_vals[i]; }
211
+ if constexpr (Ktraits::kIsEvenLen) {
212
+ auto& smem_store_vec = reinterpret_cast<typename Ktraits::BlockStoreVecT::TempStorage&>(smem_store);
213
+ using vec_t = typename Ktraits::vec_t;
214
+ Ktraits::BlockStoreVecT(smem_store_vec).Store(
215
+ reinterpret_cast<vec_t*>(out),
216
+ reinterpret_cast<vec_t(&)[Ktraits::kNLoads]>(write_vals)
217
+ );
218
+ } else {
219
+ Ktraits::BlockStoreT(smem_store).Store(out, write_vals, seqlen);
220
+ }
221
+ }
protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_fwd_bf16.cu ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ // Split into multiple files to compile in paralell
6
+
7
+ #include "selective_scan_fwd_kernel.cuh"
8
+
9
+ template void selective_scan_fwd_cuda<at::BFloat16, float>(SSMParamsBase &params, cudaStream_t stream);
10
+ template void selective_scan_fwd_cuda<at::BFloat16, complex_t>(SSMParamsBase &params, cudaStream_t stream);
protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_fwd_fp16.cu ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ // Split into multiple files to compile in paralell
6
+
7
+ #include "selective_scan_fwd_kernel.cuh"
8
+
9
+ template void selective_scan_fwd_cuda<at::Half, float>(SSMParamsBase &params, cudaStream_t stream);
10
+ template void selective_scan_fwd_cuda<at::Half, complex_t>(SSMParamsBase &params, cudaStream_t stream);
protein_lm/modeling/models/libs/mamba/csrc/selective_scan/selective_scan_fwd_fp32.cu ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ /******************************************************************************
2
+ * Copyright (c) 2023, Tri Dao.
3
+ ******************************************************************************/
4
+
5
+ // Split into multiple files to compile in paralell
6
+
7
+ #include "selective_scan_fwd_kernel.cuh"
8
+
9
+ template void selective_scan_fwd_cuda<float, float>(SSMParamsBase &params, cudaStream_t stream);
10
+ template void selective_scan_fwd_cuda<float, complex_t>(SSMParamsBase &params, cudaStream_t stream);