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Final multimodal PR with our recent developments on MM side (NVIDIA#8127
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* Hotfix (#7501) (#7568)

Signed-off-by: Jan Baczek <jbaczek@nvidia.com>
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* Avoid duplicated checkpoint save (#7555) (#7566)

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* Cache FP8 weight and transpose only at the first micro-batch in each validation and test routine (#7470) (#7483)

* Cache weight and transpose only in the first batch in all training, val, and test runs

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* Add an option to disable manual GC in validation (#7467) (#7476)

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* Remove PUBLICATIONS.md, point to github.io NeMo page instead (#7694) (#7695)

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* Fix multi rank finetune for ASR (#7684) (#7699)

* Fix multi rank finetune for ASR

* Actually add time

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* Update docs: readme, getting started, ASR intro (#7679)

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* [TTS] Add dataset to path of logged artifacts

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* [TTS] Revert axis name back to Audio Frames

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* move install info to INSTALLATION.md

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* tidy up links

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* Fix sft dataset truncation (#7464)

* Add fix

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* Automatic Lip Reading Recognition (ALR) - ASR/CV (Visual ASR) (#7330)

* striding_conv1d_k5 and dw_striding_conv1d_k5 subsampling

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* HF StarCoder to NeMo conversion script (#7421)

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* fix bug when loading dist ckpt in peft (#7452)

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* Fix adding positional embeddings in-place in transformer module (#7440)

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* Fix (#7478)

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* add sleep (#7498) (#7499)

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* Fix exp manager check for sleep (#7503) (#7504)

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* bugfix: trainer.accelerator=auto from None. (#7492) (#7493)

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* [TTS] Read audio as int32 to avoid flac read errors (#7477)

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* Add dataset 'AISHELL-3' from OpenSLR for training mandarin TTS (#7409)

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* Fix TTS FastPitch tutorial (#7494) (#7516)

* Fix

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* Fix get_dist() tensor dimension (#7506) (#7515)

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* bugfix: specify trainer.strategy=auto when devices=1 (#7509) (#7512)

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* fix (#7511)

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* Add the `strategy` argument to `MegatronGPTModel.generate()` (#7264)

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* Update subsampling.py

change striding_conv1d_k5 to striding_conv1d

Signed-off-by: Maxime Burchi <60737204+burchim@users.noreply.github.com>

* cv branch

Signed-off-by: mburchi <maxime.burchi@gmail.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* video manifest

Signed-off-by: mburchi <maxime.burchi@gmail.com>

* add collection classes

Signed-off-by: mburchi <maxime.burchi@gmail.com>

* [pre-commit.ci] auto fixes from pre-…

* fix onnx (#7703) (#7704)

Signed-off-by: fayejf <fayejf07@gmail.com>
Co-authored-by: fayejf <36722593+fayejf@users.noreply.github.com>

* move core install to /workspace (#7706)

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* Fix typo in audio codec config, encoder target (#7697)

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* Replace strategy='dp'/None with 'auto' (#7681) (#7696)

* Add strategy=auto for None and dp
* Change strategy from None to auto
---------

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* [ASR] Multichannel mask estimator with flex number of channels (#7317)

* Adding a mask estimator which can …
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4 changes: 2 additions & 2 deletions docs/source/multimodal/api.rst
Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@ Model Classes
:members: __init__, configure_optimizers


.. autoclass:: nemo.collections.multimodal.models.stable_diffusion.ldm.ddpm.MegatronLatentDiffusion
.. autoclass:: nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm.MegatronLatentDiffusion
:show-inheritance:
:no-members:
:members: __init__, training_step, validation_step, setup, build_train_valid_test_datasets
Expand Down Expand Up @@ -49,7 +49,7 @@ Modules
:show-inheritance:
:no-members:

.. autoclass:: nemo.collections.multimodal.models.stable_diffusion.ldm.autoencoder.AutoencoderKL
.. autoclass:: nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.autoencoder.AutoencoderKL
:show-inheritance:
:no-members:
:members: __init__, encode, decode
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2 changes: 1 addition & 1 deletion docs/source/multimodal/mllm/checkpoint.rst
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Expand Up @@ -108,7 +108,7 @@ Adjust model parallelism with:
--target_tensor_model_parallel_size=??? \
--pipeline_model_parallel_size=??? \
--target_pipeline_model_parallel_size=??? \
--model_class="nemo.collections.multimodal.models.neva.neva_model.MegatronNevaModel" \
--model_class="nemo.collections.multimodal.models.multimodal_llm.neva.neva_model.MegatronNevaModel" \
--precision=32 \
--tokenizer_model_path=/path/to/tokenizer.model \
--tp_conversion_only
2 changes: 1 addition & 1 deletion docs/source/multimodal/text2img/insp2p.rst
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Expand Up @@ -6,7 +6,7 @@ Model Introduction

InstructPix2Pix [InstructPix2Pix]_ :cite:`mm-models-insp2p` offers a unique approach to image editing using human-written instructions. Given an input image and a textual directive, the model adjusts the image according to the provided instructions. NeMo Multimodal presents a training pipeline for this conditional diffusion model, utilizing a dataset generated by harnessing the strengths of two prominent pretrained models: a language model (GPT-3) and a text-to-image model (Stable Diffusion). The InstructPix2Pix model operates swiftly, editing images within seconds, eliminating the need for per-example fine-tuning or inversion. It has demonstrated remarkable results across a wide variety of input images and written instructions.

