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ContextualAI / gritlm

Generative Representational Instruction Tuning
https://arxiv.org/abs/2402.09906
MIT License
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embedding embedding-models embeddings grit information-retrieval instruction-tuning llm llms mteb retrieval sbert sgpt text-embedding

readme

Generative Representational Instruction Tuning

This repository provides all materials for the paper Generative Representational Instruction Tuning. We continue developing the repository and welcome any contributions. If you want to use the code in the exact same way as in the paper, please use the 1.0.0 release (commit hash = 3ac39052ef878371a658a060e69f9c0124bfd59b). 63min video & 30min video on the paper by Niklas Muennighoff; 60min slides link.

Inference

Basic

pip install gritlm

from gritlm import GritLM

# Loads the model for both capabilities; If you only need embedding pass `mode="embedding"` to save memory (no lm head)
model = GritLM("GritLM/GritLM-7B", torch_dtype="auto")
# To load the 8x7B you will likely need multiple GPUs.
# All the kwargs are passed to HF from_pretrained so you can just do the below to load on multiple GPUs:
# model = GritLM("GritLM/GritLM-8x7B", torch_dtype="auto", device_map="auto")
# You can also load other models e.g.
# model = GritLM("Muennighoff/SGPT-125M-weightedmean-nli-bitfit", pooling_method="weighted_mean", attn=None)
# model = GritLM("hkunlp/instructor-base", pooling_method="mean", attn=None)

### Embedding/Representation ###
instruction = "Given a scientific paper title, retrieve the paper's abstract"
queries = ['Bitcoin: A Peer-to-Peer Electronic Cash System', 'Generative Representational Instruction Tuning']
documents = [
    "A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone.",
    "All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning (GRIT) whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions. Compared to other open models, our resulting GritLM 7B sets a new state of the art on the Massive Text Embedding Benchmark (MTEB) and outperforms all models up to its size on a range of generative tasks. By scaling up further, GritLM 8X7B outperforms all open generative language models that we tried while still being among the best embedding models. Notably, we find that GRIT matches training on only generative or embedding data, thus we can unify both at no performance loss. Among other benefits, the unification via GRIT speeds up Retrieval-Augmented Generation (RAG) by > 60% for long documents, by no longer requiring separate retrieval and generation models. Models, code, etc. are freely available at https://github.com/ContextualAI/gritlm."
]

def gritlm_instruction(instruction):
    return "<|user|>\n" + instruction + "\n<|embed|>\n" if instruction else "<|embed|>\n"

# No need to add instruction for retrieval documents
d_rep = model.encode(documents, instruction=gritlm_instruction(""))
q_rep = model.encode(queries, instruction=gritlm_instruction(instruction))

from scipy.spatial.distance import cosine
cosine_sim_q0_d0 = 1 - cosine(q_rep[0], d_rep[0])
cosine_sim_q0_d1 = 1 - cosine(q_rep[0], d_rep[1])
cosine_sim_q1_d0 = 1 - cosine(q_rep[1], d_rep[0])
cosine_sim_q1_d1 = 1 - cosine(q_rep[1], d_rep[1])

print("Cosine similarity between \"%s\" and \"%s\" is: %.3f" % (queries[0][:15], documents[0][:15], cosine_sim_q0_d0))
# Cosine similarity between "Bitcoin: A Peer" and "A purely peer-t" is: 0.608
print("Cosine similarity between \"%s\" and \"%s\" is: %.3f" % (queries[0][:15], documents[1][:15], cosine_sim_q0_d1))
# Cosine similarity between "Bitcoin: A Peer" and "All text-based " is: 0.101
print("Cosine similarity between \"%s\" and \"%s\" is: %.3f" % (queries[1][:15], documents[0][:15], cosine_sim_q1_d0))
# Cosine similarity between "Generative Repr" and "A purely peer-t" is: 0.120
print("Cosine similarity between \"%s\" and \"%s\" is: %.3f" % (queries[1][:15], documents[1][:15], cosine_sim_q1_d1))
# Cosine similarity between "Generative Repr" and "All text-based " is: 0.533

