From 1800cdf1d01fc3fddbd483d5e174eba88bbd9daf Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Sun, 12 Nov 2023 13:35:39 +0800
Subject: [PATCH 01/30] add poetry env

---
 poetry.lock         | 2906 +++++++++++++++++++++++++++++++++++++++++++
 pyproject.toml      |   41 +
 research_journal.md |   65 +
 3 files changed, 3012 insertions(+)
 create mode 100644 poetry.lock
 create mode 100644 pyproject.toml
 create mode 100644 research_journal.md

diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..9ad1a2b
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,2906 @@
+# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
+
+[[package]]
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+version = "0.24.1"
+description = "Accelerate"
+optional = false
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+torch = ">=1.10.0"
+
+[package.extras]
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+quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.0.241)", "urllib3 (<2.0.0)"]
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+test-trackers = ["comet-ml", "tensorboard", "wandb"]
+testing = ["bitsandbytes", "datasets", "deepspeed", "evaluate", "parameterized", "pytest", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "tqdm", "transformers"]
+
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+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
+
+[metadata]
+lock-version = "2.0"
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diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..8208ce7
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,41 @@
+[tool.poetry]
+name = "src"
+version = "0.1.0"
+description = ""
+authors = ["wassname <git@wassname.org>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.9,<3.13"
+torch = {version = "^2.1.0+cu118", source = "pytorch"}
+matplotlib = "^3.8.0"
+loguru = "^0.7.2"
+einops = "^0.3.1"
+torchinfo = "^1.8.0"
+accelerate = "^0.24.1"
+peft = "^0.5.0"
+bitsandbytes = {url = "https://github.com/TimDettmers/bitsandbytes/releases/download/0.41.0/bitsandbytes-0.41.0-py3-none-any.whl"}
+transformers = "4.34.0"
+tqdm = "^4.66.1"
+wandb = "^0.12.6"
+ale-py = "^0.8.1"
+pygame = "^2.5.2"
+psutil = "^5.9.6"
+protobuf = "^3.10.0"
+opencv-python = "^4.8.1.78"
+hydra-core = "^1.3.2"
+gym = {extras = ["accept-rom-license"], version = "^0.26.2"}
+torchvision = "^0.16.0"
+
+[[tool.poetry.source]]
+name = "pytorch"
+url = "https://download.pytorch.org/whl/cu118"
+priority = "explicit"
+
+[tool.poetry.group.dev.dependencies]
+ipykernel = "^6.25.2"
+ruff = "^0.1.3"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/research_journal.md b/research_journal.md
new file mode 100644
index 0000000..5fe1ea2
--- /dev/null
+++ b/research_journal.md
@@ -0,0 +1,65 @@
+# 2023-11-12 13:17:35
+
+Try IRIs but with pretrained transformer with LoRA adapter
+
+- [ ] first can I run it
+- [ ] then can I add 3B with adapter...
+
+```sh
+poetry install
+. ./.venv/bin/activate
+python src/main.py env.train.id=BreakoutNoFrameskip-v4 common.device=cuda:0 wandb.mode=online
+```
+
+
+```sh
+# TODO use this code to load a transformer, and other code from my bigvae repo https://github.com/wassname/bigvae_wm
+def load_model(config, device='cuda'):
+    tokenizer = AutoTokenizer.from_pretrained(config.model_name, trust_remote_code=True)
+    tokenizer.padding_side = "left"
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    bnb_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_compute_dtype=torch.bfloat16,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_use_double_quant=True,
+    )
+    base_model = AutoModelForCausalLM.from_pretrained(
+        config.model_name,
+        device_map={"": device},
+        quantization_config=bnb_config,
+        torch_dtype=torch.bfloat16, 
+        trust_remote_code=True
+    )
+    peft_config = peft.LoraConfig(
+        peft.TaskType.CAUSAL_LM,
+        inference_mode=False,
+        r=config.rank,
+        lora_alpha=8,
+        lora_dropout=config.dropout,
+        target_modules=[
+            "self_attn.q_proj",
+            "self_attn.k_proj",
+            "self_attn.v_proj",
+            "self_attn.o_proj",
+            "mlp.gate_proj",
+            "mlp.up_proj",
+            "mlp.down_proj",
+        ],
+    )
+    base_model_peft = peft.get_peft_model(base_model, peft_config)
+    vae_model = BigVAE(
+        base_model_peft, device, peft_config, z_dim=config.z_dim,
+    )
+    if config.start_from:
+        vae_model.load_pretrained(config.start_from)
+    base_model_peft.requires_grad_(False)
+    vae_model.vae_head.requires_grad_(False)
+    vae_model.vae_head.w_d.requires_grad_()
+    router = BigVAERouter(base_model_peft, vae_model, device, peft_config)
+    if config.start_from:
+        router.load_pretrained(config.start_from, is_trainable=True)
+    print(router.model.print_trainable_parameters())
+    router.model.set_adapter("router")
+```

From 28910cfd63e25853c1ffffff3fa045075ed9b738 Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Sun, 12 Nov 2023 13:57:33 +0800
Subject: [PATCH 02/30] poetry

---
 config/tokenizer/default.yaml |  2 +-
 config/trainer.yaml           |  6 ++--
 poetry.lock                   | 65 ++++++++++++++++-------------------
 pyproject.toml                |  5 +--
 research_journal.md           |  5 ++-
 src/envs/wrappers.py          |  2 +-
 6 files changed, 42 insertions(+), 43 deletions(-)

diff --git a/config/tokenizer/default.yaml b/config/tokenizer/default.yaml
index 54a4dd7..39fe6ae 100644
--- a/config/tokenizer/default.yaml
+++ b/config/tokenizer/default.yaml
@@ -17,4 +17,4 @@ encoder:
     dropout: 0.0
 decoder:
   _target_: models.tokenizer.Decoder
-  config: ${..encoder.config}
\ No newline at end of file
+  config: ${..encoder.config}
diff --git a/config/trainer.yaml b/config/trainer.yaml
index 9e14d17..fe220a7 100644
--- a/config/trainer.yaml
+++ b/config/trainer.yaml
@@ -54,20 +54,20 @@ training:
   should: True
   learning_rate: 0.0001
   tokenizer:
-    batch_num_samples: 256
+    batch_num_samples: 64
     grad_acc_steps: 1
     max_grad_norm: 10.0
     start_after_epochs: 5
     steps_per_epoch: 200
   world_model:
-    batch_num_samples: 64
+    batch_num_samples: 16
     grad_acc_steps: 1
     max_grad_norm: 10.0
     weight_decay: 0.01
     start_after_epochs: 25
     steps_per_epoch: 200
   actor_critic:
-    batch_num_samples: 64
+    batch_num_samples: 16
     grad_acc_steps: 1
     max_grad_norm: 10.0
     start_after_epochs: 50
diff --git a/poetry.lock b/poetry.lock
index 9ad1a2b..cf1ee10 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -31,37 +31,32 @@ testing = ["bitsandbytes", "datasets", "deepspeed", "evaluate", "parameterized",
 
 [[package]]
 name = "ale-py"
-version = "0.8.1"
+version = "0.7.5"
 description = "The Arcade Learning Environment (ALE) - a platform for AI research."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ale_py-0.8.1-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:b2aa2f69a4169742800615970efe6914fa856e33eaf7fa9133c0e06a617a80e2"},
-    {file = "ale_py-0.8.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6f2f6b92c8fd6189654979bbf0b305dbe0ecf82176c47f244d8c1cbc36286b89"},
-    {file = "ale_py-0.8.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c9b168eb88c87d0f3e2a778e6c5cdde4ad951d1ca8a6dc3d3679fd45398df7d1"},
-    {file = "ale_py-0.8.1-cp310-cp310-win_amd64.whl", hash = "sha256:5fcc31f495de79ee1d6bfc0f4b7c4619948851e679bbf010035e25f23146a687"},
-    {file = "ale_py-0.8.1-cp311-cp311-macosx_10_15_x86_64.whl", hash = "sha256:0856ca777473ec4ae8a59f3af9580259adb0fd4a47d586a125a440c62e82fc10"},
-    {file = "ale_py-0.8.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f10b1df8774bbe3b00365748b5e0e07cf35f6a703bbaff991bc7b3b2247dccc9"},
-    {file = "ale_py-0.8.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0006d80dfe7745eb5a93444492337203c8bc7eb594a2c24c6a651c5c5b0eaf09"},
-    {file = "ale_py-0.8.1-cp311-cp311-win_amd64.whl", hash = "sha256:9773eea7505484e024beb2fff0f3bfd363db151bdb9799d70995448e196b1ded"},
-    {file = "ale_py-0.8.1-cp37-cp37m-macosx_10_15_x86_64.whl", hash = "sha256:87557db05be0e04130e2ec1bf909d3bb0b0bc034645d4f664e6baa573fe32191"},
-    {file = "ale_py-0.8.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ae2ba24557e0ce541ea3be13b148db2a9cfa730d83537b4cbed5e10449826e51"},
-    {file = "ale_py-0.8.1-cp37-cp37m-win_amd64.whl", hash = "sha256:ade5c32af567629164a6b49378978c728a15dc4db07ad6b679e8832d4fd3ea1f"},
-    {file = "ale_py-0.8.1-cp38-cp38-macosx_10_15_x86_64.whl", hash = "sha256:0ffecb5c956749596030e464827642945162170a132d093c3d4fa2d7e5725c18"},
-    {file = "ale_py-0.8.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:7cd74b7ee0248ef11a086c9764e142e71defd40ec8989a99232bfd2d9e8023be"},
-    {file = "ale_py-0.8.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eadf9f3990b4ff2f9e5ca35889f5e2e95cddd6a353d9d857d9b4601a6e1c4e7c"},
-    {file = "ale_py-0.8.1-cp38-cp38-win_amd64.whl", hash = "sha256:817adf9a3a82c4923c731e634520a5ecf296aca0367f5c69959a96b32119d831"},
-    {file = "ale_py-0.8.1-cp39-cp39-macosx_10_15_x86_64.whl", hash = "sha256:2d9fcfa06c74a613c5419e942ef4d3e0959533f52e94d2d4bda61d07fbfffeee"},
-    {file = "ale_py-0.8.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:f278036f9b6066062abcdf0987a0ec5a8e0f22a2c7cfac925e39378d4343d490"},
-    {file = "ale_py-0.8.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b00f74e27815131c1a2791f3d48114363fa2708e19f09ce6b7b614cb14c9d469"},
-    {file = "ale_py-0.8.1-cp39-cp39-win_amd64.whl", hash = "sha256:d49b550a2d9c25b63c343aa680fd81f253a3714cdc0e1835640933ebff1798ff"},
+    {file = "ale_py-0.7.5-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:c68d3960f0e14738362069798ff5baa85ecb337038168cb61bd4855125deb894"},
+    {file = "ale_py-0.7.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:003725a8b6f02353066564ab417f4228adc886434aa4c8d314489a7f79fe9dfd"},
+    {file = "ale_py-0.7.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1a30270d4aec820527be7a643aad381d0d1c932dc476740f63cc649dd200e065"},
+    {file = "ale_py-0.7.5-cp310-cp310-win_amd64.whl", hash = "sha256:1a72f8a311887fe3800964976c7ce3567dd701e7d9b77209d3a05c5cdf9c6af0"},
+    {file = "ale_py-0.7.5-cp37-cp37m-macosx_10_15_x86_64.whl", hash = "sha256:910c5443a836bf0bc6b03c45fefeeb9096254f807d24e247753e214062c12cae"},
+    {file = "ale_py-0.7.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1f97301b89a8b49b9d2cc034f8a7df55eb6b1930126fb6cb6c6d6239bab8a89d"},
+    {file = "ale_py-0.7.5-cp37-cp37m-win_amd64.whl", hash = "sha256:38e9f6c2b1cca246253f41028b8ad92d725beec8d4672c0fe247a217a4c7dc4f"},
+    {file = "ale_py-0.7.5-cp38-cp38-macosx_10_15_x86_64.whl", hash = "sha256:48c5abf2c59bed0b46f17ac60ec6c940e6e7d4abc6ff7e5296246286f80e4a23"},
+    {file = "ale_py-0.7.5-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:b2680b9a3c577865572dd9b8f24303189bb22c67b1b9668dcad3c20f59285e78"},
+    {file = "ale_py-0.7.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dec87873c6fe65c71129e20c52ce35dd4507ca409fa1233f4ae9c511144a9c99"},
+    {file = "ale_py-0.7.5-cp38-cp38-win_amd64.whl", hash = "sha256:1f8694a091e13be45c8704a089462b1519f7112a85e1c1b61c842c69a2608a98"},
+    {file = "ale_py-0.7.5-cp39-cp39-macosx_10_15_x86_64.whl", hash = "sha256:4c7e7fed3ce2b305d2db9ca2f87106e3df6da2c4fc2363103782ffd5975b4549"},
+    {file = "ale_py-0.7.5-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:da5be311285a00e587b886805ea56762d44bf4f1da672229c1fbaff209bfcdc2"},
+    {file = "ale_py-0.7.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e513799fbb5128fa045cfb9a31cffa7f7c188dbe885665188b6c4cb50e9d983c"},
+    {file = "ale_py-0.7.5-cp39-cp39-win_amd64.whl", hash = "sha256:9976ceea0251ccad7e562e2021c337d5e9a9b24b5f9eecfe82573c2279337016"},
 ]
 
 [package.dependencies]
 importlib-metadata = {version = ">=4.10.0", markers = "python_version < \"3.10\""}
 importlib-resources = "*"
 numpy = "*"
-typing-extensions = {version = "*", markers = "python_version < \"3.11\""}
 
 [package.extras]
 test = ["gym (>=0.23,<1.0)", "pytest (>=7.0)"]
@@ -701,32 +696,32 @@ test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre
 
 [[package]]
 name = "gym"
-version = "0.26.2"
-description = "Gym: A universal API for reinforcement learning environments"
+version = "0.22.0"
+description = "Gym: A universal API for reinforcement learning environments."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "gym-0.26.2.tar.gz", hash = "sha256:e0d882f4b54f0c65f203104c24ab8a38b039f1289986803c7d02cdbe214fbcc4"},
+    {file = "gym-0.22.0.tar.gz", hash = "sha256:339144c89951758187c378111919bc0e2f1695f9e9d9699e3f19279a6398148d"},
 ]
 
 [package.dependencies]
+ale-py = {version = ">=0.7.4,<0.8.0", optional = true, markers = "extra == \"atari\""}
 autorom = {version = ">=0.4.2,<0.5.0", extras = ["accept-rom-license"], optional = true, markers = "extra == \"accept-rom-license\""}
 cloudpickle = ">=1.2.0"
 gym_notices = ">=0.0.4"
-importlib_metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""}
+importlib_metadata = {version = ">=4.10.0", markers = "python_version < \"3.10\""}
 numpy = ">=1.18.0"
 
 [package.extras]
 accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
-all = ["ale-py (>=0.8.0,<0.9.0)", "box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (==2.2)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (==4.*)"]
-atari = ["ale-py (>=0.8.0,<0.9.0)"]
-box2d = ["box2d-py (==2.3.5)", "pygame (==2.1.0)", "swig (==4.*)"]
+all = ["ale-py (>=0.7.4,<0.8.0)", "box2d-py (==2.3.5)", "box2d-py (==2.3.5)", "lz4 (>=3.1.0)", "lz4 (>=3.1.0)", "mujoco_py (>=1.50,<2.0)", "opencv-python (>=3.0)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "scipy (>=1.4.1)", "scipy (>=1.4.1)"]
+atari = ["ale-py (>=0.7.4,<0.8.0)"]
+box2d = ["box2d-py (==2.3.5)", "pygame (==2.1.0)"]
 classic-control = ["pygame (==2.1.0)"]
-mujoco = ["imageio (>=2.14.1)", "mujoco (==2.2)"]
-mujoco-py = ["mujoco_py (>=2.1,<2.2)"]
-other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-python (>=3.0)"]
-testing = ["box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (==2.2)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (==4.*)"]
-toy-text = ["pygame (==2.1.0)"]
+mujoco = ["mujoco_py (>=1.50,<2.0)"]
+nomujoco = ["box2d-py (==2.3.5)", "lz4 (>=3.1.0)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "scipy (>=1.4.1)"]
+other = ["lz4 (>=3.1.0)", "opencv-python (>=3.0)"]
+toy-text = ["pygame (==2.1.0)", "scipy (>=1.4.1)"]
 
 [[package]]
 name = "gym-notices"
@@ -2903,4 +2898,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "914e609489e134bb4b844f424ea35ef15e1a6288e634ad4046889785123a1e1f"
+content-hash = "3fb1a61c2bf4b6fa530dff25509331cb4a0dfe863f77355d01e886feb06c9b34"
diff --git a/pyproject.toml b/pyproject.toml
index 8208ce7..fd1121a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,14 +18,15 @@ bitsandbytes = {url = "https://github.com/TimDettmers/bitsandbytes/releases/down
 transformers = "4.34.0"
 tqdm = "^4.66.1"
 wandb = "^0.12.6"
-ale-py = "^0.8.1"
+ale-py = "^0.7.4"
 pygame = "^2.5.2"
 psutil = "^5.9.6"
 protobuf = "^3.10.0"
 opencv-python = "^4.8.1.78"
 hydra-core = "^1.3.2"
-gym = {extras = ["accept-rom-license"], version = "^0.26.2"}
 torchvision = "^0.16.0"
+numpy = ">=1.18.0"
+gym = {version = "0.22.0", extras = ["accept-rom-license", "atari"]}
 
 [[tool.poetry.source]]
 name = "pytorch"
diff --git a/research_journal.md b/research_journal.md
index 5fe1ea2..57c3c0b 100644
--- a/research_journal.md
+++ b/research_journal.md
@@ -8,7 +8,10 @@ Try IRIs but with pretrained transformer with LoRA adapter
 ```sh
 poetry install
 . ./.venv/bin/activate
-python src/main.py env.train.id=BreakoutNoFrameskip-v4 common.device=cuda:0 wandb.mode=online
+python src/main.py env.train.id=BreakoutNoFrameskip-v4 common.device=cuda:0 wandb.mode=offline
+
+# or for quick debug
+WANDB_MODE=disabled python -m pdb src/main.py env.train.id=BreakoutNoFrameskip-v4
 ```
 
 
diff --git a/src/envs/wrappers.py b/src/envs/wrappers.py
index b1054a1..eca6f88 100644
--- a/src/envs/wrappers.py
+++ b/src/envs/wrappers.py
@@ -64,7 +64,7 @@ class NoopResetEnv(gym.Wrapper):
         if self.override_num_noops is not None:
             noops = self.override_num_noops
         else:
-            noops = self.unwrapped.np_random.randint(1, self.noop_max + 1)
+            noops = self.unwrapped.np_random.integers(1, self.noop_max + 1)
         assert noops > 0
         obs = None
         for _ in range(noops):