Built upon the Stable Diffusion framework, NeMo's InstructPix2Pix shares a similar architecture with Stable Diffusion (refer to :doc:`Stable Diffusion <./sd>`). What sets it apart is its unique training dataset and the combined guidance from both image and text prompts. Specifically, InstructPix2pix ::class::``nemo.collections.multimodal.models.instruct_pix2pix.ldm.ddpm_edit.MegatronLatentDiffusionEdit`` is derived directly from Stable Diffusion's ::class::``nemo.collections.multimodal.models.stable_diffusion.ldm.ddpm.MegatronLatentDiffusion``, with alterations to accommodate the dataset and provide support for dual guidance.
Built upon the Stable Diffusion framework, NeMo's InstructPix2Pix shares a similar architecture with Stable Diffusion (refer to :doc:`Stable Diffusion <./sd>`). What sets it apart is its unique training dataset and the combined guidance from both image and text prompts. Specifically, InstructPix2pix ::class::``nemo.collections.multimodal.models.instruct_pix2pix.ldm.ddpm_edit.MegatronLatentDiffusionEdit`` is derived directly from Stable Diffusion's ::class::``nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm.MegatronLatentDiffusion``, with alterations to accommodate the dataset and provide support for dual guidance.

Training Dataset
--------------------
Expand Down
2 changes: 1 addition & 1 deletion docs/source/multimodal/text2img/sd.rst
Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@ The VAE configuration is defined under **first_stage_config**.
.. code-block:: yaml
first_stage_config:
_target_: nemo.collections.multimodal.models.stable_diffusion.ldm.autoencoder.AutoencoderKL
_target_: nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.autoencoder.AutoencoderKL
from_pretrained: /path/to/vae.bin
embed_dim: 4
monitor: val/rec_loss
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Expand Up @@ -11,6 +11,7 @@ inference:
compute_logprob: False # a flag used to compute logprob of all the input text, a very special case of running inference, default False
end_strings: ["<extra_id_1>","<extra_id_7>",] # generation will stop when one of these tokens is generated
images_base_path: /pwd/images
insert_image_token: null # `left` or `right` or `null`

trainer:
devices: 8
Expand All @@ -24,7 +25,7 @@ tensor_model_parallel_size: 8
pipeline_model_parallel_size: 1
pipeline_model_parallel_split_rank: 0 # used for encoder and decoder model (0 for others)
neva_model_file: /pwd/nemo_experiments/nemo_llava.nemo #neva_22b_tp8_finetuned_v1.nemo neva_8b_tp4_finetuned_v1.nemo
llm_model_file: null
base_model_file: null
checkpoint_dir: null #/pwd/nemo_multimodal/nemo_experiments/nemo_llava_finetune/checkpoints # checkpoint file dir. This is used to load the PTL checkpoint generated during the Kosmos training
checkpoint_name: null #megatron_clip--val_loss=0.41-step=13499-consumed_samples=431904.0.ckpt # PTL checkpoint file name, only used for PTL checkpoint loading
hparams_file: null #/pwd/nemo_multimodal/nemo_experiments/nemo_llava_finetune/version_0/hparams.yaml # model configuration file, only used for PTL checkpoint loading
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -209,7 +209,7 @@ model:

optim:
name: fused_adam
lr: 2e-5
lr: 2e-4
weight_decay: 0.
betas:
- 0.9
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,8 @@
python convert_hf_llava_to_neva.py \
--in-file <path_to_hf_checkpoints_folder> \
--out-file <path_to_output_nemo_file> \
--tokenizer-model <path_to_sp_tokenizer_model>
--tokenizer-model <path_to_sp_tokenizer_model> \
--conv-template llama_2 # nvgpt, llama_2, v1 (vicuna)
"""

import os
Expand Down Expand Up @@ -49,6 +50,13 @@ def get_args():
"--in-file", type=str, default=None, required=True, help="Path to Huggingface LLaMA checkpoints",
)
parser.add_argument("--out-file", type=str, default=None, required=True, help="Path to output .nemo file.")
parser.add_argument(
"--conv-template",
type=str,
default="llama_2",
required=False,
help="Conversation template: nvgpt, llama_2, v1 (vicuna)",
)
parser.add_argument(
"--tokenizer-model", type=str, default=None, required=False, help="Path to sentencepiece tokenizer model."
)
Expand Down Expand Up @@ -121,6 +129,8 @@ def load_config(args, llava_config):
nemo_config.num_query_groups = llava_config['num_key_value_heads']
nemo_config.use_cpu_initialization = True
nemo_config.activation = 'fast-swiglu'
nemo_config.data.conv_template = args.conv_template
nemo_config.mm_cfg.model_type = args.conv_template
if args.tokenizer_model is None:
nemo_config.tokenizer.model = llava_config['tokenizer_model']
else:
Expand Down
41 changes: 41 additions & 0 deletions examples/multimodal/multimodal_llm/neva/eval/gradio_cli.py
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@@ -0,0 +1,41 @@
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import base64

import requests

# URL of the Gradio server
url = 'http://localhost:8890/api/predict/'

# Prepare the text data
text_data = '<image>Describe this image please.'

# Prepare the image data
with open("/path/to/images/001.jpg", "rb") as image_file:
encoded_string = base64.b64encode(image_file.read()).decode()