### Generation ###
# We did not finetune GritLM models with system prompts, as you can just include system-like instructions together with your user instruction
messages = [
    {"role": "user", "content": "Please write me a poem about my recent hike of Mt. Fuji at midnight in the style of Shakespeare."},
]
encoded = model.tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
encoded = encoded.to(model.device)
gen = model.generate(encoded, max_new_tokens=256, do_sample=False)
decoded = model.tokenizer.batch_decode(gen)
print(decoded[0])
"""
<s> <|user|>
Please write me a poem about my recent hike of Mt. Fuji at midnight in the style of Shakespeare.
<|assistant|>
Oh, Mt. Fuji, mountain grand,
A sight to see, a climb to command,
At midnight, in the dark of night,
I climbed your slopes, with all my might.

The stars above, they shone so bright,
A beacon in the darkness, guiding light,
The wind did blow, with a gentle sigh,
As I climbed higher, with a steady eye.

The path was steep, the climb was tough,
But I pressed on, with a steadfast rough,
For the summit, I longed to see,
The view from the top, a sight to be.

At last, I reached the peak, and stood,
With awe and wonder, I gazed aloud,
The world below, a sight to see,
A view that's worth the climb, you'll agree.

Mt. Fuji, mountain grand,
A sight to see, a climb to command,
At midnight, in the dark of night,
I climbed your slopes, with all my might.</s>
"""

Caching

pip install gritlm

import numpy as np
import torch
from gritlm import GritLM

# Loads the model for both capabilities; If you only need embedding pass `mode="embedding"` to save memory (no lm head)
model = GritLM("GritLM/GritLM-7B", torch_dtype="auto")
# To load the 8x7B you will likely need multiple GPUs.
# All the kwargs are passed to HF from_pretrained so you can just do the below to load on multiple GPUs:
# model = GritLM("GritLM/GritLM-8x7B", torch_dtype="auto", device_map="auto")
# You can also load other models e.g.
# model = GritLM("Muennighoff/SGPT-125M-weightedmean-nli-bitfit", pooling_method="weighted_mean", attn=None)
# model = GritLM("hkunlp/instructor-base", pooling_method="mean", attn=None)

queries = ['Please explain to me how Bitcoin works.', 'What is "Generative Representational Instruction Tuning"?']
documents = [
    "A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone.",
    "All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning (GRIT) whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions. Compared to other open models, our resulting GritLM 7B sets a new state of the art on the Massive Text Embedding Benchmark (MTEB) and outperforms all models up to its size on a range of generative tasks. By scaling up further, GritLM 8X7B outperforms all open generative language models that we tried while still being among the best embedding models. Notably, we find that GRIT matches training on only generative or embedding data, thus we can unify both at no performance loss. Among other benefits, the unification via GRIT speeds up Retrieval-Augmented Generation (RAG) by > 60% for long documents, by no longer requiring separate retrieval and generation models. Models, code, etc. are freely available at https://github.com/ContextualAI/gritlm."
]

CACHE_FORMAT_DOC = "\n<|user|>\n{query}\n\nAnswer the prior query while optionally using the context prior to it\n<|assistant|>\n"
CACHE_FORMAT_QUERY = "\n<|user|>\n{doc}\n\nOptionally using the prior context answer the query prior to it\n<|assistant|>\n"
CACHE_FORMAT_QUERY_DOC = "\n<|user|>\nOptionally using the prior context answer the query prior to it\n<|assistant|>\n"
CACHE_FORMAT_DOC_QUERY = "\n<|user|>\nAnswer the prior query while optionally using the context prior to it\n<|assistant|>\n"

def gritlm_instruction(instruction):
    return "<|user|>\n" + instruction + "\n<|embed|>\n" if instruction else "<|embed|>\n"