From 7dfded5bde61430d1ac3b3712497865d7649471a Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Sun, 12 Nov 2023 14:41:59 +0800
Subject: [PATCH 03/30] debuggign

---
 .vscode/launch.json | 25 +++++++++++++++++++++++++
 research_journal.md | 32 +++++++++++++++++++++++++++++++-
 2 files changed, 56 insertions(+), 1 deletion(-)
 create mode 100644 .vscode/launch.json

diff --git a/.vscode/launch.json b/.vscode/launch.json
new file mode 100644
index 0000000..fda7a32
--- /dev/null
+++ b/.vscode/launch.json
@@ -0,0 +1,25 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "main",
+            "type": "python",
+            "request": "launch",
+            "program": "${workspaceFolder}/src/main.py",
+            "console": "integratedTerminal",
+            "justMyCode": false,
+            "autoReload": {"enable": true,},
+            "env": {"WANDB_MODE":"disabled"},
+            "args": [
+                "env.train.id=BreakoutNoFrameskip-v4",
+                // # make it start early
+                "trainer.training.tokenizer.start_after_epochs=1",
+                "trainer.training.world_model.start_after_epochs=2",
+                "trainer.training.actor_critic.start_after_epochs=3",
+            ]
+        }
+    ]
+}
diff --git a/research_journal.md b/research_journal.md
index 57c3c0b..6be63bb 100644
--- a/research_journal.md
+++ b/research_journal.md
@@ -2,7 +2,7 @@
 
 Try IRIs but with pretrained transformer with LoRA adapter
 
-- [ ] first can I run it
+- [x] first can I run it yes with a 1/2 batch size
 - [ ] then can I add 3B with adapter...
 
 ```sh
@@ -66,3 +66,33 @@ def load_model(config, device='cuda'):
     print(router.model.print_trainable_parameters())
     router.model.set_adapter("router")
 ```
+
+Debugging:
+    batch['observations'].shape
+    torch.Size([16, 20, 3, 64, 64])
+
+    obs_tokens.shape
+    torch.Size([16, 20, 16])
+
+    https://vscode.dev/github/wassname/iris_bigvae/blob/just_llms2/src/models/world_model.py#L105
+    tokens
+    tensor([[222, 222, 222,  ..., 409,  55,   2],
+            [222, 222, 222,  ..., 409, 139,   1],
+            [222, 222, 222,  ..., 168, 190,   3],
+            ...,
+            [222, 222, 222,  ..., 168,  55,   0],
+            [222, 222, 222,  ..., 237, 190,   3],
+            [222, 222, 222,  ..., 168,  55,   0]], device='cuda:0')
+    tokens.shape
+    torch.Size([16, 340])
+    where 16 is the batch size. 340 is the step size?. actions was 16,20 int
+
+    tokens.shape int
+    torch.Size([16, 340])
+
+    sequences.shape float32
+    torch.Size([16, 340, 256])
+
+    transfrmer
+    x.shape
+    torch.Size([16, 340, 256])

From a6b850bd9655a334c7f901fc0efc0635968d35c4 Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Sun, 12 Nov 2023 14:44:10 +0800
Subject: [PATCH 04/30] fix debug launch

---
 .vscode/launch.json       |  6 +++---
 config/trainer.yaml       |  2 +-
 src/models/transformer.py | 44 +++++++++++++++++++--------------------
 3 files changed, 25 insertions(+), 27 deletions(-)

diff --git a/.vscode/launch.json b/.vscode/launch.json
index fda7a32..ac43a5c 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -16,9 +16,9 @@
             "args": [
                 "env.train.id=BreakoutNoFrameskip-v4",
                 // # make it start early
-                "trainer.training.tokenizer.start_after_epochs=1",
-                "trainer.training.world_model.start_after_epochs=2",
-                "trainer.training.actor_critic.start_after_epochs=3",
+                "training.tokenizer.start_after_epochs=1",
+                "training.world_model.start_after_epochs=2",
+                "training.actor_critic.start_after_epochs=3",
             ]
         }
     ]
diff --git a/config/trainer.yaml b/config/trainer.yaml
index fe220a7..bd02bf0 100644
--- a/config/trainer.yaml
+++ b/config/trainer.yaml
@@ -7,7 +7,7 @@ defaults:
   - datasets: default
 
 wandb:
-  mode: online
+  mode: offline
   project: iris
   entity: null
   name: null
diff --git a/src/models/transformer.py b/src/models/transformer.py
index 2aaac69..dea058d 100644
--- a/src/models/transformer.py
+++ b/src/models/transformer.py
@@ -32,29 +32,6 @@ class TransformerConfig:
     def max_tokens(self):
         return self.tokens_per_block * self.max_blocks
 
-
-class Transformer(nn.Module):
-    def __init__(self, config: TransformerConfig) -> None:
-        super().__init__()
-        self.config = config
-        self.drop = nn.Dropout(config.embed_pdrop)
-        self.blocks = nn.ModuleList([Block(config) for _ in range(config.num_layers)])
-        self.ln_f = nn.LayerNorm(config.embed_dim)
-
-    def generate_empty_keys_values(self, n: int, max_tokens: int) -> KeysValues:
-        device = self.ln_f.weight.device  # Assumption that all submodules are on the same device
-        return KeysValues(n, self.config.num_heads, max_tokens, self.config.embed_dim, self.config.num_layers, device)
-
-    def forward(self, sequences: torch.Tensor, past_keys_values: Optional[KeysValues] = None) -> torch.Tensor:
-        assert past_keys_values is None or len(past_keys_values) == len(self.blocks)
-        x = self.drop(sequences)
-        for i, block in enumerate(self.blocks):
-            x = block(x, None if past_keys_values is None else past_keys_values[i])
-
-        x = self.ln_f(x)
-        return x
-
-
 class Block(nn.Module):
     def __init__(self, config: TransformerConfig) -> None:
         super().__init__()
@@ -118,3 +95,24 @@ class SelfAttention(nn.Module):
         y = self.resid_drop(self.proj(y))
 
         return y
+
+class Transformer(nn.Module):
+    def __init__(self, config: TransformerConfig) -> None:
+        super().__init__()
+        self.config = config
+        self.drop = nn.Dropout(config.embed_pdrop)
+        self.blocks = nn.ModuleList([Block(config) for _ in range(config.num_layers)])
+        self.ln_f = nn.LayerNorm(config.embed_dim)
+
+    def generate_empty_keys_values(self, n: int, max_tokens: int) -> KeysValues:
+        device = self.ln_f.weight.device  # Assumption that all submodules are on the same device
+        return KeysValues(n, self.config.num_heads, max_tokens, self.config.embed_dim, self.config.num_layers, device)
+
+    def forward(self, sequences: torch.Tensor, past_keys_values: Optional[KeysValues] = None) -> torch.Tensor:
+        assert past_keys_values is None or len(past_keys_values) == len(self.blocks)
+        x = self.drop(sequences)
+        for i, block in enumerate(self.blocks):
+            x = block(x, None if past_keys_values is None else past_keys_values[i])
+
+        x = self.ln_f(x)
+        return x

From 1454e5c053c1c9ab84370703b20e8671a85840f5 Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Sun, 12 Nov 2023 16:13:23 +0800
Subject: [PATCH 05/30] training

---
 .vscode/launch.json             |  5 ++-
 config/trainer.yaml             |  6 +--
 config/world_model/default.yaml |  2 +-
 poetry.lock                     | 44 ++++++++++++++++++-
 pyproject.toml                  |  1 +
 src/models/transformer.py       | 76 +++++++++++++++++++++++++++++++--
 src/models/world_model.py       |  5 ++-
 7 files changed, 126 insertions(+), 13 deletions(-)

diff --git a/.vscode/launch.json b/.vscode/launch.json
index ac43a5c..2648c79 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -17,8 +17,9 @@
                 "env.train.id=BreakoutNoFrameskip-v4",
                 // # make it start early
                 "training.tokenizer.start_after_epochs=1",
-                "training.world_model.start_after_epochs=2",
-                "training.actor_critic.start_after_epochs=3",
+                "training.world_model.start_after_epochs=1",
+                "training.actor_critic.start_after_epochs=1",
+                "training.tokenizer.steps_per_epoch=20",
             ]
         }
     ]
diff --git a/config/trainer.yaml b/config/trainer.yaml
index bd02bf0..01af86e 100644
--- a/config/trainer.yaml
+++ b/config/trainer.yaml
@@ -54,20 +54,20 @@ training:
   should: True
   learning_rate: 0.0001
   tokenizer:
-    batch_num_samples: 64
+    batch_num_samples: 32
     grad_acc_steps: 1
     max_grad_norm: 10.0
     start_after_epochs: 5
     steps_per_epoch: 200
   world_model:
-    batch_num_samples: 16
+    batch_num_samples: 4
     grad_acc_steps: 1
     max_grad_norm: 10.0
     weight_decay: 0.01
     start_after_epochs: 25
     steps_per_epoch: 200
   actor_critic:
-    batch_num_samples: 16
+    batch_num_samples: 8
     grad_acc_steps: 1
     max_grad_norm: 10.0
     start_after_epochs: 50
diff --git a/config/world_model/default.yaml b/config/world_model/default.yaml
index bfcbb31..d03a227 100644
--- a/config/world_model/default.yaml
+++ b/config/world_model/default.yaml
@@ -4,7 +4,7 @@ max_blocks: 20
 attention: 'causal'
 num_layers: 10
 num_heads: 4
-embed_dim: 256
+embed_dim: 2560 # change this to whatever the embedding dimension is in your pretrained llm
 embed_pdrop: 0.1
 resid_pdrop: 0.1
 attn_pdrop: 0.1
diff --git a/poetry.lock b/poetry.lock
index cf1ee10..aafa8da 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2214,6 +2214,48 @@ tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
 testing = ["h5py (>=3.7.0)", "huggingface_hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools_rust (>=1.5.2)"]
 torch = ["safetensors[numpy]", "torch (>=1.10)"]
 
+[[package]]
+name = "scipy"
+version = "1.11.3"
+description = "Fundamental algorithms for scientific computing in Python"
+optional = false
+python-versions = "<3.13,>=3.9"
+files = [
+    {file = "scipy-1.11.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:370f569c57e1d888304052c18e58f4a927338eafdaef78613c685ca2ea0d1fa0"},
+    {file = "scipy-1.11.3-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:9885e3e4f13b2bd44aaf2a1a6390a11add9f48d5295f7a592393ceb8991577a3"},
+    {file = "scipy-1.11.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e04aa19acc324a1a076abb4035dabe9b64badb19f76ad9c798bde39d41025cdc"},
+    {file = "scipy-1.11.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3e1a8a4657673bfae1e05e1e1d6e94b0cabe5ed0c7c144c8aa7b7dbb774ce5c1"},
+    {file = "scipy-1.11.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:7abda0e62ef00cde826d441485e2e32fe737bdddee3324e35c0e01dee65e2a88"},
+    {file = "scipy-1.11.3-cp310-cp310-win_amd64.whl", hash = "sha256:033c3fd95d55012dd1148b201b72ae854d5086d25e7c316ec9850de4fe776929"},
+    {file = "scipy-1.11.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:925c6f09d0053b1c0f90b2d92d03b261e889b20d1c9b08a3a51f61afc5f58165"},
+    {file = "scipy-1.11.3-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:5664e364f90be8219283eeb844323ff8cd79d7acbd64e15eb9c46b9bc7f6a42a"},
+    {file = "scipy-1.11.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:00f325434b6424952fbb636506f0567898dca7b0f7654d48f1c382ea338ce9a3"},
+    {file = "scipy-1.11.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5f290cf561a4b4edfe8d1001ee4be6da60c1c4ea712985b58bf6bc62badee221"},
+    {file = "scipy-1.11.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:91770cb3b1e81ae19463b3c235bf1e0e330767dca9eb4cd73ba3ded6c4151e4d"},
+    {file = "scipy-1.11.3-cp311-cp311-win_amd64.whl", hash = "sha256:e1f97cd89c0fe1a0685f8f89d85fa305deb3067d0668151571ba50913e445820"},
+    {file = "scipy-1.11.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:dfcc1552add7cb7c13fb70efcb2389d0624d571aaf2c80b04117e2755a0c5d15"},
+    {file = "scipy-1.11.3-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:0d3a136ae1ff0883fffbb1b05b0b2fea251cb1046a5077d0b435a1839b3e52b7"},
+    {file = "scipy-1.11.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bae66a2d7d5768eaa33008fa5a974389f167183c87bf39160d3fefe6664f8ddc"},
+    {file = "scipy-1.11.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d2f6dee6cbb0e263b8142ed587bc93e3ed5e777f1f75448d24fb923d9fd4dce6"},
+    {file = "scipy-1.11.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:74e89dc5e00201e71dd94f5f382ab1c6a9f3ff806c7d24e4e90928bb1aafb280"},
+    {file = "scipy-1.11.3-cp312-cp312-win_amd64.whl", hash = "sha256:90271dbde4be191522b3903fc97334e3956d7cfb9cce3f0718d0ab4fd7d8bfd6"},
+    {file = "scipy-1.11.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:a63d1ec9cadecce838467ce0631c17c15c7197ae61e49429434ba01d618caa83"},
+    {file = "scipy-1.11.3-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:5305792c7110e32ff155aed0df46aa60a60fc6e52cd4ee02cdeb67eaccd5356e"},
+    {file = "scipy-1.11.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9ea7f579182d83d00fed0e5c11a4aa5ffe01460444219dedc448a36adf0c3917"},
+    {file = "scipy-1.11.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c77da50c9a91e23beb63c2a711ef9e9ca9a2060442757dffee34ea41847d8156"},
+    {file = "scipy-1.11.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:15f237e890c24aef6891c7d008f9ff7e758c6ef39a2b5df264650eb7900403c0"},
+    {file = "scipy-1.11.3-cp39-cp39-win_amd64.whl", hash = "sha256:4b4bb134c7aa457e26cc6ea482b016fef45db71417d55cc6d8f43d799cdf9ef2"},
+    {file = "scipy-1.11.3.tar.gz", hash = "sha256:bba4d955f54edd61899776bad459bf7326e14b9fa1c552181f0479cc60a568cd"},
+]
+
+[package.dependencies]
+numpy = ">=1.21.6,<1.28.0"
+
+[package.extras]
+dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
+doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
+test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
 [[package]]
 name = "sentry-sdk"
 version = "1.34.0"
@@ -2898,4 +2940,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "3fb1a61c2bf4b6fa530dff25509331cb4a0dfe863f77355d01e886feb06c9b34"
+content-hash = "e2ad5cf8670b043bb7174a4c7f6e4c89644461eb1680e282b436541672e4e67c"
diff --git a/pyproject.toml b/pyproject.toml
index fd1121a..3cad2fb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -27,6 +27,7 @@ hydra-core = "^1.3.2"
 torchvision = "^0.16.0"
 numpy = ">=1.18.0"
 gym = {version = "0.22.0", extras = ["accept-rom-license", "atari"]}
+scipy = "^1.11.3"
 
 [[tool.poetry.source]]
 name = "pytorch"
diff --git a/src/models/transformer.py b/src/models/transformer.py
index dea058d..431fe73 100644
--- a/src/models/transformer.py
+++ b/src/models/transformer.py
@@ -16,6 +16,8 @@ from .kv_caching import KeysValues, KVCache
 
 @dataclass
 class TransformerConfig:
+
+        
     tokens_per_block: int
     max_blocks: int
     attention: str
@@ -27,6 +29,12 @@ class TransformerConfig:
     embed_pdrop: float
     resid_pdrop: float
     attn_pdrop: float
+    
+    model_name: str = "stabilityai/stablelm-3b-4e1t"
+    dropout: float = 0
+    rank: int = 32
+    z_dim: int = 768
+    start_from: str = None
 
     @property
     def max_tokens(self):
@@ -100,19 +108,79 @@ class Transformer(nn.Module):
     def __init__(self, config: TransformerConfig) -> None:
         super().__init__()
         self.config = config
-        self.drop = nn.Dropout(config.embed_pdrop)
-        self.blocks = nn.ModuleList([Block(config) for _ in range(config.num_layers)])
-        self.ln_f = nn.LayerNorm(config.embed_dim)
+        self.model = load_pretrained_model(config)
+        # self.ln_f = nn.LayerNorm(self.model.config.vocab_size, config.embed_dim)
+        self.ln_f = nn.Linear(self.model.config.vocab_size, config.embed_dim)
+        # self.drop = nn.Dropout(config.embed_pdrop)
+        # self.blocks = nn.ModuleList([Block(config) for _ in range(config.num_layers)])
+        # self.ln_f = nn.LayerNorm(config.embed_dim)
 
     def generate_empty_keys_values(self, n: int, max_tokens: int) -> KeysValues:
         device = self.ln_f.weight.device  # Assumption that all submodules are on the same device
         return KeysValues(n, self.config.num_heads, max_tokens, self.config.embed_dim, self.config.num_layers, device)
 