# Data to send
data = {'data': [text_data, encoded_string]}

# Sending a POST request to the Gradio server
response = requests.post(url, json=data)

# Checking if the request was successful
if response.status_code == 200:
# Parsing the response
response_data = response.json()
print("Response from server:", response_data)
else:
print("Failed to get a response from the server, status code:", response.status_code)
108 changes: 108 additions & 0 deletions examples/multimodal/multimodal_llm/neva/eval/gradio_server.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,108 @@
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import base64
import io

import gradio as gr
import PIL.Image
from omegaconf import OmegaConf

from nemo.collections.multimodal.parts.utils import create_neva_model_and_processor

CFG_STRING = """
trainer:
devices: 1
num_nodes: 1
accelerator: gpu
logger: False # logger provided by exp_manager
precision: bf16 # 16, 32, or bf16
inference:
greedy: False # Whether or not to use sampling ; use greedy decoding otherwise
top_k: 0 # The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_p: 0.9 # If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.
temperature: 0.2 # sampling temperature
add_BOS: False # add the bos token at the begining of the prompt
tokens_to_generate: 256 # The minimum length of the sequence to be generated.
all_probs: False # whether return the log prob for all the tokens in vocab
repetition_penalty: 1.2 # The parameter for repetition penalty. 1.0 means no penalty.
min_tokens_to_generate: 0 # The minimum length of the sequence to be generated.
compute_logprob: False # a flag used to compute logprob of all the input text, a very special case of running inference, default False
end_strings: ["<extra_id_1>","<extra_id_7>",] # generation will stop when one of these tokens is generated
images_base_path: /pwd/images
insert_image_token: null # `left` or `right` or `null`
cluster_type: BCP
tensor_model_parallel_size: 1
pipeline_model_parallel_size: 1
pipeline_model_parallel_split_rank: 0 # used for encoder and decoder model (0 for others)
neva_model_file: /pwd/nemo_experiments/nemo_llava.nemo #neva_22b_tp8_finetuned_v1.nemo neva_8b_tp4_finetuned_v1.nemo
base_model_file: null
checkpoint_dir: null #/pwd/nemo_multimodal/nemo_experiments/nemo_llava_finetune/checkpoints # checkpoint file dir. This is used to load the PTL checkpoint generated during the Kosmos training
checkpoint_name: null #megatron_clip--val_loss=0.41-step=13499-consumed_samples=431904.0.ckpt # PTL checkpoint file name, only used for PTL checkpoint loading
hparams_file: null #/pwd/nemo_multimodal/nemo_experiments/nemo_llava_finetune/version_0/hparams.yaml # model configuration file, only used for PTL checkpoint loading
"""

cfg = OmegaConf.create(CFG_STRING)
cfg.neva_model_file = "/path/to/llava-v1.5-7b.nemo"
model, image_processor = create_neva_model_and_processor(cfg)


def predict(prompt, image_base64=None):
input_data = {"prompt": prompt}
if image_base64 is not None:
image_data = base64.b64decode(image_base64)
# image = PIL.Image.fromarray(image)
image = PIL.Image.open(io.BytesIO(image_data))
input_data["image"] = image_processor(image)

length_params: LengthParam = {
"max_length": cfg.inference.tokens_to_generate,
"min_length": cfg.inference.min_tokens_to_generate,
}
sampling_params: SamplingParam = {
"use_greedy": cfg.inference.greedy,
"temperature": cfg.inference.temperature,
"top_k": cfg.inference.top_k,
"top_p": cfg.inference.top_p,
"repetition_penalty": cfg.inference.repetition_penalty,
"add_BOS": cfg.inference.add_BOS,
"all_probs": cfg.inference.all_probs,
"compute_logprob": cfg.inference.compute_logprob,
"end_strings": cfg.inference.end_strings,
}

# Generate model responses
responses = model.generate(
input_prompts=[input_data], # Adjust based on your model's requirements
length_params=length_params, # Define these parameters as in your original code
sampling_params=sampling_params, # Define these parameters as in your original code
inference_config=cfg,
)

return responses[0]["clean_response"]


iface = gr.Interface(
fn=predict,
inputs=[gr.Textbox(), gr.Textbox()],
outputs="text",
title="Multimodal Model Inference",
description="Enter a prompt and optionally upload an image for model inference.",
)

if __name__ == "__main__":
iface.launch(server_port=8890, share=False)

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