### GRIT DOC CACHING ###
# cache: Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`
d_rep, d_cache = model.encode(documents, instruction=gritlm_instruction(""), get_cache=True)
q_rep = model.encode(queries, instruction=gritlm_instruction(""))

from scipy.spatial.distance import cosine
sims = {q: [1 - cosine(q_rep[i], d_rep[j]) for j in range(len(d_rep))] for i, q in enumerate(queries)}

for q, q_sims in sims.items():
    sim_idx = np.argmax(q_sims)
    cache = tuple([
        (d_cache[i][0][sim_idx:sim_idx+1], d_cache[i][1][sim_idx:sim_idx+1]) for i, c in enumerate(d_cache)
    ])
    # BOS is already in the cache
    inputs = model.tokenizer(CACHE_FORMAT_DOC.format(query=q), return_tensors="pt", add_special_tokens=False).to(model.device)
    inputs["use_cache"] = True
    # Attend to the cache too
    inputs["attention_mask"] = torch.cat((
        torch.ones((cache[0][0].shape[0], cache[0][0].shape[2]), dtype=torch.long, device=inputs["attention_mask"].device),
        inputs["attention_mask"],
    ), dim=1)
    generation = model.generate(**inputs, max_new_tokens=256, past_key_values=cache, do_sample=False)
    decoded = model.tokenizer.batch_decode(generation)
    print(decoded[0])

"""
<|user|>
What is "Generative Representational Instruction Tuning"?

Answer the prior query while optionally using the context prior to it
<|assistant|>
Generative Representational Instruction Tuning (GRIT) is a method for training language models that can perform both generative and embedding tasks. It involves training a large language model to handle both types of tasks by distinguishing between them through instructions. GRIT is designed to improve the performance of language models on both generative and embedding tasks, and it can be used to unify both types of tasks at no performance loss.</s>
"""

### GRIT QUERY CACHING ###
# cache: Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`
d_rep = model.encode(documents, instruction=gritlm_instruction(""))
q_rep, q_cache = model.encode(queries, instruction=gritlm_instruction(""), get_cache=True)

from scipy.spatial.distance import cosine
sims = {d: [1 - cosine(q_rep[i], d_rep[j]) for j in range(len(d_rep))] for i, d in enumerate(documents)}

for d, d_sims in sims.items():
    sim_idx = np.argmax(d_sims)
    cache = tuple([
        (q_cache[i][0][sim_idx:sim_idx+1], q_cache[i][1][sim_idx:sim_idx+1]) for i, c in enumerate(q_cache)
    ])
    # BOS is already in the cache
    inputs = model.tokenizer(CACHE_FORMAT_QUERY.format(doc=d), return_tensors="pt", add_special_tokens=False).to(model.device)
    inputs["use_cache"] = True
    # Attend to the cache too
    inputs["attention_mask"] = torch.cat((
        torch.ones((cache[0][0].shape[0], cache[0][0].shape[2]), dtype=torch.long, device=inputs["attention_mask"].device),
        inputs["attention_mask"],
    ), dim=1)
    generation = model.generate(**inputs, max_new_tokens=256, past_key_values=cache, do_sample=False)
    decoded = model.tokenizer.batch_decode(generation)
    print(decoded[0])

"""
<|user|>
All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning (GRIT) whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions. Compared to other open models, our resulting GritLM 7B sets a new state of the art on the Massive Text Embedding Benchmark (MTEB) and outperforms all models up to its size on a range of generative tasks. By scaling up further, GritLM 8X7B outperforms all open generative language models that we tried while still being among the best embedding models. Notably, we find that GRIT matches training on only generative or embedding data, thus we can unify both at no performance loss. Among other benefits, the unification via GRIT speeds up Retrieval-Augmented Generation (RAG) by > 60% for long documents, by no longer requiring separate retrieval and generation models. Models, code, etc. are freely available at https://github.com/ContextualAI/gritlm.