+    # @torch.cuda.amp.autocast(dtype=torch.bfloat16)
     def forward(self, sequences: torch.Tensor, past_keys_values: Optional[KeysValues] = None) -> torch.Tensor:
-        assert past_keys_values is None or len(past_keys_values) == len(self.blocks)
+        # assert past_keys_values is None or len(past_keys_values) == len(self.blocks)
+        sequences = sequences.to(torch.bfloat16)
+        outputs = self.model(
+            inputs_embeds=sequences,
+            return_dict=True,
+            output_hidden_states=True,
+        )
+        x = outputs.logits.to(torch.float32)
+        # TODO: we output with last dim 50304 but want 2560 (embed dim)
+        x = self.ln_f(x)
+        return x
+        
+        inputs_embeds
         x = self.drop(sequences)
         for i, block in enumerate(self.blocks):
             x = block(x, None if past_keys_values is None else past_keys_values[i])
 
         x = self.ln_f(x)
         return x
+
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel, LoraConfig
+import peft
+
+def load_pretrained_model(config, device="cuda:0"):
+    tokenizer = AutoTokenizer.from_pretrained(config.model_name, trust_remote_code=True)
+    tokenizer.padding_side = "left"
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    bnb_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_compute_dtype=torch.bfloat16,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_use_double_quant=True,
+    )
+    base_model = AutoModelForCausalLM.from_pretrained(
+        config.model_name,
+        device_map={"": device},
+        quantization_config=bnb_config,
+        torch_dtype=torch.bfloat16, 
+        trust_remote_code=True
+    )
+    peft_config = peft.LoraConfig(
+        peft.TaskType.CAUSAL_LM,
+        inference_mode=False,
+        r=config.rank,
+        lora_alpha=8,
+        lora_dropout=config.dropout,
+        target_modules=[
+            "self_attn.q_proj",
+            "self_attn.k_proj",
+            "self_attn.v_proj",
+            "self_attn.o_proj",
+            "mlp.gate_proj",
+            "mlp.up_proj",
+            "mlp.down_proj",
+        ],
+    )
+    base_model_peft = peft.get_peft_model(base_model, peft_config)
+    base_model_peft.add_adapter("dynamics", peft_config)
+    print(base_model_peft.print_trainable_parameters())
+    return base_model_peft
diff --git a/src/models/world_model.py b/src/models/world_model.py
index 0691034..194119e 100644
--- a/src/models/world_model.py
+++ b/src/models/world_model.py
@@ -26,8 +26,7 @@ class WorldModel(nn.Module):
     def __init__(self, obs_vocab_size: int, act_vocab_size: int, config: TransformerConfig) -> None:
         super().__init__()
         self.obs_vocab_size, self.act_vocab_size = obs_vocab_size, act_vocab_size
-        self.config = config
-        self.transformer = Transformer(config)
+        self.config = config       
 
         all_but_last_obs_tokens_pattern = torch.ones(config.tokens_per_block)
         all_but_last_obs_tokens_pattern[-2] = 0
@@ -74,6 +73,8 @@ class WorldModel(nn.Module):
         )
 
         self.apply(init_weights)
+        
+        self.transformer = Transformer(config)
 
     def __repr__(self) -> str:
         return "world_model"

From a3ded691e1746c8969f9814e3c2ae92981f30bac Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Sun, 12 Nov 2023 17:27:17 +0800
Subject: [PATCH 06/30] misc

---
 .vscode/launch.json       |  2 ++
 research_journal.md       |  6 ++++++
 src/models/transformer.py | 13 -------------
 3 files changed, 8 insertions(+), 13 deletions(-)

diff --git a/.vscode/launch.json b/.vscode/launch.json
index 2648c79..0705eb8 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -20,6 +20,8 @@
                 "training.world_model.start_after_epochs=1",
                 "training.actor_critic.start_after_epochs=1",
                 "training.tokenizer.steps_per_epoch=20",
+                "training.world_model.steps_per_epoch=10",
+                "training.actor_critic.steps_per_epoch=20",
             ]
         }
     ]
diff --git a/research_journal.md b/research_journal.md
index 6be63bb..243c94c 100644
--- a/research_journal.md
+++ b/research_journal.md
@@ -96,3 +96,9 @@ Debugging:
     transfrmer
     x.shape
     torch.Size([16, 340, 256])
+
+# 2023-11-12 16:58:37
+
+So I got it training, but during imagination it passes in a single token with no past steps. But the slicer seems to need at least on block? And so I get none?
+
+hmm it's because num_kept_tokens is 16 not 1. So there should be a whole block passed in ?
diff --git a/src/models/transformer.py b/src/models/transformer.py
index 431fe73..7c0c44c 100644
--- a/src/models/transformer.py
+++ b/src/models/transformer.py
@@ -109,11 +109,7 @@ class Transformer(nn.Module):
         super().__init__()
         self.config = config
         self.model = load_pretrained_model(config)
-        # self.ln_f = nn.LayerNorm(self.model.config.vocab_size, config.embed_dim)
         self.ln_f = nn.Linear(self.model.config.vocab_size, config.embed_dim)
-        # self.drop = nn.Dropout(config.embed_pdrop)
-        # self.blocks = nn.ModuleList([Block(config) for _ in range(config.num_layers)])
-        # self.ln_f = nn.LayerNorm(config.embed_dim)
 
     def generate_empty_keys_values(self, n: int, max_tokens: int) -> KeysValues:
         device = self.ln_f.weight.device  # Assumption that all submodules are on the same device
@@ -129,15 +125,6 @@ class Transformer(nn.Module):
             output_hidden_states=True,
         )
         x = outputs.logits.to(torch.float32)
-        # TODO: we output with last dim 50304 but want 2560 (embed dim)
-        x = self.ln_f(x)
-        return x
-        
-        inputs_embeds
-        x = self.drop(sequences)
-        for i, block in enumerate(self.blocks):
-            x = block(x, None if past_keys_values is None else past_keys_values[i])
-
         x = self.ln_f(x)
         return x
 

From c8f59b0504376b3f131f0871eb1b1e36a5538bfc Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Sun, 12 Nov 2023 18:41:22 +0800
Subject: [PATCH 07/30] misc

---
 .vscode/launch.json | 10 +++++-----
 research_journal.md |  9 +++++++++
 2 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/.vscode/launch.json b/.vscode/launch.json
index 0705eb8..e0c9bdf 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -17,11 +17,11 @@
                 "env.train.id=BreakoutNoFrameskip-v4",
                 // # make it start early
                 "training.tokenizer.start_after_epochs=1",
-                "training.world_model.start_after_epochs=1",
-                "training.actor_critic.start_after_epochs=1",
-                "training.tokenizer.steps_per_epoch=20",
-                "training.world_model.steps_per_epoch=10",
-                "training.actor_critic.steps_per_epoch=20",
+                "training.world_model.start_after_epochs=2",
+                "training.actor_critic.start_after_epochs=3",
+                "training.tokenizer.steps_per_epoch=40",
+                "training.world_model.steps_per_epoch=40",
+                "training.actor_critic.steps_per_epoch=40",
             ]
         }
     ]
diff --git a/research_journal.md b/research_journal.md
index 243c94c..689861b 100644
--- a/research_journal.md
+++ b/research_journal.md
@@ -102,3 +102,12 @@ Debugging:
 So I got it training, but during imagination it passes in a single token with no past steps. But the slicer seems to need at least on block? And so I get none?
 
 hmm it's because num_kept_tokens is 16 not 1. So there should be a whole block passed in ?
+
+wait apparently it's also a problem in the normal repo.... I confuse! maybe it's my config! maybe I need >larger than block size. nope
+
+hmm it still happens in the original repo with my debug params. maybe it's my debug params
+
+
+... trying a full run without my debug params...
+
+note trains.world_model.batch_num_samples:4 fill 20GB gpu ram for the 3b stability ai llm

From b97a499a195467ac050091a28b7afe11bfdb2086 Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Mon, 13 Nov 2023 07:00:17 +0800
Subject: [PATCH 08/30] Fix bugs and improve performance in training process

---
 .vscode/launch.json         | 10 +++++-----
 research_journal.md         | 36 ++++++++++++++++++++++++++++++++++++
 src/envs/world_model_env.py |  5 +++--
 src/models/slicer.py        |  3 ++-
 src/models/transformer.py   | 10 +++++++++-
 5 files changed, 55 insertions(+), 9 deletions(-)

diff --git a/.vscode/launch.json b/.vscode/launch.json
index e0c9bdf..06bb888 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -17,11 +17,11 @@
                 "env.train.id=BreakoutNoFrameskip-v4",
                 // # make it start early
                 "training.tokenizer.start_after_epochs=1",
-                "training.world_model.start_after_epochs=2",
-                "training.actor_critic.start_after_epochs=3",
-                "training.tokenizer.steps_per_epoch=40",
-                "training.world_model.steps_per_epoch=40",
-                "training.actor_critic.steps_per_epoch=40",
+                "training.world_model.start_after_epochs=1",
+                "training.actor_critic.start_after_epochs=1",
+                "training.tokenizer.steps_per_epoch=10",
+                "training.world_model.steps_per_epoch=10",
+                "training.actor_critic.steps_per_epoch=10",
             ]
         }
     ]
diff --git a/research_journal.md b/research_journal.md
index 689861b..94d442f 100644
--- a/research_journal.md
+++ b/research_journal.md
@@ -111,3 +111,39 @@ hmm it still happens in the original repo with my debug params. maybe it's my de
 ... trying a full run without my debug params...
 
 note trains.world_model.batch_num_samples:4 fill 20GB gpu ram for the 3b stability ai llm
+
+ok even with a full run I get the error. I think it's a bug in the original repo. I'll try to debug it there.
+
+    Epoch 51 / 600
+
+    Experience collection (train_dataset): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:03<00:00, 59.91it/s]
+    Training tokenizer: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:17<00:00, 11.53it/s]
+    Training world_model: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [02:11<00:00,  1.53it/s]
+    Training actor_critic:   0%|                                                                                                                                                                                     | 0/200 [00:00<?, ?it/s]
+    Error executing job with overrides: ['env.train.id=BreakoutNoFrameskip-v4', 'common.device=cuda:0', 'wandb.mode=offline']
+    Traceback (most recent call last):
+    File "/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/src/main.py", line 10, in main
+        trainer.run()
+    File "/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/src/trainer.py", line 111, in run
+        to_log += self.train_agent(epoch)
+    File "/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/src/trainer.py", line 146, in train_agent
+        metrics_actor_critic = self.train_component(self.agent.actor_critic, self.optimizer_actor_critic, sequence_length=1 + self.cfg.training.actor_critic.burn_in, sample_from_start=False, tokenizer=self.agent.tokenizer, world_model=self.agent.world_model, **cfg_actor_critic)
+    File "/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/src/trainer.py", line 161, in train_component
+        losses = component.compute_loss(batch, **kwargs_loss) / grad_acc_steps
+    File "/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/src/models/actor_critic.py", line 102, in compute_loss
+        outputs = self.imagine(batch, tokenizer, world_model, horizon=imagine_horizon)
+    File "/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/src/models/actor_critic.py", line 149, in imagine
+        obs, reward, done, _ = wm_env.step(action_token, should_predict_next_obs=(k < horizon - 1))
+    File "/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
+        return func(*args, **kwargs)
+    File "/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/src/envs/world_model_env.py", line 75, in step
+        reward = Categorical(logits=outputs_wm.logits_rewards).sample().float().cpu().numpy().reshape(-1) - 1   # (B,)
+    File "/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/torch/distributions/categorical.py", line 70, in __init__
+        super().__init__(batch_shape, validate_args=validate_args)
+    File "/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/torch/distributions/distribution.py", line 66, in __init__
+        valid = constraint.check(value)
+    File "/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/torch/distributions/constraints.py", line 226, in check
+        result = result.reshape(
+    RuntimeError: cannot reshape tensor of 0 elements into shape [8, 0, -1] because the unspecified dimension size -1 can be any value and is ambiguous
+
+Oh maybe it's because we don't keep track of KV cache, but it's actually used to track number of steps!!
diff --git a/src/envs/world_model_env.py b/src/envs/world_model_env.py
index a159fb2..13af000 100644
--- a/src/envs/world_model_env.py
+++ b/src/envs/world_model_env.py
@@ -56,7 +56,7 @@ class WorldModelEnv:
     def step(self, action: Union[int, np.ndarray, torch.LongTensor], should_predict_next_obs: bool = True) -> None:
         assert self.keys_values_wm is not None and self.num_observations_tokens is not None
 
-        num_passes = 1 + self.num_observations_tokens if should_predict_next_obs else 1
+        num_passes = 2 + self.num_observations_tokens if should_predict_next_obs else 1
 
         output_sequence, obs_tokens = [], []
 
@@ -71,7 +71,8 @@ class WorldModelEnv:
             outputs_wm = self.world_model(token, past_keys_values=self.keys_values_wm)
             output_sequence.append(outputs_wm.output_sequence)
 
-            if k == 0:
+            # if k == 0:
+            if self.world_model(token, past_keys_values=self.keys_values_wm).logits_rewards.shape[1] > 0:
                 reward = Categorical(logits=outputs_wm.logits_rewards).sample().float().cpu().numpy().reshape(-1) - 1   # (B,)
                 done = Categorical(logits=outputs_wm.logits_ends).sample().cpu().numpy().astype(bool).reshape(-1)       # (B,)
 
diff --git a/src/models/slicer.py b/src/models/slicer.py
index 6566271..49563e9 100644
--- a/src/models/slicer.py
+++ b/src/models/slicer.py
@@ -31,7 +31,8 @@ class Head(Slicer):
         self.head_module = head_module
 
     def forward(self, x: torch.Tensor, num_steps: int, prev_steps: int) -> torch.Tensor:
-        x_sliced = x[:, self.compute_slice(num_steps, prev_steps)]  # x is (B, T, E)
+        s = self.compute_slice(num_steps, prev_steps)
+        x_sliced = x[:, s]  # x is (B, T, E)
         return self.head_module(x_sliced)
 
 
diff --git a/src/models/transformer.py b/src/models/transformer.py
index 7c0c44c..ecf68ae 100644
--- a/src/models/transformer.py
+++ b/src/models/transformer.py
@@ -117,7 +117,7 @@ class Transformer(nn.Module):
 
     # @torch.cuda.amp.autocast(dtype=torch.bfloat16)
     def forward(self, sequences: torch.Tensor, past_keys_values: Optional[KeysValues] = None) -> torch.Tensor:
-        # assert past_keys_values is None or len(past_keys_values) == len(self.blocks)
+        assert past_keys_values is None or len(past_keys_values) == self.config.num_layers
         sequences = sequences.to(torch.bfloat16)
         outputs = self.model(
             inputs_embeds=sequences,
@@ -126,6 +126,14 @@ class Transformer(nn.Module):
         )
         x = outputs.logits.to(torch.float32)
         x = self.ln_f(x)
+        
+        # fake it, since it's used to keep track of steps
+        if past_keys_values is not None:
+            k_size = past_keys_values[0]._k_cache._cache.size()
+            k_size = (*k_size[:2], 1, *k_size[3:])
+            v_size = past_keys_values[0]._v_cache._cache.size()
+            v_size = (*v_size[:2], 1, *v_size[3:])
+            past_keys_values[0].update(torch.rand(k_size), torch.rand(v_size))
         return x
 
 

From e417790413bd143dd52ed3847c339f964eb96d40 Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Mon, 13 Nov 2023 08:11:13 +0800
Subject: [PATCH 09/30] fix kv_cache faking?

---
 src/models/transformer.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/src/models/transformer.py b/src/models/transformer.py
index ecf68ae..9b28849 100644
--- a/src/models/transformer.py
+++ b/src/models/transformer.py
@@ -130,10 +130,10 @@ class Transformer(nn.Module):
         # fake it, since it's used to keep track of steps
         if past_keys_values is not None:
             k_size = past_keys_values[0]._k_cache._cache.size()
-            k_size = (*k_size[:2], 1, *k_size[3:])
-            v_size = past_keys_values[0]._v_cache._cache.size()
-            v_size = (*v_size[:2], 1, *v_size[3:])
-            past_keys_values[0].update(torch.rand(k_size), torch.rand(v_size))
+            # k_size = (x.shape[0], x.shape[1], x.shape[1], 1)
+            # v_size = past_keys_values[0]._v_cache._cache.size()
+            v_size = (k_size[0], k_size[1], x.shape[1], k_size[3])
+            past_keys_values[0].update(torch.rand(v_size), torch.rand(v_size))
         return x
 
 

From e495db47e4fe1b6d604ee5f7f63878d6e11922a1 Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Mon, 13 Nov 2023 17:17:03 +0800
Subject: [PATCH 10/30] Fix num_passes calculation in WorldModelEnv.step()

---
 src/envs/world_model_env.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/envs/world_model_env.py b/src/envs/world_model_env.py
index 13af000..6188d2c 100644
--- a/src/envs/world_model_env.py
+++ b/src/envs/world_model_env.py
@@ -56,7 +56,7 @@ class WorldModelEnv:
     def step(self, action: Union[int, np.ndarray, torch.LongTensor], should_predict_next_obs: bool = True) -> None:
         assert self.keys_values_wm is not None and self.num_observations_tokens is not None
 
-        num_passes = 2 + self.num_observations_tokens if should_predict_next_obs else 1
+        num_passes = 1 + self.num_observations_tokens if should_predict_next_obs else 1
 
         output_sequence, obs_tokens = [], []
 
@@ -71,8 +71,8 @@ class WorldModelEnv:
             outputs_wm = self.world_model(token, past_keys_values=self.keys_values_wm)
             output_sequence.append(outputs_wm.output_sequence)
 
-            # if k == 0:
-            if self.world_model(token, past_keys_values=self.keys_values_wm).logits_rewards.shape[1] > 0:
+            # if outputs_wm.logits_rewards.shape[1] > 0:
+            if k == 0:
                 reward = Categorical(logits=outputs_wm.logits_rewards).sample().float().cpu().numpy().reshape(-1) - 1   # (B,)
                 done = Categorical(logits=outputs_wm.logits_ends).sample().cpu().numpy().astype(bool).reshape(-1)       # (B,)
 

From 106027a7f98a38ec7c70682f8542c8329edb3cea Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Tue, 14 Nov 2023 10:42:17 +0800
Subject: [PATCH 11/30] add 1b model, use contextlib,