Optionally using the prior context answer the query prior to it
<|assistant|>
GRIT stands for generative representational instruction tuning. It is a method for training large language models to handle both generative and embedding tasks by distinguishing between them through instructions. GritLM is a large language model trained using GRIT that sets a new state of the art on the Massive Text Embedding Benchmark (MTEB) and outperforms all models up to its size on a range of generative tasks. GritLM 8X7B is a larger version of GritLM that outperforms all open generative language models that were tried while still being among the best embedding models. GRIT matches training on only generative or embedding data, thus unifying both at no performance loss. This unification via GRIT speeds up Retrieval-Augmented Generation (RAG) by > 60% for long documents, by no longer requiring separate retrieval and generation models. Models, code, etc. are freely available at <https://github.com/ContextualAI/gritlm>.</s>
"""

### GRIT QUERY-DOC CACHING ###
# cache: Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`
d_rep, d_cache = model.encode(documents, instruction=gritlm_instruction(""), get_cache=True, add_special_tokens=False)
q_rep, q_cache = model.encode(queries, instruction=gritlm_instruction(""), get_cache=True)

from scipy.spatial.distance import cosine
sims = {q: [1 - cosine(q_rep[i], d_rep[j]) for j in range(len(d_rep))] for i, q in enumerate(queries)}

for i, (q, q_sims) in enumerate(sims.items()):
    sim_idx = np.argmax(q_sims)
    cache_query = tuple([
        (q_cache[j][0][i:i+1], q_cache[j][1][i:i+1]) for j, c in enumerate(q_cache)
    ])
    cache_doc = tuple([
        (d_cache[j][0][sim_idx:sim_idx+1], d_cache[j][1][sim_idx:sim_idx+1]) for j, c in enumerate(d_cache)
    ])
    # For DOC-QUERY simply swap the order of the cache, change the format to CACHE_FORMAT_DOC_QUERY & set add_special_tokens=True in the `model.encode(..` above
    cache = [(
        torch.cat((layer[0], cache_doc[i][0]), dim=2),
        torch.cat((layer[1], cache_doc[i][1]), dim=2),
    ) for i, layer in enumerate(cache_query)]
    # BOS is already in the cache
    inputs = model.tokenizer(CACHE_FORMAT_QUERY_DOC, return_tensors="pt", add_special_tokens=False).to(model.device)
    inputs["use_cache"] = True
    # Attend to the cache too
    inputs["attention_mask"] = torch.cat((
        torch.ones((cache[0][0].shape[0], cache[0][0].shape[2]), dtype=torch.long, device=inputs["attention_mask"].device),
        inputs["attention_mask"],
    ), dim=1)
    generation = model.generate(**inputs, max_new_tokens=256, past_key_values=cache, do_sample=False)
    decoded = model.tokenizer.batch_decode(generation)
    print(decoded[0])

"""
<|user|>
Optionally using the prior context answer the query prior to it
<|assistant|>
Sure, here's an example of how the prior context could be used to answer a query:

Query: "What is GRIT?"

Prior context: "We introduce generative representation instruction tuning (GRIT) whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions."

Answer: GRIT is a method for training language models to handle both generative and embedding tasks by distinguishing between them through instructions.</s>
"""

Models

The weights and logs of all models from the paper are freely available:

The names will not always match across HF & WandB, but you can usually figure out which belongs to which via the --output_dir in the command. Note that we renamed all models from sgpt2 to gritlm at some point, so some names/logs/commands contain the old name.

Shortcuts:

The most important ones are:

Model Description Emb performance (MTEB) Gen performance
GritLM-7B 7B parameter model that uses bidirectional attention for embedding and causal attention for generation. It is finetuned from Mistral-7B 66.8 55.5
GritLM-8x7B 8x7B parameter model that uses bidirectional attention for embedding and causal attention for generation. It is finetuned from Mistral-8x7B 65.7 65.7
Generative-only variant 7B parameter model generative-only equivalent of GritLM-7B. 41.2 55.2
Embedding-only variant 7B parameter model embedding-only equivalent of GritLM-7B. 66.8 7.6

For GritLM-7B and GritLM-8x7B, the folder contains a custom modeling file (modeling_gritlm*.py) which adds bidirectional attention via the keyword argument is_causal, such that if you load them with from_pretrained in transformers, it is automatically available. We did not add this for any other models uploaded to the organization, thus for those, you need to either add it yourself or simply replace the modeling_mistral.py & modeling_mixtral.py files in your transformers installation with scripts/modeling_mistral_gritlm.py & scripts/modeling_mixtral_gritlm.py. Note that for models that do not use bidirectional attention or when you do not intend to use the bidirectional attention (e.g. for generation), you don't need to do anything.