---
 config/trainer.yaml             |  4 +--
 config/world_model/default.yaml |  2 +-
 research_journal.md             | 16 +++++++++++
 src/models/actor_critic.py      |  4 +++
 src/models/transformer.py       | 51 +++++++++++++++++++++++++++------
 src/utils.py                    |  4 ++-
 6 files changed, 68 insertions(+), 13 deletions(-)

diff --git a/config/trainer.yaml b/config/trainer.yaml
index 01af86e..81f5e88 100644
--- a/config/trainer.yaml
+++ b/config/trainer.yaml
@@ -60,14 +60,14 @@ training:
     start_after_epochs: 5
     steps_per_epoch: 200
   world_model:
-    batch_num_samples: 4
+    batch_num_samples: 8
     grad_acc_steps: 1
     max_grad_norm: 10.0
     weight_decay: 0.01
     start_after_epochs: 25
     steps_per_epoch: 200
   actor_critic:
-    batch_num_samples: 8
+    batch_num_samples: 32
     grad_acc_steps: 1
     max_grad_norm: 10.0
     start_after_epochs: 50
diff --git a/config/world_model/default.yaml b/config/world_model/default.yaml
index d03a227..cb9fcd3 100644
--- a/config/world_model/default.yaml
+++ b/config/world_model/default.yaml
@@ -4,7 +4,7 @@ max_blocks: 20
 attention: 'causal'
 num_layers: 10
 num_heads: 4
-embed_dim: 2560 # change this to whatever the embedding dimension is in your pretrained llm
+embed_dim: 2048 # change this to whatever the embedding dimension is in your pretrained llm 2048 for llama. 2560 for stablelm
 embed_pdrop: 0.1
 resid_pdrop: 0.1
 attn_pdrop: 0.1
diff --git a/research_journal.md b/research_journal.md
index 94d442f..bfd295d 100644
--- a/research_journal.md
+++ b/research_journal.md
@@ -147,3 +147,19 @@ ok even with a full run I get the error. I think it's a bug in the original repo
     RuntimeError: cannot reshape tensor of 0 elements into shape [8, 0, -1] because the unspecified dimension size -1 can be any value and is ambiguous
 
 Oh maybe it's because we don't keep track of KV cache, but it's actually used to track number of steps!!
+
+# 2023-11-13 20:11:51
+
+I go it working byt ut takes 30 seconds for one one actor critic batch, werird
+
+Experience collection (train_dataset): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:03<00:00, 60.45it/s]
+Training tokenizer: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:17<00:00, 11.26it/s]
+Training world_model: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [02:12<00:00,  1.51it/s]
+Training actor_critic:  82%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍                             | 165/200 [1:01:43<13:24, 22.99s/it]
+
+
+hm maybe it's just the face it has to backprop throguh the whole LLM :( damn... is there another way to train it? Daym. How many params did the original have?
+
+well running eval on the transformer brought it down from 100sec to 60, but it's still huge. 
+
+But then why is the model training fast? It makes not sense
diff --git a/src/models/actor_critic.py b/src/models/actor_critic.py
index c22e641..69dc3de 100644
--- a/src/models/actor_critic.py
+++ b/src/models/actor_critic.py
@@ -146,6 +146,10 @@ class ActorCritic(nn.Module):
 
             outputs_ac = self(obs)
             action_token = Categorical(logits=outputs_ac.logits_actions).sample()
+            
+            # TODO this is really slow, I guess we need grad? does it help to put it in eval? no
+            # wm_env.world_model.eval()
+            
             obs, reward, done, _ = wm_env.step(action_token, should_predict_next_obs=(k < horizon - 1))
 
             all_actions.append(action_token)
diff --git a/src/models/transformer.py b/src/models/transformer.py
index 9b28849..40b4430 100644
--- a/src/models/transformer.py
+++ b/src/models/transformer.py
@@ -5,7 +5,7 @@ Credits to https://github.com/karpathy/minGPT
 from dataclasses import dataclass
 import math
 from typing import Optional
-
+from contextlib import contextmanager
 from einops import rearrange
 import torch
 import torch.nn as nn
@@ -30,7 +30,9 @@ class TransformerConfig:
     resid_pdrop: float
     attn_pdrop: float
     
-    model_name: str = "stabilityai/stablelm-3b-4e1t"
+    # model_name: str = "stabilityai/stablelm-3b-4e1t"
+    # https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T
+    model_name: str = "PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T"
     dropout: float = 0
     rank: int = 32
     z_dim: int = 768
@@ -118,13 +120,14 @@ class Transformer(nn.Module):
     # @torch.cuda.amp.autocast(dtype=torch.bfloat16)
     def forward(self, sequences: torch.Tensor, past_keys_values: Optional[KeysValues] = None) -> torch.Tensor:
         assert past_keys_values is None or len(past_keys_values) == self.config.num_layers
-        sequences = sequences.to(torch.bfloat16)
-        outputs = self.model(
-            inputs_embeds=sequences,
-            return_dict=True,
-            output_hidden_states=True,
-        )
-        x = outputs.logits.to(torch.float32)
+        with set_adapter(self.model, "dynamics"), disable_causal_mask(), torch.cuda.amp.autocast(dtype=torch.bfloat16):
+            # sequences = sequences.to(torch.bfloat16)
+            outputs = self.model(
+                inputs_embeds=sequences,
+                return_dict=True,
+                output_hidden_states=True,
+            )
+        x = outputs.logits#.to(torch.float32)
         x = self.ln_f(x)
         
         # fake it, since it's used to keep track of steps
@@ -136,6 +139,8 @@ class Transformer(nn.Module):
             past_keys_values[0].update(torch.rand(v_size), torch.rand(v_size))
         return x
 
+        
+
 
 from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
 from peft import PeftModel, LoraConfig
@@ -179,3 +184,31 @@ def load_pretrained_model(config, device="cuda:0"):
     base_model_peft.add_adapter("dynamics", peft_config)
     print(base_model_peft.print_trainable_parameters())
     return base_model_peft
+
+@contextmanager
+def set_adapter(model, adapter_name):
+    old_adapter_name = model.active_adapter
+    try:
+        if adapter_name is not None:
+            model.set_adapter(adapter_name)
+            yield model
+        else:
+            with model.disable_adapter():
+                yield model
+    finally:
+        model.set_adapter(old_adapter_name)
+
+@contextmanager
+def disable_causal_mask():
+    import transformers.models.llama.modeling_llama as modeling
+
+    decoder_fn = modeling._make_causal_mask
+
+    def encoder_fn(*args, **kwargs):
+        return torch.zeros_like(decoder_fn(*args, **kwargs))
+
+    try:
+        modeling._make_causal_mask = encoder_fn
+        yield
+    finally:
+        modeling._make_causal_mask = decoder_fn
diff --git a/src/utils.py b/src/utils.py
index 1f134d4..ed8e31a 100644
--- a/src/utils.py
+++ b/src/utils.py
@@ -10,6 +10,8 @@ import torch.nn as nn
 
 from episode import Episode
 
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
+
 
 def configure_optimizer(model, learning_rate, weight_decay, *blacklist_module_names):
     """Credits to https://github.com/karpathy/minGPT"""
@@ -17,7 +19,7 @@ def configure_optimizer(model, learning_rate, weight_decay, *blacklist_module_na
     decay = set()
     no_decay = set()
     whitelist_weight_modules = (torch.nn.Linear, torch.nn.Conv1d)
-    blacklist_weight_modules = (torch.nn.LayerNorm, torch.nn.Embedding)
+    blacklist_weight_modules = tuple(ALL_LAYERNORM_LAYERS+[torch.nn.Embedding])
     for mn, m in model.named_modules():
         for pn, p in m.named_parameters():
             fpn = '%s.%s' % (mn, pn) if mn else pn  # full param name

From 1b6462991bd1316469c3ef01bc6f6f7e55cb10ea Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Thu, 16 Nov 2023 17:17:57 +0800
Subject: [PATCH 12/30] smaller horizon, smaller lstm. 100x faster
 actor_critic. does it learn though?

---
 config/trainer.yaml             |   4 ++--
 config/world_model/default.yaml |   2 +-
 img/2023-11-16-13-01-11.png     | Bin 0 -> 85598 bytes
 research_journal.md             |  36 ++++++++++++++++++++++++++++++++
 src/envs/world_model_env.py     |   3 +--
 src/models/actor_critic.py      |  13 ++++++------
 src/trainer.py                  |   1 +
 7 files changed, 48 insertions(+), 11 deletions(-)
 create mode 100644 img/2023-11-16-13-01-11.png

diff --git a/config/trainer.yaml b/config/trainer.yaml
index 81f5e88..f8022a0 100644
--- a/config/trainer.yaml
+++ b/config/trainer.yaml
@@ -54,7 +54,7 @@ training:
   should: True
   learning_rate: 0.0001
   tokenizer:
-    batch_num_samples: 32
+    batch_num_samples: 128
     grad_acc_steps: 1
     max_grad_norm: 10.0
     start_after_epochs: 5
@@ -71,7 +71,7 @@ training:
     grad_acc_steps: 1
     max_grad_norm: 10.0
     start_after_epochs: 50
-    steps_per_epoch: 200
+    steps_per_epoch: 20
     imagine_horizon: ${common.sequence_length}
     burn_in: 20
     gamma: 0.995
diff --git a/config/world_model/default.yaml b/config/world_model/default.yaml
index cb9fcd3..16769b7 100644
--- a/config/world_model/default.yaml
+++ b/config/world_model/default.yaml
@@ -1,6 +1,6 @@
 _target_: models.TransformerConfig
 tokens_per_block: 17
-max_blocks: 20
+max_blocks: 10
 attention: 'causal'
 num_layers: 10
 num_heads: 4
diff --git a/img/2023-11-16-13-01-11.png b/img/2023-11-16-13-01-11.png
new file mode 100644
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HcmV?d00001

diff --git a/research_journal.md b/research_journal.md
index bfd295d..de33a90 100644
--- a/research_journal.md
+++ b/research_journal.md
@@ -163,3 +163,39 @@ hm maybe it's just the face it has to backprop throguh the whole LLM :( damn...
 well running eval on the transformer brought it down from 100sec to 60, but it's still huge. 
 
 But then why is the model training fast? It makes not sense
+ 
+# 2023-11-16 12:54:48
+
+Why is agent so slow? Lets find out
+- look at diagram
+- look at train_agent
+  - to tokenizer.compute_loss is just tokenizer
+  - world_model.compute_loss user tokenizer with no grad
+  - actor_critic? takes an hour!!
+    - imagine (with grad?)
+      - x20 = horizon
+        - self(obs)
+        - WorldModelEnv.step this has no grad!
+          - transformer
+          - tokenizer with no grad
+    - compute_lambda_returns with no grad
+
+
+So changes:
+- the world model step always had no grad!
+- I just made the lstm smaller and the horizon smaller
+- from 1h to 3m. Reasonable.
+
+
+Experiment: 
+- try no grad on the model? ok it now takes 20 minutes to train... still slow
+with a smaller lstm and only 10 steos ut tajes 8 mins,
+
+![](img/2023-11-16-13-01-11.png)
+
+Experience collection (train_dataset): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:05<00:00, 36.11it/s]
+Training tokenizer: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:55<00:00,  3.61it/s]
+Training world_model: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [01:12<00:00,  2.76it/s]
+Training tokenizer: 55sec
+Training world_model 72 sec
+train actor_critic 3min. It looks like it scales with lstm size!
diff --git a/src/envs/world_model_env.py b/src/envs/world_model_env.py
index 6188d2c..73e3852 100644
--- a/src/envs/world_model_env.py
+++ b/src/envs/world_model_env.py
@@ -65,13 +65,12 @@ class WorldModelEnv:
 
         token = action.clone().detach() if isinstance(action, torch.Tensor) else torch.tensor(action, dtype=torch.long)
         token = token.reshape(-1, 1).to(self.device)  # (B, 1)
+        
 
         for k in range(num_passes):  # assumption that there is only one action token.
 
             outputs_wm = self.world_model(token, past_keys_values=self.keys_values_wm)
             output_sequence.append(outputs_wm.output_sequence)
-
-            # if outputs_wm.logits_rewards.shape[1] > 0:
             if k == 0:
                 reward = Categorical(logits=outputs_wm.logits_rewards).sample().float().cpu().numpy().reshape(-1) - 1   # (B,)
                 done = Categorical(logits=outputs_wm.logits_ends).sample().cpu().numpy().astype(bool).reshape(-1)       # (B,)
diff --git a/src/models/actor_critic.py b/src/models/actor_critic.py
index 69dc3de..0fff2b6 100644
--- a/src/models/actor_critic.py
+++ b/src/models/actor_critic.py
@@ -36,6 +36,7 @@ class ImagineOutput:
 class ActorCritic(nn.Module):
     def __init__(self, act_vocab_size, use_original_obs: bool = False) -> None:
         super().__init__()
+        shrink = 4
         self.use_original_obs = use_original_obs
         self.conv1 = nn.Conv2d(3, 32, 3, stride=1, padding=1)
         self.maxp1 = nn.MaxPool2d(2, 2)
@@ -43,15 +44,15 @@ class ActorCritic(nn.Module):
         self.maxp2 = nn.MaxPool2d(2, 2)
         self.conv3 = nn.Conv2d(32, 64, 3, stride=1, padding=1)
         self.maxp3 = nn.MaxPool2d(2, 2)
-        self.conv4 = nn.Conv2d(64, 64, 3, stride=1, padding=1)
+        self.conv4 = nn.Conv2d(64, 64//shrink, 3, stride=1, padding=1)
         self.maxp4 = nn.MaxPool2d(2, 2)
 
-        self.lstm_dim = 512
-        self.lstm = nn.LSTMCell(1024, self.lstm_dim)
+        self.lstm_dim = 64
+        self.lstm = nn.LSTMCell(1024//shrink, self.lstm_dim)
         self.hx, self.cx = None, None
 
-        self.critic_linear = nn.Linear(512, 1)
-        self.actor_linear = nn.Linear(512, act_vocab_size)
+        self.critic_linear = nn.Linear(self.lstm_dim, 1)
+        self.actor_linear = nn.Linear(self.lstm_dim, act_vocab_size)
 
     def __repr__(self) -> str:
         return "actor_critic"
@@ -85,7 +86,7 @@ class ActorCritic(nn.Module):
         x = F.relu(self.maxp2(self.conv2(x)))
         x = F.relu(self.maxp3(self.conv3(x)))
         x = F.relu(self.maxp4(self.conv4(x)))
-        x = torch.flatten(x, start_dim=1)
+        x = torch.flatten(x, start_dim=1) # [b=32, 64//shrink, 4, 4]
 
         if mask_padding is None:
             self.hx, self.cx = self.lstm(x, (self.hx, self.cx))
diff --git a/src/trainer.py b/src/trainer.py
index ec94572..8b53aa5 100644
--- a/src/trainer.py
+++ b/src/trainer.py
@@ -169,6 +169,7 @@ class Trainer:
             if max_grad_norm is not None:
                 torch.nn.utils.clip_grad_norm_(component.parameters(), max_grad_norm)
 
+ 
             optimizer.step()
 
         metrics = {f'{str(component)}/train/total_loss': loss_total_epoch, **intermediate_losses}

From 1af7aa74fa356f9bb438d9ca71d34d2a9f8cadf2 Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Fri, 17 Nov 2023 06:25:09 +0800
Subject: [PATCH 13/30] 3min epoch breakout

https://wandb.ai/wassname/iris/runs/1zjsneyu?workspace=user-wassname
---
 README.md                       | 13 ++++++
 config/tokenizer/default.yaml   |  6 +--
 config/trainer.yaml             |  4 +-
 config/world_model/default.yaml | 15 ++++---
 research_journal.md             | 24 +++++++++++
 src/models/actor_critic.py      | 13 +++---
 src/models/transformer.py       | 73 +--------------------------------
 7 files changed, 57 insertions(+), 91 deletions(-)

diff --git a/README.md b/README.md
index 2530571..617104b 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,16 @@
+
+A fork of IRIS where I use a pretrained LLM as the tranformer (with LoRa). 
+
+My hypothesis: Pretrained LLM's make good world models by including a lot of world information!
+
+details:
+- for speed and cost I use a small 1.5B model. But it would be interesting to try a 7B one
+- for speed I use a smaller actor critic than in IRIS
+- max_blocks 20->10
+- batch smaller because of my small machine
+- actor_critic.steps_per_epoch 200->20
+- world_model.batch_num_sampler; 64->8 because the forzen transformer uses lots of gpu ram
+
 # Transformers are Sample-Efficient World Models (IRIS)
 
 [Transformers are Sample-Efficient World Models](https://openreview.net/forum?id=vhFu1Acb0xb) <br>
diff --git a/config/tokenizer/default.yaml b/config/tokenizer/default.yaml
index 39fe6ae..e789557 100644
--- a/config/tokenizer/default.yaml
+++ b/config/tokenizer/default.yaml
@@ -1,14 +1,14 @@
 _target_: models.tokenizer.Tokenizer
 
-vocab_size: 512
-embed_dim: 512
+vocab_size: 2048
+embed_dim: 2048
 encoder:
   _target_: models.tokenizer.Encoder
   config:
     _target_: models.tokenizer.EncoderDecoderConfig
     resolution: 64
     in_channels: 3
-    z_channels: 512
+    z_channels: 2048
     ch: 64
     ch_mult: [1, 1, 1, 1, 1]
     num_res_blocks: 2
diff --git a/config/trainer.yaml b/config/trainer.yaml
index f8022a0..4adf8b1 100644
--- a/config/trainer.yaml
+++ b/config/trainer.yaml
@@ -60,7 +60,7 @@ training:
     start_after_epochs: 5
     steps_per_epoch: 200
   world_model:
-    batch_num_samples: 8
+    batch_num_samples: 8 # pretrained models use lots of 
     grad_acc_steps: 1
     max_grad_norm: 10.0
     weight_decay: 0.01
@@ -71,7 +71,7 @@ training:
     grad_acc_steps: 1
     max_grad_norm: 10.0
     start_after_epochs: 50
-    steps_per_epoch: 20
+    steps_per_epoch: 40
     imagine_horizon: ${common.sequence_length}
     burn_in: 20
     gamma: 0.995
diff --git a/config/world_model/default.yaml b/config/world_model/default.yaml
index 16769b7..22e8175 100644
--- a/config/world_model/default.yaml
+++ b/config/world_model/default.yaml
@@ -1,10 +1,9 @@
 _target_: models.TransformerConfig
+max_blocks: 10  # this is the rollout length when training policy
+num_layers: 1
+num_heads: 1
+embed_dim: 2048  # change this to whatever the embedding dimension is in your pretrained llm 2048 for llama. 2560 for stablelm
+dropout: 0.1
+model_name: "PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T"
+rank: 32
 tokens_per_block: 17
-max_blocks: 10
-attention: 'causal'
-num_layers: 10
-num_heads: 4
-embed_dim: 2048 # change this to whatever the embedding dimension is in your pretrained llm 2048 for llama. 2560 for stablelm
-embed_pdrop: 0.1
-resid_pdrop: 0.1
-attn_pdrop: 0.1
diff --git a/research_journal.md b/research_journal.md
index de33a90..5e2b17e 100644
--- a/research_journal.md
+++ b/research_journal.md
@@ -199,3 +199,27 @@ Training world_model: 100%|█████████████████
 Training tokenizer: 55sec
 Training world_model 72 sec
 train actor_critic 3min. It looks like it scales with lstm size!
+
+
+new changes 10mins
+lets try no lstm?
+Right now it will take 41 hours for on epoch lol
+
+
+
+ram during stages:
+- actor critic 20G/24
+
+
+how big does my actor critic need to be?
+- IRIS: large 512 lstm on 64,64,3 obs
+  - We ran our experiments with 8 Nvidia A100 40GB GPUs. With two Atari environments running on the same GPU, training takes around 7 days, resulting in an average of 3.5 days per environment.
+- twm: mlp 512
+
+
+How long to train?
+`600*10//6/24` = 41 days
+- 600 epochs * 10 minutes / 6 to get hours, 24 to get days
+
+
+
diff --git a/src/models/actor_critic.py b/src/models/actor_critic.py
index 0fff2b6..75b4261 100644
--- a/src/models/actor_critic.py
+++ b/src/models/actor_critic.py
@@ -36,18 +36,19 @@ class ImagineOutput:
 class ActorCritic(nn.Module):
     def __init__(self, act_vocab_size, use_original_obs: bool = False) -> None:
         super().__init__()
-        shrink = 4
+        shrink = 8
+        s = 2
         self.use_original_obs = use_original_obs
-        self.conv1 = nn.Conv2d(3, 32, 3, stride=1, padding=1)
+        self.conv1 = nn.Conv2d(3, 32//s, 3, stride=1, padding=1)
         self.maxp1 = nn.MaxPool2d(2, 2)
-        self.conv2 = nn.Conv2d(32, 32, 3, stride=1, padding=1)
+        self.conv2 = nn.Conv2d(32//s, 32//s, 3, stride=1, padding=1)
         self.maxp2 = nn.MaxPool2d(2, 2)
-        self.conv3 = nn.Conv2d(32, 64, 3, stride=1, padding=1)
+        self.conv3 = nn.Conv2d(32//s, 64//s, 3, stride=1, padding=1)
         self.maxp3 = nn.MaxPool2d(2, 2)
-        self.conv4 = nn.Conv2d(64, 64//shrink, 3, stride=1, padding=1)
+        self.conv4 = nn.Conv2d(64//s, 64//shrink, 3, stride=1, padding=1)
         self.maxp4 = nn.MaxPool2d(2, 2)
 
-        self.lstm_dim = 64
+        self.lstm_dim = 16
         self.lstm = nn.LSTMCell(1024//shrink, self.lstm_dim)
         self.hx, self.cx = None, None
 
diff --git a/src/models/transformer.py b/src/models/transformer.py
index 40b4430..86139c4 100644
--- a/src/models/transformer.py
+++ b/src/models/transformer.py
@@ -17,94 +17,23 @@ from .kv_caching import KeysValues, KVCache
 @dataclass
 class TransformerConfig:
 
-        
-    tokens_per_block: int
     max_blocks: int
-    attention: str
 
     num_layers: int
     num_heads: int
     embed_dim: int
-
-    embed_pdrop: float
-    resid_pdrop: float
-    attn_pdrop: float
+    tokens_per_block: int
     
     # model_name: str = "stabilityai/stablelm-3b-4e1t"
     # https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T
     model_name: str = "PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T"
     dropout: float = 0
     rank: int = 32
-    z_dim: int = 768
-    start_from: str = None
 
     @property
     def max_tokens(self):
         return self.tokens_per_block * self.max_blocks
 
-class Block(nn.Module):
-    def __init__(self, config: TransformerConfig) -> None:
-        super().__init__()
-        self.ln1 = nn.LayerNorm(config.embed_dim)
-        self.ln2 = nn.LayerNorm(config.embed_dim)
-        self.attn = SelfAttention(config)
-        self.mlp = nn.Sequential(
-            nn.Linear(config.embed_dim, 4 * config.embed_dim),
-            nn.GELU(),
-            nn.Linear(4 * config.embed_dim, config.embed_dim),
-            nn.Dropout(config.resid_pdrop),
-        )
-
-    def forward(self, x: torch.Tensor, past_keys_values: Optional[KeysValues] = None) -> torch.Tensor:
-        x_attn = self.attn(self.ln1(x), past_keys_values)
-        x = x + x_attn
-        x = x + self.mlp(self.ln2(x))
-        return x
-
-
-class SelfAttention(nn.Module):
-    def __init__(self, config: TransformerConfig) -> None:
-        super().__init__()
-        assert config.embed_dim % config.num_heads == 0
-        assert config.attention in ('causal', 'block_causal')
-        self.num_heads = config.num_heads
-        self.key = nn.Linear(config.embed_dim, config.embed_dim)
-        self.query = nn.Linear(config.embed_dim, config.embed_dim)
-        self.value = nn.Linear(config.embed_dim, config.embed_dim)
-        self.attn_drop = nn.Dropout(config.attn_pdrop)
-        self.resid_drop = nn.Dropout(config.resid_pdrop)
-        self.proj = nn.Linear(config.embed_dim, config.embed_dim)
-
-        causal_mask = torch.tril(torch.ones(config.max_tokens, config.max_tokens))
-        block_causal_mask = torch.max(causal_mask, torch.block_diag(*[torch.ones(config.tokens_per_block, config.tokens_per_block) for _ in range(config.max_blocks)]))
-        self.register_buffer('mask', causal_mask if config.attention == 'causal' else block_causal_mask)
-
-    def forward(self, x: torch.Tensor, kv_cache: Optional[KVCache] = None) -> torch.Tensor:
-        B, T, C = x.size()
-        if kv_cache is not None:
-            b, nh, L, c = kv_cache.shape
-            assert nh == self.num_heads and b == B and c * nh == C
-        else:
-            L = 0
-
-        q = self.query(x).view(B, T, self.num_heads, C // self.num_heads).transpose(1, 2)   # (B, nh, T, hs)
-        k = self.key(x).view(B, T, self.num_heads, C // self.num_heads).transpose(1, 2)     # (B, nh, T, hs)
-        v = self.value(x).view(B, T, self.num_heads, C // self.num_heads).transpose(1, 2)   # (B, nh, T, hs)
-
-        if kv_cache is not None:
-            kv_cache.update(k, v)
-            k, v = kv_cache.get()
-
-        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
-        att = att.masked_fill(self.mask[L:L + T, :L + T] == 0, float('-inf'))
-        att = F.softmax(att, dim=-1)
-        att = self.attn_drop(att)
-        y = att @ v
-        y = rearrange(y, 'b h t e -> b t (h e)')
-
-        y = self.resid_drop(self.proj(y))
-
-        return y
 
 class Transformer(nn.Module):
     def __init__(self, config: TransformerConfig) -> None:

From a778296e40b7b22f25f777574945570ea5d61f79 Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Fri, 17 Nov 2023 12:30:16 +0800
Subject: [PATCH 14/30] relative imports

---
 research_journal.md               |  9 +++++++++
 src/agent.py                      |  8 ++++----
 src/collector.py                  | 10 +++++-----
 src/dataset.py                    |  2 +-
 src/envs/__init__.py              |  2 +-
 src/envs/wrappers.py              | 32 ++++++++++++++++++++++++++++++-
 src/main.py                       |  4 +++-
 src/models/actor_critic.py        | 14 +++++++-------
 src/models/tokenizer/tokenizer.py |  4 ++--
 src/models/world_model.py         |  4 ++--
 src/trainer.py                    | 16 ++++++++--------
 src/utils.py                      |  2 +-
 12 files changed, 74 insertions(+), 33 deletions(-)

diff --git a/research_journal.md b/research_journal.md
index 5e2b17e..8bdf3fe 100644
--- a/research_journal.md
+++ b/research_journal.md
@@ -221,5 +221,14 @@ How long to train?
 `600*10//6/24` = 41 days
 - 600 epochs * 10 minutes / 6 to get hours, 24 to get days
 
+# 2023-11-17 07:59:44
 
+so I've got it working with  these times. But maybe it's too small
+
+Epoch 148 / 600
+
+Experience collection (train_dataset): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:10<00:00, 19.25it/s]
+Training tokenizer: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:59<00:00,  3.34it/s]
+Training world_model: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [01:15<00:00,  2.64it/s]
+Training actor_critic: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [03:20<00:00, 10.05s/it]
 
diff --git a/src/agent.py b/src/agent.py
index f5dd1d3..a8d943c 100644
--- a/src/agent.py
+++ b/src/agent.py
@@ -4,10 +4,10 @@ import torch
 from torch.distributions.categorical import Categorical
 import torch.nn as nn
 
-from models.actor_critic import ActorCritic
-from models.tokenizer import Tokenizer
-from models.world_model import WorldModel
-from utils import extract_state_dict
+from .models.actor_critic import ActorCritic
+from .models.tokenizer import Tokenizer
+from .models.world_model import WorldModel
+from .utils import extract_state_dict
 
 
 class Agent(nn.Module):
diff --git a/src/collector.py b/src/collector.py
index 85e0150..8c5947e 100644
--- a/src/collector.py
+++ b/src/collector.py
@@ -8,11 +8,11 @@ import torch
 from tqdm import tqdm
 import wandb
 
-from agent import Agent
-from dataset import EpisodesDataset
-from envs import SingleProcessEnv, MultiProcessEnv
-from episode import Episode
-from utils import EpisodeDirManager, RandomHeuristic
+from src.agent import Agent
+from src.dataset import EpisodesDataset
+from src.envs import SingleProcessEnv, MultiProcessEnv
+from src.episode import Episode
+from src.utils import EpisodeDirManager, RandomHeuristic
 
 
 class Collector:
diff --git a/src/dataset.py b/src/dataset.py
index 59d9f30..5cc2d6e 100644
--- a/src/dataset.py
+++ b/src/dataset.py
@@ -7,7 +7,7 @@ from typing import Dict, List, Optional, Tuple
 import psutil
 import torch
 
-from episode import Episode
+from src.episode import Episode
 
 Batch = Dict[str, torch.Tensor]
 
diff --git a/src/envs/__init__.py b/src/envs/__init__.py
index 5afe68b..4d85674 100644
--- a/src/envs/__init__.py
+++ b/src/envs/__init__.py
@@ -1,4 +1,4 @@
 from .multi_process_env import MultiProcessEnv
-from .wrappers import make_atari, ResizeObsWrapper
+from .wrappers import make_atari, make_crafter, make_env, ResizeObsWrapper
 from .single_process_env import SingleProcessEnv
 from .world_model_env import WorldModelEnv
diff --git a/src/envs/wrappers.py b/src/envs/wrappers.py
index eca6f88..4183483 100644
--- a/src/envs/wrappers.py
+++ b/src/envs/wrappers.py
@@ -7,11 +7,22 @@ from typing import Tuple
 import gym
 import numpy as np
 from PIL import Image
+import crafter
+
+
+def make_env(id, size=64, max_episode_steps=None, noop_max=30, frame_skip=4, done_on_life_loss=False, clip_reward=False):
+    if id.startswith('Crafter'):
+        return make_crafter(id, size=size, max_episode_steps=max_episode_steps, done_on_life_loss=done_on_life_loss)
+    if id.startswith('MiniHack'):
+        return make_minihack(size=size, max_episode_steps=max_episode_steps, done_on_life_loss=done_on_life_loss)
+    else:
+        return make_atari(id, size, max_episode_steps, noop_max, frame_skip, done_on_life_loss, clip_reward)
 
 
 def make_atari(id, size=64, max_episode_steps=None, noop_max=30, frame_skip=4, done_on_life_loss=False, clip_reward=False):
     env = gym.make(id)
-    assert 'NoFrameskip' in env.spec.id or 'Frameskip' not in env.spec
+    print(env.spec)
+    assert 'NoFrameskip' in env.spec.id or 'Frameskip' not in str(env.spec)
     env = ResizeObsWrapper(env, (size, size))
     if clip_reward:
         env = RewardClippingWrapper(env)
@@ -24,6 +35,25 @@ def make_atari(id, size=64, max_episode_steps=None, noop_max=30, frame_skip=4, d
         env = EpisodicLifeEnv(env)
     return env
 
+def make_crafter(id, size=64, max_episode_steps=None, done_on_life_loss=False):
+    # https://github.com/danijar/dreamerv2/blob/07d906e9c4322c6fc2cd6ed23e247ccd6b7c8c41/dreamerv2/common/envs.py#L242
+    # https://github.com/footoredo/torchbeast/blob/12939569cc46b6a8616e4c25b138d97248cc8581/torchbeast/atari_wrappers.py#L301
+    env = gym.make(id)
+    return env
+
+
+def make_minihack(id, size=64, max_episode_steps=None, noop_max=30, frame_skip=4, done_on_life_loss=False, clip_reward=False):
+    # https://github.com/facebookresearch/minihack/blob/47065748f04714c49ba5b52fb74d166228c7acc1/minihack/agent/common/envs/wrapper.py#L117
+    # https://github.com/roger-creus/SOFE/blob/5551a115a9c7e1d632cf6996bf5dcabde59cdcc5/e3b/minihack/torchbeast/src/utils.py#L110
+    env = gym.make(id,
+                   # https://minihack.readthedocs.io/en/latest/getting-started/observation_spaces.html
+                   observation_keys=("pixel_crop"),
+                #    obs_crop_h=9,
+                #    obs_crop_w=9,
+                    )
+    env = ResizeObsWrapper(env, (size, size))
+    return env
+
 
 class ResizeObsWrapper(gym.ObservationWrapper):
     def __init__(self, env: gym.Env, size: Tuple[int, int]) -> None:
diff --git a/src/main.py b/src/main.py
index be4177b..6a84b94 100644
--- a/src/main.py
+++ b/src/main.py
@@ -2,7 +2,9 @@ import hydra
 from omegaconf import DictConfig
 
 from trainer import Trainer
-
+from loguru import logger
+import sys
+logger.add(sys.stderr, format="{time} {level} {message}", filter="my_module", level="INFO")
 
 @hydra.main(config_path="../config", config_name="trainer")
 def main(cfg: DictConfig):
diff --git a/src/models/actor_critic.py b/src/models/actor_critic.py
index 75b4261..13eb51d 100644
--- a/src/models/actor_critic.py
+++ b/src/models/actor_critic.py
@@ -10,11 +10,11 @@ import torch.nn as nn
 import torch.nn.functional as F
 from tqdm import tqdm
 
-from dataset import Batch
-from envs.world_model_env import WorldModelEnv
-from models.tokenizer import Tokenizer
-from models.world_model import WorldModel
-from utils import compute_lambda_returns, LossWithIntermediateLosses
+from ..dataset import Batch
+from ..envs.world_model_env import WorldModelEnv
+from ..models.tokenizer import Tokenizer
+from ..models.world_model import WorldModel
+from ..utils import compute_lambda_returns, LossWithIntermediateLosses
 
 
 @dataclass
@@ -34,7 +34,7 @@ class ImagineOutput:
 
 
 class ActorCritic(nn.Module):
-    def __init__(self, act_vocab_size, use_original_obs: bool = False) -> None:
+    def __init__(self, act_vocab_size, use_original_obs: bool = False, lstm_dim = 16) -> None:
         super().__init__()
         shrink = 8
         s = 2
@@ -48,7 +48,7 @@ class ActorCritic(nn.Module):
         self.conv4 = nn.Conv2d(64//s, 64//shrink, 3, stride=1, padding=1)
         self.maxp4 = nn.MaxPool2d(2, 2)
 
-        self.lstm_dim = 16
+        self.lstm_dim = lstm_dim
         self.lstm = nn.LSTMCell(1024//shrink, self.lstm_dim)
         self.hx, self.cx = None, None
 
diff --git a/src/models/tokenizer/tokenizer.py b/src/models/tokenizer/tokenizer.py
index 6528fa4..1b8723d 100644
--- a/src/models/tokenizer/tokenizer.py
+++ b/src/models/tokenizer/tokenizer.py
@@ -9,10 +9,10 @@ from einops import rearrange
 import torch
 import torch.nn as nn
 
-from dataset import Batch
+from src.dataset import Batch
 from .lpips import LPIPS
 from .nets import Encoder, Decoder
-from utils import LossWithIntermediateLosses
+from src.utils import LossWithIntermediateLosses
 
 
 @dataclass
diff --git a/src/models/world_model.py b/src/models/world_model.py
index 194119e..349423c 100644
--- a/src/models/world_model.py
+++ b/src/models/world_model.py
@@ -6,12 +6,12 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from dataset import Batch
+from ..dataset import Batch
 from .kv_caching import KeysValues
 from .slicer import Embedder, Head
 from .tokenizer import Tokenizer
 from .transformer import Transformer, TransformerConfig
-from utils import init_weights, LossWithIntermediateLosses
+from ..utils import init_weights, LossWithIntermediateLosses
 
 
 @dataclass
diff --git a/src/trainer.py b/src/trainer.py
index 8b53aa5..5091fc3 100644
--- a/src/trainer.py
+++ b/src/trainer.py
@@ -14,14 +14,14 @@ import torch.nn as nn
 from tqdm import tqdm
 import wandb
 
-from agent import Agent
-from collector import Collector
-from envs import SingleProcessEnv, MultiProcessEnv
-from episode import Episode
-from make_reconstructions import make_reconstructions_from_batch
-from models.actor_critic import ActorCritic
-from models.world_model import WorldModel
-from utils import configure_optimizer, EpisodeDirManager, set_seed
+from src.agent import Agent
+from src.collector import Collector
+from src.envs import SingleProcessEnv, MultiProcessEnv
+from src.episode import Episode
+from src.make_reconstructions import make_reconstructions_from_batch
+from src.models.actor_critic import ActorCritic
+from src.models.world_model import WorldModel
+from src.utils import configure_optimizer, EpisodeDirManager, set_seed
 
 
 class Trainer:
diff --git a/src/utils.py b/src/utils.py
index ed8e31a..1aaf3b2 100644
--- a/src/utils.py
+++ b/src/utils.py
@@ -8,7 +8,7 @@ import numpy as np
 import torch
 import torch.nn as nn
 