Training

Data

The repo uses the below format. See training/toy_data.jsonl for an example.

Format:

We release the below datasets:

They are explained in more detail in the paper and its appendix. So to e.g. train a GRIT model on MEDI2 & Tulu2, simply download both via git clone https... and then place them in the same directory and follow the instructions below to run. Unfortunately, we cannot release the E5S data used for our final models.

Run

Setup:

# First install PyTorch (https://pytorch.org/get-started/locally/; we used torch==2.2.0 with NVIDIA-SMI 535.104.05, Driver Version: 535.104.05, CUDA Version: 12.2), then do the below
git clone https://github.com/ContextualAI/gritlm
cd gritlm
pip install -e .
# If you want to use GradCache, you need to use the one in this repository
cd gritlm/training/GradCache
pip install -e .
cd ../..

Below are easy examples for getting started:

Embedding model

torchrun --nproc_per_node 1 \
-m training.run \
--output_dir test_path \
--model_name_or_path openaccess-ai-collective/tiny-mistral \
--train_data training/toy_data/toy_data_embedding.jsonl \
--learning_rate 1e-5 \
--num_train_epochs 5 \
--per_device_train_batch_size 2 \
--dataloader_drop_last True \
--normalized True \
--temperature 0.02 \
--query_max_len 32 \
--passage_max_len 128 \
--train_group_size 2 \
--mode embedding \
--attn cccc

Generative model

torchrun --nproc_per_node 1 \
-m training.run \
--output_dir test_path \
--model_name_or_path openaccess-ai-collective/tiny-mistral \
--train_data training/toy_data/toy_data_generative.jsonl \
--learning_rate 1e-5 \
--num_train_epochs 5 \
--per_device_train_batch_size 2 \
--dataloader_drop_last True \
--passage_max_len 128 \
--mode generative \
--attn cccc

Unified model (GRIT)

torchrun --nproc_per_node 1 \
-m training.run \
--output_dir test_path \
--model_name_or_path openaccess-ai-collective/tiny-mistral \
--train_data training/toy_data \
--learning_rate 1e-5 \
--num_train_epochs 5 \
--per_device_train_batch_size 2 \
--dataloader_drop_last True \
--normalized True \
--temperature 0.02 \
--query_max_len 32 \
--passage_max_len 128 \
--train_group_size 2 \
--mode unified \
--attn cccc

All arguments are explained in training/arguments.py or the HF TrainingArguments documentation except for nproc_per_node which is the number of GPUs per node. For our actual training runs, we use accelerate to easily use multiple nodes and GPUs as well as slightly different settings (e.g. --attn bbcc). The scripts are all in scripts/training, for example scripts/training/train_gritlm_8x7b.sh was used for GritLM-8x7B. For models from the ablations, you can check their folder on the huggingface hub which contains a training_args.bin file with the arguments. You can also check all their arguments on the WandB: https://wandb.ai/muennighoff/gritlm. After training, you may first have to run python scripts/reformat_statedict.py path_to_statedict to remove the model. prefix from the checkpoint, and then you can shard the checkpoint via python scripts/shard.py path_to_model_folder for easier usage.

Alignment

For the experiments on aligning GritLM with KTO we use https://github.com/huggingface/trl with the scripts in https://github.com/Muennighoff/kto.

Evaluation

Embedding

cd gritlm
python evaluation/eval_mteb.py \
--model_name_or_path GritLM/GritLM-7B \
--task_types Classification,Clustering,PairClassification,Reranking,Retrieval,STS,Summarization \
--batch_size 32

For a faster way, check scripts/eval_mteb.sh which submits jobs across multiple GPUs for each dataset.