-from episode import Episode
+from .episode import Episode
 
 from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
 

From 1dabd792516bfa0e033ae341294e8a46504ed8f6 Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Fri, 17 Nov 2023 18:17:17 +0800
Subject: [PATCH 15/30] debugging model size and speed

---
 .vscode/launch.json              |  27 +-
 config/actor_critic/default.yaml |   1 +
 config/datasets/default.yaml     |   4 +-
 config/env/default.yaml          |   4 +-
 config/tokenizer/default.yaml    |   8 +-
 config/trainer.yaml              |   6 +-
 config/world_model/default.yaml  |   2 +-
 justfile                         |  10 +
 notebooks/01_debug_models.ipynb  | 747 +++++++++++++++++++++++++++++++
 poetry.lock                      | 200 ++++++++-
 pyproject.toml                   |   2 +
 research_journal.md              |  82 ++++
 src/models/actor_critic.py       |   4 +-
 src/models/transformer.py        |   5 +-
 src/trainer.py                   |   1 +
 15 files changed, 1088 insertions(+), 15 deletions(-)
 create mode 100644 justfile
 create mode 100644 notebooks/01_debug_models.ipynb

diff --git a/.vscode/launch.json b/.vscode/launch.json
index 06bb888..9044a8c 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -4,6 +4,28 @@
     // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
     "version": "0.2.0",
     "configurations": [
+        {
+            "name": "test",
+            "type": "python",
+            "request": "launch",
+            "program": "${workspaceFolder}/src/main.py",
+            "console": "integratedTerminal",
+            "justMyCode": false,
+            "autoReload": {"enable": true,},
+            "env": {"WANDB_MODE":"disabled"},
+            "args": [
+                "'wandb.mode=disabled",
+                // "env.train.id=BreakoutNoFrameskip-v4",
+                "env.train.id=CrafterReward-v1",
+                // # make it start early
+                "training.tokenizer.start_after_epochs=1",
+                "training.world_model.start_after_epochs=1",
+                "training.actor_critic.start_after_epochs=1",
+                "training.tokenizer.steps_per_epoch=10",
+                "training.world_model.steps_per_epoch=10",
+                "training.actor_critic.steps_per_epoch=10",
+            ]
+        },
         {
             "name": "main",
             "type": "python",
@@ -14,11 +36,14 @@
             "autoReload": {"enable": true,},
             "env": {"WANDB_MODE":"disabled"},
             "args": [
-                "env.train.id=BreakoutNoFrameskip-v4",
+                "'wandb.mode=disabled",
+                // "env.train.id=BreakoutNoFrameskip-v4",
+                "env.train.id=CrafterReward-v1",
                 // # make it start early
                 "training.tokenizer.start_after_epochs=1",
                 "training.world_model.start_after_epochs=1",
                 "training.actor_critic.start_after_epochs=1",
+                
                 "training.tokenizer.steps_per_epoch=10",
                 "training.world_model.steps_per_epoch=10",
                 "training.actor_critic.steps_per_epoch=10",
diff --git a/config/actor_critic/default.yaml b/config/actor_critic/default.yaml
index e4e2eee..69b1ea8 100644
--- a/config/actor_critic/default.yaml
+++ b/config/actor_critic/default.yaml
@@ -1 +1,2 @@
 use_original_obs: False
+lstm_dim: 512
diff --git a/config/datasets/default.yaml b/config/datasets/default.yaml
index c959c98..de8d15a 100644
--- a/config/datasets/default.yaml
+++ b/config/datasets/default.yaml
@@ -1,8 +1,8 @@
 train:
-  _target_: dataset.EpisodesDatasetRamMonitoring
+  _target_: src.dataset.EpisodesDatasetRamMonitoring
   max_ram_usage: 30G
   name: train_dataset
 test:
-  _target_: dataset.EpisodesDataset
+  _target_: src.dataset.EpisodesDataset
   max_num_episodes: null
   name: test_dataset
diff --git a/config/env/default.yaml b/config/env/default.yaml
index f6b3b89..dc885ba 100644
--- a/config/env/default.yaml
+++ b/config/env/default.yaml
@@ -1,5 +1,5 @@
 train:
-  _target_: envs.make_atari
+  _target_: src.envs.make_env
   id: null
   size: 64
   max_episode_steps: 20000
@@ -18,4 +18,4 @@ test:
   done_on_life_loss: False
   clip_reward: False
 
-keymap: atari/${.train.id}
\ No newline at end of file
+keymap: atari/${.train.id}
diff --git a/config/tokenizer/default.yaml b/config/tokenizer/default.yaml
index e789557..64e2bac 100644
--- a/config/tokenizer/default.yaml
+++ b/config/tokenizer/default.yaml
@@ -1,11 +1,11 @@
-_target_: models.tokenizer.Tokenizer
+_target_: src.models.tokenizer.Tokenizer
 