Generative

## Setup
# Setup eval for MMLU/GSM8K/BBH/TyDi QA/Alpaca
git clone https://github.com/Muennighoff/open-instruct.git
cd open-instruct
pip install -r requirements.txt
bash ./scripts/prepare_eval_data.sh
cd ..
# Setup eval for HumanEvalPack
git clone https://github.com/bigcode-project/bigcode-evaluation-harness
cd bigcode-evaluation-harness
pip install -e .
cd ..
MODEL_PATH=GritLM/gritlm-7b
# Run all evals except for Alpaca; You may have to change some paths etc.
bash scripts/generative_eval.sh {path to model}
# Run Alpaca 1.0
export OPENAI_API_KEY=YOUR_API_KEY
python -m eval.alpaca_farm.run_eval \
--use_vllm \
--model_name_or_path $MODEL_PATH \
--tokenizer_name_or_path $MODEL_PATH \
--save_dir ./ \
--use_chat_format \
--chat_formatting_function eval.templates.create_prompt_with_gritlm_chat_format
# Alpaca 2.0 (not used in the paper)
python -m eval.alpaca_farm.run_eval \
--use_vllm \
--model_name_or_path $MODEL_PATH \
--tokenizer_name_or_path $MODEL_PATH \
--save_dir $MODEL_PATH \
--use_chat_format \
--chat_formatting_function eval.templates.create_prompt_with_gritlm_chat_format \
--alpaca2

Known issues

Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information

You can suppress this exception and fall back to eager by setting:
import torch._dynamo
torch._dynamo.config.suppress_errors = True 

example_value = wrap_to_fake_tensor_and_record(

File "/env/lib/conda/gritlmnew/lib/python3.9/site-packages/torch/_dynamo/variables/builder.p y", line 1587, in wrap_to_fake_tensor_and_record
fake_e = wrap_fake_exception(
File "/env/lib/conda/gritlmnew/lib/python3.9/site-packages/torch/_dynamo/utils.py", line 916 , in wrap_fake_exception
return fn()
File "/env/lib/conda/gritlmnew/lib/python3.9/site-packages/torch/_dynamo/variables/builder.p y", line 1588, in
lambda: tx.fake_mode.from_tensor(
File "/env/lib/conda/gritlmnew/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py" , line 1721, in from_tensor
return self.fake_tensor_converter(
File "/env/lib/conda/gritlmnew/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py" , line 371, in call
return self.from_real_tensor(
File "/env/lib/conda/gritlmnew/lib/python3.9/site-packages/torch/_subclasses/fake_tensor.py" , line 324, in from_real_tensor
out = self.meta_converter(
File "/env/lib/conda/gritlmnew/lib/python3.9/site-packages/torch/_subclasses/meta_utils.py", line 591, in call
r = self.meta_tensor(
File "/env/lib/conda/gritlmnew/lib/python3.9/site-packages/torch/_subclasses/meta_utils.py", line 307, in meta_tensor
base = self.meta_tensor(
File "/env/lib/conda/gritlmnew/lib/python3.9/site-packages/torch/_subclasses/meta_utils.py", line 478, in meta_tensor
r.grad = self.meta_tensor(
torch._dynamo.exc.InternalTorchDynamoError: attempting to assign a gradient of size '[2726400 0]' to a tensor of size '[218112000]'. Please ensure that the gradient and the tensor are the same size

- DeepSpeed + FlashAttention2 + Optim & Params offloaded to CPU + DeepSpeed ZeRo3 init fails:
```bash
s. (Triggered internally at /opt/conda/conda-bld/pytorch_1702400412039/work/torch/csrc/t
ensor/python_tensor.cpp:83.)                                                            
  total_norm_cuda = get_accelerator().FloatTensor([float(total_norm)])                  
Invalidate trace cache @ step 1: expected module 1, but got module 2                    
[E ProcessGroupNCCL.cpp:475] [Rank 1] Watchdog caught collective operation timeout: Work

Visuals

Acknowledgements

The code is inspired by:

Please see additional acknowledgments in the paper.

Citation

If useful please consider citing 😊

@misc{muennighoff2024generative,
      title={Generative Representational Instruction Tuning}, 
      author={Niklas Muennighoff and Hongjin Su and Liang Wang and Nan Yang and Furu Wei and Tao Yu and Amanpreet Singh and Douwe Kiela},
      year={2024},
      eprint={2402.09906},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}