 vocab_size: 2048
 embed_dim: 2048
 encoder:
-  _target_: models.tokenizer.Encoder
+  _target_: src.models.tokenizer.Encoder
   config:
-    _target_: models.tokenizer.EncoderDecoderConfig
+    _target_: src.models.tokenizer.EncoderDecoderConfig
     resolution: 64
     in_channels: 3
     z_channels: 2048
@@ -16,5 +16,5 @@ encoder:
     out_ch: 3
     dropout: 0.0
 decoder:
-  _target_: models.tokenizer.Decoder
+  _target_: src.models.tokenizer.Decoder
   config: ${..encoder.config}
diff --git a/config/trainer.yaml b/config/trainer.yaml
index 4adf8b1..d1f92a6 100644
--- a/config/trainer.yaml
+++ b/config/trainer.yaml
@@ -67,7 +67,7 @@ training:
     start_after_epochs: 25
     steps_per_epoch: 200
   actor_critic:
-    batch_num_samples: 32
+    batch_num_samples: 16
     grad_acc_steps: 1
     max_grad_norm: 10.0
     start_after_epochs: 50
@@ -92,3 +92,7 @@ evaluation:
     num_episodes_to_save: ${training.actor_critic.batch_num_samples}
     horizon: ${training.actor_critic.imagine_horizon}
     start_after_epochs: ${training.actor_critic.start_after_epochs}
+
+hydra:
+  job:
+    chdir: True
diff --git a/config/world_model/default.yaml b/config/world_model/default.yaml
index 22e8175..08ede7e 100644
--- a/config/world_model/default.yaml
+++ b/config/world_model/default.yaml
@@ -1,4 +1,4 @@
-_target_: models.TransformerConfig
+_target_: src.models.TransformerConfig
 max_blocks: 10  # this is the rollout length when training policy
 num_layers: 1
 num_heads: 1
diff --git a/justfile b/justfile
new file mode 100644
index 0000000..1f26170
--- /dev/null
+++ b/justfile
@@ -0,0 +1,10 @@
+
+breakout:
+    python src/main.py env.train.id=BreakoutNoFrameskip-v4 
+
+crafter:
+    python src/main.py env.train.id=CrafterReward-v1
+
+minihack:
+    python src/main.py env.train.id=MiniHack-River-v0
+
diff --git a/notebooks/01_debug_models.ipynb b/notebooks/01_debug_models.ipynb
new file mode 100644
index 0000000..8c14e02
--- /dev/null
+++ b/notebooks/01_debug_models.ipynb
@@ -0,0 +1,747 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# autoreload import your package\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "import gym\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.style.use('ggplot')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Debug model components\n",
+    "\n",
+    "### Using trainer? :poop:\n",
+    "\n",
+    "Hyrda is really annoying\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'wandb': {'mode': 'disabled', 'project': 'iris', 'entity': None, 'name': None, 'group': None, 'tags': None, 'notes': None}, 'initialization': {'path_to_checkpoint': None, 'load_tokenizer': False, 'load_world_model': False, 'load_actor_critic': False}, 'common': {'epochs': 600, 'device': 'cuda:0', 'do_checkpoint': False, 'seed': 0, 'sequence_length': '${world_model.max_blocks}', 'resume': True}, 'collection': {'train': {'num_envs': 1, 'stop_after_epochs': 500, 'num_episodes_to_save': 10, 'config': {'epsilon': 0.01, 'should_sample': True, 'temperature': 1.0, 'num_steps': 200, 'burn_in': '${training.actor_critic.burn_in}'}}, 'test': {'num_envs': 8, 'num_episodes_to_save': '${collection.train.num_episodes_to_save}', 'config': {'epsilon': 0.0, 'should_sample': True, 'temperature': 0.5, 'num_episodes': 16, 'burn_in': '${training.actor_critic.burn_in}'}}}, 'training': {'should': True, 'learning_rate': 0.0001, 'tokenizer': {'batch_num_samples': 128, 'grad_acc_steps': 1, 'max_grad_norm': 10.0, 'start_after_epochs': 1, 'steps_per_epoch': 10}, 'world_model': {'batch_num_samples': 4, 'grad_acc_steps': 1, 'max_grad_norm': 10.0, 'weight_decay': 0.01, 'start_after_epochs': 1, 'steps_per_epoch': 10}, 'actor_critic': {'batch_num_samples': 4, 'grad_acc_steps': 1, 'max_grad_norm': 10.0, 'start_after_epochs': 1, 'steps_per_epoch': 10, 'imagine_horizon': '${common.sequence_length}', 'burn_in': 20, 'gamma': 0.995, 'lambda_': 0.95, 'entropy_weight': 0.001}}, 'evaluation': {'should': True, 'every': 5, 'tokenizer': {'batch_num_samples': '${training.tokenizer.batch_num_samples}', 'start_after_epochs': '${training.tokenizer.start_after_epochs}', 'save_reconstructions': True}, 'world_model': {'batch_num_samples': '${training.world_model.batch_num_samples}', 'start_after_epochs': '${training.world_model.start_after_epochs}'}, 'actor_critic': {'num_episodes_to_save': '${training.actor_critic.batch_num_samples}', 'horizon': '${training.actor_critic.imagine_horizon}', 'start_after_epochs': '${training.actor_critic.start_after_epochs}'}}, 'tokenizer': {'_target_': 'src.models.tokenizer.Tokenizer', 'vocab_size': 2048, 'embed_dim': 2048, 'encoder': {'_target_': 'src.models.tokenizer.Encoder', 'config': {'_target_': 'src.models.tokenizer.EncoderDecoderConfig', 'resolution': 64, 'in_channels': 3, 'z_channels': 2048, 'ch': 64, 'ch_mult': [1, 1, 1, 1, 1], 'num_res_blocks': 2, 'attn_resolutions': [8, 16], 'out_ch': 3, 'dropout': 0.0}}, 'decoder': {'_target_': 'src.models.tokenizer.Decoder', 'config': '${..encoder.config}'}}, 'world_model': {'_target_': 'src.models.TransformerConfig', 'max_blocks': 10, 'num_layers': 1, 'num_heads': 1, 'embed_dim': 2048, 'dropout': 0.1, 'model_name': 'PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T', 'rank': 32, 'tokens_per_block': 17}, 'actor_critic': {'use_original_obs': False, 'lstm_dim': 512}, 'env': {'train': {'_target_': 'src.envs.make_env', 'id': 'CrafterReward-v1', 'size': 64, 'max_episode_steps': 20000, 'noop_max': 30, 'frame_skip': 4, 'done_on_life_loss': True, 'clip_reward': False}, 'test': {'_target_': '${..train._target_}', 'id': '${..train.id}', 'size': '${..train.size}', 'max_episode_steps': 108000, 'noop_max': 1, 'frame_skip': '${..train.frame_skip}', 'done_on_life_loss': False, 'clip_reward': False}, 'keymap': 'atari/${.train.id}'}, 'datasets': {'train': {'_target_': 'src.dataset.EpisodesDatasetRamMonitoring', 'max_ram_usage': '30G', 'name': 'train_dataset'}, 'test': {'_target_': 'src.dataset.EpisodesDataset', 'max_num_episodes': None, 'name': 'test_dataset'}}}\n",
+      "Tokenizer : shape of latent is (2048, 4, 4).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+      "  warnings.warn(\n",
+      "/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n",
+      "  warnings.warn(msg)\n",
+      "Using pad_token, but it is not set yet.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trainable params: 50,462,720 || all params: 1,150,511,104 || trainable%: 4.386113252149889\n",
+      "None\n",
+      "32314243 parameters in agent.tokenizer\n",
+      "752979973 parameters in agent.world_model\n",
+      "3224626 parameters in agent.actor_critic\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "import os\n",
+    "os.environ['WANDB_MODE'] = 'disabled'\n",
+    "\n",
+    "import hydra\n",
+    "from hydra import initialize, initialize_config_module, initialize_config_dir, compose\n",
+    "from omegaconf import OmegaConf\n",
+    "\n",
+    "from pathlib import Path\n",
+    "from datetime import datetime\n",
+    "\n",
+    "from src.trainer import Trainer\n",
+    "\n",
+    "\n",
+    "class Trainer2(Trainer):\n",
+    "    \n",
+    "    def load_checkpoint(self, *args, **kwargs):\n",
+    "        pass\n",
+    "\n",
+    "\n",
+    "\n",
+    "ts = datetime.now().strftime(\"%Y-%m-%d/%H-%M-%S\")\n",
+    "run_dir = Path(f\"..outputs/{ts}\").absolute()\n",
+    "run_dir.mkdir(parents=True, exist_ok=True)\n",
+    "abs_config_dir=os.path.abspath(\"../config\")\n",
+    "os.chdir(run_dir)\n",
+    "# with initialize_config_dir(version_base=None, config_dir=abs_config_dir):\n",
+    "with initialize(version_base=None, config_path=\"../config\"):\n",
+    "    cfg = compose(config_name='trainer', overrides=[\n",
+    "        f'hydra.run.dir={run_dir}',\n",
+    "        # f\"initialization.path_to_checkpoint={str(path_to_checkpoint.absolute())}\",\n",
+    "        'wandb.mode=disabled',\n",
+    "        \"env.train.id=CrafterReward-v1\",\n",
+    "        \"training.tokenizer.start_after_epochs=1\",\n",
+    "        \"training.world_model.start_after_epochs=1\",\n",
+    "        \"training.actor_critic.start_after_epochs=1\",\n",
+    "        \"training.tokenizer.steps_per_epoch=10\",\n",
+    "        \"training.world_model.steps_per_epoch=10\",\n",
+    "        \"training.actor_critic.steps_per_epoch=10\",\n",
+    "        \"common.do_checkpoint=False\",\n",
+    "        \"common.resume=True\",\n",
+    "        \"training.world_model.batch_num_samples=4\",\n",
+    "        \"training.actor_critic.batch_num_samples=4\",\n",
+    "        ])\n",
+    "    print(cfg)\n",
+    "\n",
+    "    with run_dir:\n",
+    "        Path('media/episodes/train').mkdir(parents=True, exist_ok=True)\n",
+    "        trainer = Trainer2(cfg)\n",
+    "    trainer\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Trainer train_agent\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Experience collection (train_dataset): 100%|██████████| 200/200 [00:03<00:00, 58.00it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'train_dataset/episode_length': 183,\n",
+       "  'train_dataset/episode_return': tensor(0.1000),\n",
+       "  'train_dataset/episode_num': 0,\n",
+       "  'train_dataset/action_histogram': <wandb.sdk.data_types.histogram.Histogram at 0x7f97f7373eb0>},\n",
+       " {'train_dataset/#episodes': 2,\n",
+       "  'train_dataset/#steps': 200,\n",
+       "  'train_dataset/return': 0.100000024}]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "self=trainer\n",
+    "epoch = 52\n",
+    "\n",
+    "# get out first exp\n",
+    "self.train_collector.collect(self.agent, epoch, **self.cfg.collection.train.config)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "self.agent.train()\n",
+    "self.agent.zero_grad()\n",
+    "\n",
+    "metrics_tokenizer, metrics_world_model, metrics_actor_critic = {}, {}, {}\n",
+    "\n",
+    "cfg_tokenizer = self.cfg.training.tokenizer\n",
+    "cfg_world_model = self.cfg.training.world_model\n",
+    "cfg_actor_critic = self.cfg.training.actor_critic\n",
+    "\n",
+    "# if epoch > cfg_tokenizer.start_after_epochs:\n",
+    "#     metrics_tokenizer = self.train_component(self.agent.tokenizer, self.optimizer_tokenizer, sequence_length=1, sample_from_start=True, **cfg_tokenizer)\n",
+    "# self.agent.tokenizer.eval()\n",
+    "\n",
+    "# if epoch > cfg_world_model.start_after_epochs:\n",
+    "#     metrics_world_model = self.train_component(self.agent.world_model, self.optimizer_world_model, sequence_length=self.cfg.common.sequence_length, sample_from_start=True, tokenizer=self.agent.tokenizer, **cfg_world_model)\n",
+    "# self.agent.world_model.eval()\n",
+    "\n",
+    "# if epoch > cfg_actor_critic.start_after_epochs:\n",
+    "#     metrics_actor_critic = self.train_component(self.agent.actor_critic, self.optimizer_actor_critic, sequence_length=1 + self.cfg.training.actor_critic.burn_in, sample_from_start=False, tokenizer=self.agent.tokenizer, world_model=self.agent.world_model, **cfg_actor_critic)\n",
+    "# self.agent.actor_critic.eval()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from torchinfo import summary\n",
+    "import torch\n",
+    "from einops import rearrange\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Directly benchmark models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenizer = self.agent.tokenizer\n",
+    "world_model = self.agent.world_model\n",
+    "actor_critic = self.agent.actor_critic\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "batch_num_samples = cfg.training.world_model.batch_num_samples\n",
+    "sequence_length = cfg.common.sequence_length\n",
+    "sample_from_start = False\n",
+    "# train_dataset = instantiate(cfg.datasets.train)\n",
+    "batch_num_samples\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch = self.train_dataset.sample_batch(batch_num_samples, sequence_length, sample_from_start)\n",
+    "batch = {k: v.to(self.device) for k, v in batch.items()}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 190 ms, sys: 4.87 ms, total: 194 ms\n",
+      "Wall time: 195 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<src.utils.LossWithIntermediateLosses at 0x7f97ed202a30>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "self.agent.world_model.compute_loss(batch,  tokenizer=self.agent.tokenizer)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 9.35 s, sys: 17.3 ms, total: 9.37 s\n",
+      "Wall time: 9.37 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<src.utils.LossWithIntermediateLosses at 0x7f97f274beb0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# TODO: why is this so slow?\n",
+    "cfg_actor_critic = self.cfg.training.actor_critic\n",
+    "self.agent.actor_critic.compute_loss(batch,  tokenizer=self.agent.tokenizer, world_model=self.agent.world_model, **cfg_actor_critic)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 9.11 s, sys: 15.5 ms, total: 9.13 s\n",
+      "Wall time: 9.13 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# is this the slow part... yes. damn\n",
+    "actor_critic.imagine(batch, tokenizer, world_model, horizon=10);\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # takes 0.1 s, fast\n",
+    "# wm_env = WorldModelEnv(tokenizer, world_model, device)\n",
+    "# wm_env\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([4, 3, 64, 64])\n",
+      "CPU times: user 105 ms, sys: 207 µs, total: 105 ms\n",
+      "Wall time: 105 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# this takes 0.1 seconds and is run 10+ time. So 1 second. Hmm\n",
+    "from src.envs.world_model_env import WorldModelEnv, Categorical\n",
+    "initial_observations = batch['observations']\n",
+    "\n",
+    "# get the right obs\n",
+    "wm_env = WorldModelEnv(self.agent.tokenizer, self.agent.world_model, self.device)\n",
+    "obs = wm_env.reset_from_initial_observations(initial_observations[:, -1])\n",
+    "print(obs.shape)\n",
+    "\n",
+    "\n",
+    "# make sure hidden states are right\n",
+    "self.agent.actor_critic.reset(obs.shape[0])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gc\n",
+    "gc.collect()\n",
+    "torch.cuda.empty_cache()\n",
+    "# obs\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.6 ms, sys: 309 µs, total: 1.9 ms\n",
+      "Wall time: 1.72 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# 700us\n",
+    "# fast, executed 10+ times\n",
+    "outputs_ac = actor_critic(obs)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([4, 1, 17])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outputs_ac.logits_actions.shape\n",
+    "# action_token.shape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %%timeit\n",
+    "# slow! takes 1s, executed 10+ times this is the culprit, not the lstm. hmm\n",
+    "k=3\n",
+    "horizon = 6\n",
+    "action_token = Categorical(logits=outputs_ac.logits_actions).sample()\n",
+    "obs, reward, done, _ = wm_env.step(action_token, should_predict_next_obs=(k < horizon - 1))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "66.5 ms ± 1.53 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "# 62ms\n",
+    "# this is the slow part again. no grad and eval don't hepl\n",
+    "outputs_wm = world_model(action_token, past_keys_values=wm_env.keys_values_wm)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "num_steps=1\n",
+    "prev_steps=0\n",
+    "sequences = world_model.embedder(action_token, num_steps, prev_steps) + world_model.pos_emb(prev_steps + torch.arange(num_steps, device=action_token.device))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/notebooks/01_debug_models.ipynb Cell 24\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/notebooks/01_debug_models.ipynb#X64sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m get_ipython()\u001b[39m.\u001b[39;49mrun_cell_magic(\u001b[39m'\u001b[39;49m\u001b[39mtimeit\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39m# ofc it\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39ms the transformer that\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39ms slow. I guess we just call it was more than during training\u001b[39;49m\u001b[39m\\n\u001b[39;49;00m\u001b[39mpast_keys_values = wm_env.keys_values_wm\u001b[39;49m\u001b[39m\\n\u001b[39;49;00m\u001b[39mx = world_model.transformer(sequences, past_keys_values)\u001b[39;49m\u001b[39m\\n\u001b[39;49;00m\u001b[39m\"\u001b[39;49m)\n",
+      "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2515\u001b[0m, in \u001b[0;36mInteractiveShell.run_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2513\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbuiltin_trap:\n\u001b[1;32m   2514\u001b[0m     args \u001b[39m=\u001b[39m (magic_arg_s, cell)\n\u001b[0;32m-> 2515\u001b[0m     result \u001b[39m=\u001b[39m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   2517\u001b[0m \u001b[39m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m   2518\u001b[0m \u001b[39m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m   2519\u001b[0m \u001b[39m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m   2520\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(fn, magic\u001b[39m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[39mFalse\u001b[39;00m):\n",
+      "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/IPython/core/magics/execution.py:1189\u001b[0m, in \u001b[0;36mExecutionMagics.timeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1186\u001b[0m         \u001b[39mif\u001b[39;00m time_number \u001b[39m>\u001b[39m\u001b[39m=\u001b[39m \u001b[39m0.2\u001b[39m:\n\u001b[1;32m   1187\u001b[0m             \u001b[39mbreak\u001b[39;00m\n\u001b[0;32m-> 1189\u001b[0m all_runs \u001b[39m=\u001b[39m timer\u001b[39m.\u001b[39;49mrepeat(repeat, number)\n\u001b[1;32m   1190\u001b[0m best \u001b[39m=\u001b[39m \u001b[39mmin\u001b[39m(all_runs) \u001b[39m/\u001b[39m number\n\u001b[1;32m   1191\u001b[0m worst \u001b[39m=\u001b[39m \u001b[39mmax\u001b[39m(all_runs) \u001b[39m/\u001b[39m number\n",
+      "File \u001b[0;32m~/miniforge3/lib/python3.9/timeit.py:205\u001b[0m, in \u001b[0;36mTimer.repeat\u001b[0;34m(self, repeat, number)\u001b[0m\n\u001b[1;32m    203\u001b[0m r \u001b[39m=\u001b[39m []\n\u001b[1;32m    204\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(repeat):\n\u001b[0;32m--> 205\u001b[0m     t \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtimeit(number)\n\u001b[1;32m    206\u001b[0m     r\u001b[39m.\u001b[39mappend(t)\n\u001b[1;32m    207\u001b[0m \u001b[39mreturn\u001b[39;00m r\n",
+      "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/IPython/core/magics/execution.py:173\u001b[0m, in \u001b[0;36mTimer.timeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m    171\u001b[0m gc\u001b[39m.\u001b[39mdisable()\n\u001b[1;32m    172\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 173\u001b[0m     timing \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49minner(it, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtimer)\n\u001b[1;32m    174\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m    175\u001b[0m     \u001b[39mif\u001b[39;00m gcold:\n",
+      "File \u001b[0;32m<magic-timeit>:3\u001b[0m, in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
+      "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
+      "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/.venv/lib/python3.9/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
+      "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/src/models/transformer.py:69\u001b[0m, in \u001b[0;36mTransformer.forward\u001b[0;34m(self, sequences, past_keys_values)\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[39m# k_size = (x.shape[0], x.shape[1], x.shape[1], 1)\u001b[39;00m\n\u001b[1;32m     67\u001b[0m     \u001b[39m# v_size = past_keys_values[0]._v_cache._cache.size()\u001b[39;00m\n\u001b[1;32m     68\u001b[0m     v_size \u001b[39m=\u001b[39m (k_size[\u001b[39m0\u001b[39m], k_size[\u001b[39m1\u001b[39m], x\u001b[39m.\u001b[39mshape[\u001b[39m1\u001b[39m], k_size[\u001b[39m3\u001b[39m])\n\u001b[0;32m---> 69\u001b[0m     past_keys_values[\u001b[39m0\u001b[39;49m]\u001b[39m.\u001b[39;49mupdate(torch\u001b[39m.\u001b[39;49mrand(v_size), torch\u001b[39m.\u001b[39;49mrand(v_size))\n\u001b[1;32m     70\u001b[0m \u001b[39mreturn\u001b[39;00m x\n",
+      "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/src/models/kv_caching.py:59\u001b[0m, in \u001b[0;36mKVCache.update\u001b[0;34m(self, k, v)\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mupdate\u001b[39m(\u001b[39mself\u001b[39m, k: torch\u001b[39m.\u001b[39mTensor, v: torch\u001b[39m.\u001b[39mTensor):\n\u001b[0;32m---> 59\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_k_cache\u001b[39m.\u001b[39;49mupdate(k)\n\u001b[1;32m     60\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_v_cache\u001b[39m.\u001b[39mupdate(v)\n",
+      "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/worldmodels/iris_bigvae/src/models/kv_caching.py:33\u001b[0m, in \u001b[0;36mCache.update\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mupdate\u001b[39m(\u001b[39mself\u001b[39m, x: torch\u001b[39m.\u001b[39mTensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m     32\u001b[0m     \u001b[39massert\u001b[39;00m (x\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_cache\u001b[39m.\u001b[39mndim) \u001b[39mand\u001b[39;00m \u001b[39mall\u001b[39m([x\u001b[39m.\u001b[39msize(i) \u001b[39m==\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_cache\u001b[39m.\u001b[39msize(i) \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m (\u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m3\u001b[39m)])\n\u001b[0;32m---> 33\u001b[0m     \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_size \u001b[39m+\u001b[39m x\u001b[39m.\u001b[39msize(\u001b[39m2\u001b[39m) \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_cache\u001b[39m.\u001b[39mshape[\u001b[39m2\u001b[39m]\n\u001b[1;32m     34\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_cache \u001b[39m=\u001b[39m AssignWithoutInplaceCheck\u001b[39m.\u001b[39mapply(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_cache, x, \u001b[39m2\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_size, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_size \u001b[39m+\u001b[39m x\u001b[39m.\u001b[39msize(\u001b[39m2\u001b[39m))\n\u001b[1;32m     35\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_size \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m x\u001b[39m.\u001b[39msize(\u001b[39m2\u001b[39m)\n",
+      "\u001b[0;31mAssertionError\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "# ofc it's the transformer that's slow. I guess we just call it was more than during training\n",
+    "past_keys_values = wm_env.keys_values_wm\n",
+    "x = world_model.transformer(sequences, past_keys_values)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# past_keys_values = wm_env.keys_values_wm\n",
+    "# x = world_model.transformer(sequences, past_keys_values)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%timeit\n",
+    "# ofc it's the transformer that's slow. I guess we just call it was more than during training\n",
+    "past_keys_values = wm_env.keys_values_wm\n",
+    "x = world_model.transformer(sequences, past_keys_values)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logits_observations = world_model.head_observations(x, num_steps=num_steps, prev_steps=prev_steps)\n",
+    "logits_rewards = world_model.head_rewards(x, num_steps=num_steps, prev_steps=prev_steps)\n",
+    "logits_ends = world_model.head_ends(x, num_steps=num_steps, prev_steps=prev_steps)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Torchinfo model sizes\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observations = self.agent.tokenizer.preprocess_input(rearrange(batch['observations'], 'b t c h w -> (b t) c h w'))\n",
+    "# z, z_quantized, reconstructions = self.agent.tokenizer(observations, should_preprocess=False, should_postprocess=False)\n",
+    "summary(self.agent.tokenizer, input_data=observations)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "    obs_tokens = self.agent.tokenizer.encode(batch['observations'], should_preprocess=True).tokens  # (BL, K)\n",
+    "\n",
+    "act_tokens = rearrange(batch['actions'], 'b l -> b l 1')\n",
+    "tokens = rearrange(torch.cat((obs_tokens, act_tokens), dim=2), 'b l k1 -> b (l k1)')  # \n",
+    "\n",
+    "summary(self.agent.world_model, input_data=tokens)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from src.envs.world_model_env import WorldModelEnv\n",
+    "initial_observations = batch['observations']\n",
+    "\n",
+    "# get the right obs\n",
+    "wm_env = WorldModelEnv(self.agent.tokenizer, self.agent.world_model, self.device)\n",
+    "obs = wm_env.reset_from_initial_observations(initial_observations[:, -1])\n",
+    "obs.shape\n",
+    "\n",
+    "\n",
+    "# make sure hidden states are right\n",
+    "self.agent.actor_critic.reset(obs.shape[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "from torchinfo import summary\n",
+    "summary(self.agent.actor_critic, input_data=obs)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Debug env\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import minihack\n",
+    "env = gym.make(\"MiniHack-River-v0\", observation_keys=(\"pixel_crop\", \"pixel\", 'blstats', 'message'))\n",
+    "env.reset() # each reset generates a new environment instance\n",
+    "obs, reward, end, info = env.step(1)  # move agent '@' north\n",
+    "print(obs['pixel_crop'].shape)\n",
+    "plt.imshow(obs['pixel_crop'])\n",
+    "plt.show()\n",
+    "\n",
+    "print(obs['pixel'].shape)\n",
+    "plt.imshow(obs['pixel'])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # plt.imshow(obs['glyphs_crop'])\n",
+    "# obs['glyphs_crop'].shape\n",
+    "# obs['blstats']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import minihack\n",
+    "env = gym.make(\"MiniHack-Room-5x5-v0\", observation_keys=(\"pixel_crop\", \"pixel\", 'blstats', 'message'))\n",
+    "env.reset() # each reset generates a new environment instance\n",
+    "obs, reward, end, info = env.step(1)  # move agent '@' north\n",
+    "print(obs['pixel_crop'].shape)\n",
+    "plt.imshow(obs['pixel_crop'])\n",
+    "plt.show()\n",
+    "\n",
+    "print(obs['pixel'].shape)\n",
+    "plt.imshow(obs['pixel'])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import minihack\n",
+    "import crafter\n",
+    "env = gym.make(\"CrafterReward-v1\")\n",
+    "env.reset() # each reset generates a new environment instance\n",
+    "obs, reward, end, info = env.step(1)  # move agent '@' north\n",
+    "print(obs.shape)\n",
+    "plt.imshow(obs)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/poetry.lock b/poetry.lock
index aafa8da..c67ea9c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -441,6 +441,26 @@ mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.6.1)", "types-Pill
 test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
 test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
 
+[[package]]
+name = "crafter"
+version = "1.8.2"
+description = "Open world survival game for reinforcement learning."
+optional = false
+python-versions = "*"
+files = [
+    {file = "crafter-1.8.2.tar.gz", hash = "sha256:4a142c291aa0b137c0808890381b38803876a5811b362801b8cc669faa350def"},
+]
+
+[package.dependencies]
+imageio = "*"
+numpy = "*"
+opensimplex = "*"
+pillow = "*"
+"ruamel.yaml" = "*"
+
+[package.extras]
+gui = ["pygame"]
+
 [[package]]
 name = "cycler"
 version = "0.12.1"
@@ -794,6 +814,37 @@ files = [
     {file = "idna-3.4.tar.gz", hash = "sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4"},
 ]
 
+[[package]]
+name = "imageio"
+version = "2.31.5"
+description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "imageio-2.31.5-py3-none-any.whl", hash = "sha256:97f68e12ba676f2f4b541684ed81f7f3370dc347e8321bc68ee34d37b2dbac9f"},
+    {file = "imageio-2.31.5.tar.gz", hash = "sha256:d8e53f9cd4054880276a3dac0a28c85ba7874084856a55a0294a8ae6ed7f3a8e"},
+]
+
+[package.dependencies]
+numpy = "*"
+pillow = ">=8.3.2"
+
+[package.extras]
+all-plugins = ["astropy", "av", "imageio-ffmpeg", "psutil", "tifffile"]
+all-plugins-pypy = ["av", "imageio-ffmpeg", "psutil", "tifffile"]
+build = ["wheel"]
+dev = ["black", "flake8", "fsspec[github]", "pytest", "pytest-cov"]
+docs = ["numpydoc", "pydata-sphinx-theme", "sphinx (<6)"]
+ffmpeg = ["imageio-ffmpeg", "psutil"]
+fits = ["astropy"]
+full = ["astropy", "av", "black", "flake8", "fsspec[github]", "gdal", "imageio-ffmpeg", "itk", "numpydoc", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "sphinx (<6)", "tifffile", "wheel"]
+gdal = ["gdal"]
+itk = ["itk"]
+linting = ["black", "flake8"]
+pyav = ["av"]
+test = ["fsspec[github]", "pytest", "pytest-cov"]
+tifffile = ["tifffile"]
+
 [[package]]
 name = "importlib-metadata"
 version = "6.8.0"
@@ -1244,6 +1295,28 @@ files = [
 [package.dependencies]
 traitlets = "*"
 
+[[package]]
+name = "minihack"
+version = "0.1.5"
+description = "MiniHack The Planet: A Sandbox for Open-Ended Reinforcement Learning Research"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "minihack-0.1.5.tar.gz", hash = "sha256:b732a05eed9b70cc48735d63fd0c34498611be1342f4bd45a3ad128367d8c97a"},
+]
+
+[package.dependencies]
+gym = ">=0.15,<=0.23"
+nle = "0.9.0"
+numpy = ">=1.16"
+
+[package.extras]
+all = ["black (>=19.10b0)", "flake8 (>=3.7)", "flake8-bugbear (>=20.1)", "hydra-colorlog (>=1.0.0)", "hydra-colorlog (>=1.0.0)", "hydra-core (>=1.0.0)", "hydra-core (>=1.0.0)", "hydra-submitit-launcher (>=1.1.1)", "hydra-submitit-launcher (>=1.1.1)", "inflect", "myst-parser (==0.15.1)", "nbsphinx (==0.8.6)", "pre-commit (>=2.0.1)", "pytest (>=5.3)", "pytest-benchmark (>=3.1.0)", "pyyaml", "ray[default] (==1.3.0)", "ray[rllib] (==1.3.0)", "sphinx (==4.0.2)", "sphinx-rtd-theme (==1.0.0)", "stanza", "torch (>=1.3.1)", "torch (>=1.3.1)", "wandb (>=0.10.31)", "wandb (>=0.10.31)"]
+dev = ["black (>=19.10b0)", "flake8 (>=3.7)", "flake8-bugbear (>=20.1)", "myst-parser (==0.15.1)", "nbsphinx (==0.8.6)", "pre-commit (>=2.0.1)", "pytest (>=5.3)", "pytest-benchmark (>=3.1.0)", "sphinx (==4.0.2)", "sphinx-rtd-theme (==1.0.0)"]
+polybeast = ["hydra-colorlog (>=1.0.0)", "hydra-core (>=1.0.0)", "hydra-submitit-launcher (>=1.1.1)", "pyyaml", "torch (>=1.3.1)", "wandb (>=0.10.31)"]
+rllib = ["hydra-colorlog (>=1.0.0)", "hydra-core (>=1.0.0)", "hydra-submitit-launcher (>=1.1.1)", "ray[default] (==1.3.0)", "ray[rllib] (==1.3.0)", "torch (>=1.3.1)", "wandb (>=0.10.31)"]
+wiki = ["inflect", "stanza"]
+
 [[package]]
 name = "mpmath"
 version = "1.3.0"
@@ -1290,6 +1363,26 @@ doc = ["nb2plots (>=0.7)", "nbconvert (<7.9)", "numpydoc (>=1.6)", "pillow (>=9.
 extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.11)", "sympy (>=1.10)"]
 test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
 
+[[package]]
+name = "nle"
+version = "0.9.0"
+description = "The NetHack Learning Environment (NLE): a reinforcement learning environment based on NetHack"
+optional = false
+python-versions = ">=3.5"
+files = [
+    {file = "nle-0.9.0.tar.gz", hash = "sha256:a98644bdd547017cdde9fdf589b245da98ff8753327285e949d506d9006f10d4"},
+]
+
+[package.dependencies]
+gym = ">=0.15,<=0.23"
+numpy = ">=1.16"
+pybind11 = ">=2.2"
+
+[package.extras]
+agent = ["torch (>=1.3.1)"]
+all = ["black (>=19.10b0)", "cmake_format (>=0.6.10)", "flake8 (>=3.7)", "flake8-bugbear (>=20.1)", "memory-profiler (>=0.60.0)", "pre-commit (>=2.0.1)", "pytest (>=6.2.5)", "pytest-benchmark (>=3.4.1)", "sphinx (>=2.4.4)", "sphinx-rtd-theme (==0.4.3)", "torch (>=1.3.1)"]
+dev = ["black (>=19.10b0)", "cmake_format (>=0.6.10)", "flake8 (>=3.7)", "flake8-bugbear (>=20.1)", "memory-profiler (>=0.60.0)", "pre-commit (>=2.0.1)", "pytest (>=6.2.5)", "pytest-benchmark (>=3.4.1)", "sphinx (>=2.4.4)", "sphinx-rtd-theme (==0.4.3)"]
+
 [[package]]
 name = "numpy"
 version = "1.26.1"
@@ -1371,6 +1464,20 @@ numpy = [
     {version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
 ]
 
+[[package]]
+name = "opensimplex"
+version = "0.4.5"
+description = "OpenSimplex is a noise generation function like Perlin or Simplex noise, but better."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "opensimplex-0.4.5-py3-none-any.whl", hash = "sha256:5e34f2b6f7e2d3e798d7f060b45cb1f92c02b68b6fc2626ac2a36bd53ec6c773"},
+    {file = "opensimplex-0.4.5.tar.gz", hash = "sha256:c390cf70dea97b32bd1a49ba6781e84f48dc93b1cc6c06f36d9d44c548299f90"},
+]
+
+[package.dependencies]
+numpy = ">=1.22"
+
 [[package]]
 name = "packaging"
 version = "23.2"
@@ -1645,6 +1752,20 @@ files = [
 [package.extras]
 tests = ["pytest"]
 
+[[package]]
+name = "pybind11"
+version = "2.11.1"
+description = "Seamless operability between C++11 and Python"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "pybind11-2.11.1-py3-none-any.whl", hash = "sha256:33cdd02a6453380dd71cc70357ce388ad1ee8d32bd0e38fc22b273d050aa29b3"},
+    {file = "pybind11-2.11.1.tar.gz", hash = "sha256:00cd59116a6e8155aecd9174f37ba299d1d397ed4a6b86ac1dfe01b3e40f2cc4"},
+]
+
+[package.extras]
+global = ["pybind11-global (==2.11.1)"]
+
 [[package]]
 name = "pycparser"
 version = "2.21"
@@ -2069,6 +2190,83 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "ruamel-yaml"
+version = "0.18.5"
+description = "ruamel.yaml is a YAML parser/emitter that supports roundtrip preservation of comments, seq/map flow style, and map key order"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "ruamel.yaml-0.18.5-py3-none-any.whl", hash = "sha256:a013ac02f99a69cdd6277d9664689eb1acba07069f912823177c5eced21a6ada"},
+    {file = "ruamel.yaml-0.18.5.tar.gz", hash = "sha256:61917e3a35a569c1133a8f772e1226961bf5a1198bea7e23f06a0841dea1ab0e"},
+]
+
+[package.dependencies]
+"ruamel.yaml.clib" = {version = ">=0.2.7", markers = "platform_python_implementation == \"CPython\" and python_version < \"3.13\""}
+
+[package.extras]
+docs = ["mercurial (>5.7)", "ryd"]
+jinja2 = ["ruamel.yaml.jinja2 (>=0.2)"]
+
+[[package]]
+name = "ruamel-yaml-clib"
+version = "0.2.8"
+description = "C version of reader, parser and emitter for ruamel.yaml derived from libyaml"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "ruamel.yaml.clib-0.2.8-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:b42169467c42b692c19cf539c38d4602069d8c1505e97b86387fcf7afb766e1d"},
+    {file = "ruamel.yaml.clib-0.2.8-cp310-cp310-macosx_13_0_arm64.whl", hash = "sha256:07238db9cbdf8fc1e9de2489a4f68474e70dffcb32232db7c08fa61ca0c7c462"},
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+    {file = "ruamel.yaml.clib-0.2.8-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:184565012b60405d93838167f425713180b949e9d8dd0bbc7b49f074407c5a8b"},
+    {file = "ruamel.yaml.clib-0.2.8-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:a75879bacf2c987c003368cf14bed0ffe99e8e85acfa6c0bfffc21a090f16880"},
+    {file = "ruamel.yaml.clib-0.2.8-cp39-cp39-win32.whl", hash = "sha256:84b554931e932c46f94ab306913ad7e11bba988104c5cff26d90d03f68258cd5"},
+    {file = "ruamel.yaml.clib-0.2.8-cp39-cp39-win_amd64.whl", hash = "sha256:25ac8c08322002b06fa1d49d1646181f0b2c72f5cbc15a85e80b4c30a544bb15"},
+    {file = "ruamel.yaml.clib-0.2.8.tar.gz", hash = "sha256:beb2e0404003de9a4cab9753a8805a8fe9320ee6673136ed7f04255fe60bb512"},
+]
+
 [[package]]
 name = "ruff"
 version = "0.1.5"
@@ -2940,4 +3138,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "e2ad5cf8670b043bb7174a4c7f6e4c89644461eb1680e282b436541672e4e67c"
+content-hash = "d265b7789c918f4c2dc7d3db9ea80871958320fedc47576d0e0f78f327f42f6e"
diff --git a/pyproject.toml b/pyproject.toml
index 3cad2fb..f43a0b0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,6 +28,8 @@ torchvision = "^0.16.0"
 numpy = ">=1.18.0"
 gym = {version = "0.22.0", extras = ["accept-rom-license", "atari"]}
 scipy = "^1.11.3"
+crafter = "^1.8.2"
+minihack = "^0.1.5"
 
 [[tool.poetry.source]]
 name = "pytorch"
diff --git a/research_journal.md b/research_journal.md
index 8bdf3fe..2e6e327 100644
--- a/research_journal.md
+++ b/research_journal.md
@@ -232,3 +232,85 @@ Training tokenizer: 100%|██████████████████
 Training world_model: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [01:15<00:00,  2.64it/s]
 Training actor_critic: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [03:20<00:00, 10.05s/it]
 
+
+
+- what about resume? oh we seem to have that although the code doesn't make sense https://hydra.cc/docs/tutorials/basic/running_your_app/working_directory/ https://hydra.cc/docs/1.2/upgrades/1.1_to_1.2/changes_to_job_working_dir/ see eval.py
+- [ ] but it's still too damn slow. what about bfloat16? using auto case?
+- why does it take so long? it would be nice to have a reproduction notebook
+- also the model might be to small now....
+
+how to play
+```sh
+cd outputs/2023-11-17/07-59-44
+python scripts/play.sh
+```
+
+
+## Envs
+
+tl:dr just use pong or breakout or crafter (1m steps)
+
+for steps see [crafter paper](https://arxiv.org/pdf/2109.06780.pdf)
+
+Nethack learning env. What's the obs size? 21x79 of glyphs (5991 possibilities) and 21 dim of stats
+- they use an lstm of 128. 5 layer conv
+- requires 1B steps
+- 
+atari:
+- reqs 200M stpes
+
+progcen:
+- 200M steps
+
+minihack:
+- 2M steps for room 5xt
+- but needs editing to be atari compatible. e.g. 336 × 1264 × 3 pixels
+- pixel_crop 64,64,3 or 9x9 crop works!
+- lstm 256
+- The training on MiniHack’s Room-5x5 task for two million timesteps using our IMPALA baseline takes approximately 4:30 minutes (r
+
+crafter
+- reqs 1M steps
+- "All agents trained for 1M environment steps in under 24 hours on a single GPU and we repeated the training for 10 random seeds per method. The training reward curves are included in Appendix "
+
+
+
+
+from https://arxiv.org/pdf/2111.09794.pdf
+There are several PCG state-varying gridworld environments (
+- [MiniGrid](https://minigrid.farama.org/environments/minigrid/), ~~- BabyAI~~
+- Crafter,
+- 2019 [Rogue-gym,](https://github.com/kngwyu/rogue-gym) 
+- 2020 MarsExplorer, maxe exploration. 1M steps
+- NLE, 
+- MiniHack; 
+- [gym\_nethack](http://campbelljc.com/research/gym_nethack/)
+- 2018 [rogueinabox](https://github.com/rogueinabox/rogueinabox)
+- [rogue-gym](https://github.com/kngwyu/rogue-gym)
+- [MiniGrid](https://github.com/maximecb/gym-minigrid)
+- 2019 [CoinRun](https://github.com/openai/coinrun) no traction or maintanance
+- [MineRL](http://minerl.io/docs)
+- [Project Malmo](https://www.microsoft.com/en-us/research/project/project-malmo/) miencraft
+- [OpenAI Procgen Benchmark](https://openai.com/blog/procgen-benchmark/) 200M steps
+- 2020 [Obstacle Tower](https://github.com/Unity-Technologies/obstacle-tower-env) - 3d slow
+
+non-PCG observation-varying continuous control environments
+- (RoboSuite, DMC-Remastered, DMC-GB, DCS, KitchenShift, NaturalEnvs MuJoCo; Fan
+et al., 2021; Grigsby & Qi, 2020; Hansen & Wang, 2021; Stone et al., 2021; Xing et al., 2021a;
+Zhang et al., 2018a), and multi-task continuous control benchmarks which could be adapted
+to ZSG (CausalWorld, RLBench, Meta-world; Ahmed et al., 2020; James et al., 2019a; Yu
+et al., 2019).
+
+## investigating model slowness
+
+So it's all just that using the transformer to imagine takes almost 0.1s. but it's run so many more times than during training. All my ideas to speed it up don't work.
+
+- [x] eval. no grad
+- [x] remove the call for adapter, causal mask each time?
+- [ ] lower rank?
+
+
+Ok so it's all just the
+- rollout, controller by max block size. 10x
+- the fact that actor_critic can use a larger batch, therefore 4-8x more samples
+- for each one it imagines 2
diff --git a/src/models/actor_critic.py b/src/models/actor_critic.py
index 13eb51d..976439a 100644
--- a/src/models/actor_critic.py
+++ b/src/models/actor_critic.py
@@ -36,8 +36,8 @@ class ImagineOutput:
 class ActorCritic(nn.Module):
     def __init__(self, act_vocab_size, use_original_obs: bool = False, lstm_dim = 16) -> None:
         super().__init__()
-        shrink = 8
-        s = 2
+        shrink = 1
+        s = 1
         self.use_original_obs = use_original_obs
         self.conv1 = nn.Conv2d(3, 32//s, 3, stride=1, padding=1)
         self.maxp1 = nn.MaxPool2d(2, 2)
diff --git a/src/models/transformer.py b/src/models/transformer.py
index 86139c4..5ccdc70 100644
--- a/src/models/transformer.py
+++ b/src/models/transformer.py
@@ -49,7 +49,8 @@ class Transformer(nn.Module):
     # @torch.cuda.amp.autocast(dtype=torch.bfloat16)
     def forward(self, sequences: torch.Tensor, past_keys_values: Optional[KeysValues] = None) -> torch.Tensor:
         assert past_keys_values is None or len(past_keys_values) == self.config.num_layers
-        with set_adapter(self.model, "dynamics"), disable_causal_mask(), torch.cuda.amp.autocast(dtype=torch.bfloat16):
+        # with set_adapter(self.model, "dynamics"), disable_causal_mask(), torch.cuda.amp.autocast(dtype=torch.bfloat16):
+        with torch.cuda.amp.autocast(dtype=torch.bfloat16):
             # sequences = sequences.to(torch.bfloat16)
             outputs = self.model(
                 inputs_embeds=sequences,
@@ -111,7 +112,9 @@ def load_pretrained_model(config, device="cuda:0"):
     )
     base_model_peft = peft.get_peft_model(base_model, peft_config)
     base_model_peft.add_adapter("dynamics", peft_config)
+    base_model_peft.set_adapter("dynamics")
     print(base_model_peft.print_trainable_parameters())
+    disable_causal_mask()
     return base_model_peft
 
 @contextmanager
diff --git a/src/trainer.py b/src/trainer.py
index 5091fc3..9402c60 100644
--- a/src/trainer.py
+++ b/src/trainer.py
@@ -46,6 +46,7 @@ class Trainer:
         self.reconstructions_dir = self.media_dir / 'reconstructions'
 
         if not cfg.common.resume:
+            print('cwd', Path.cwd())
             config_dir = Path('config')
             config_path = config_dir / 'trainer.yaml'
             config_dir.mkdir(exist_ok=False, parents=False)

From a341e7aaad35c4468c8911130250f8e3b9b1462c Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Sat, 18 Nov 2023 07:44:05 +0800
Subject: [PATCH 16/30] abs imports, fix play for crafter

---
 config/trainer.yaml        |  2 +-
 research_journal.md        |  4 ++++
 src/agent.py               |  8 ++++----
 src/game/keymap.py         | 27 ++++++++++++++++++++++++++-
 src/models/actor_critic.py | 10 +++++-----
 src/models/world_model.py  | 12 ++++++------
 src/play.py                | 10 +++++-----
 src/utils.py               |  2 +-
 8 files changed, 52 insertions(+), 23 deletions(-)

diff --git a/config/trainer.yaml b/config/trainer.yaml
index d1f92a6..390a3ac 100644
--- a/config/trainer.yaml
+++ b/config/trainer.yaml
@@ -7,7 +7,7 @@ defaults:
   - datasets: default
 
 wandb:
-  mode: offline
+  mode: online
   project: iris
   entity: null
   name: null
diff --git a/research_journal.md b/research_journal.md
index 2e6e327..0d40317 100644
--- a/research_journal.md
+++ b/research_journal.md
@@ -314,3 +314,7 @@ Ok so it's all just the
 - rollout, controller by max block size. 10x
 - the fact that actor_critic can use a larger batch, therefore 4-8x more samples
 - for each one it imagines 2
+
+# 2023-11-18 06:17:55
+
+It trained overnight, now I would like to view a replay
diff --git a/src/agent.py b/src/agent.py
index a8d943c..5534232 100644
--- a/src/agent.py
+++ b/src/agent.py
@@ -4,10 +4,10 @@ import torch
 from torch.distributions.categorical import Categorical
 import torch.nn as nn
 
-from .models.actor_critic import ActorCritic
-from .models.tokenizer import Tokenizer
-from .models.world_model import WorldModel
-from .utils import extract_state_dict
+from src.models.actor_critic import ActorCritic
+from src.models.tokenizer import Tokenizer
+from src.models.world_model import WorldModel
+from src.utils import extract_state_dict
 
 
 class Agent(nn.Module):
diff --git a/src/game/keymap.py b/src/game/keymap.py
index 62aadc7..735eb8c 100644
--- a/src/game/keymap.py
+++ b/src/game/keymap.py
@@ -12,6 +12,10 @@ def get_keymap_and_action_names(name):
 
     if name == 'atari':
         return ATARI_KEYMAP, ATARI_ACTION_NAMES
+    
+    if name == 'atari/CrafterReward-v1':
+        env_id = name.split('atari/')[1]
+        return CRAFTER_KEYMAP, gym.make(env_id).action_names 
 
     assert name.startswith('atari/')
     env_id = name.split('atari/')[1]
@@ -100,4 +104,25 @@ EMPTY_ACTION_NAMES = [
 ]
 
 EMPTY_KEYMAP = {
-}
\ No newline at end of file
+}
+
+CRAFTER_KEYMAP = {
+    pygame.K_a: 'move_left',
+    pygame.K_d: 'move_right',
+    pygame.K_w: 'move_up',
+    pygame.K_s: 'move_down',
+    pygame.K_SPACE: 'do',
+    pygame.K_TAB: 'sleep',
+
+    pygame.K_r: 'place_stone',
+    pygame.K_t: 'place_table',
+    pygame.K_f: 'place_furnace',
+    pygame.K_p: 'place_plant',
+
+    pygame.K_1: 'make_wood_pickaxe',
+    pygame.K_2: 'make_stone_pickaxe',
+    pygame.K_3: 'make_iron_pickaxe',
+    pygame.K_4: 'make_wood_sword',
+    pygame.K_5: 'make_stone_sword',
+    pygame.K_6: 'make_iron_sword',
+}
diff --git a/src/models/actor_critic.py b/src/models/actor_critic.py
index 976439a..ec1256d 100644
--- a/src/models/actor_critic.py
+++ b/src/models/actor_critic.py
@@ -10,11 +10,11 @@ import torch.nn as nn
 import torch.nn.functional as F
 from tqdm import tqdm
 
-from ..dataset import Batch
-from ..envs.world_model_env import WorldModelEnv
-from ..models.tokenizer import Tokenizer
-from ..models.world_model import WorldModel
-from ..utils import compute_lambda_returns, LossWithIntermediateLosses
+from src.dataset import Batch
+from src.envs.world_model_env import WorldModelEnv
+from src.models.tokenizer import Tokenizer
+from src.models.world_model import WorldModel
+from src.utils import compute_lambda_returns, LossWithIntermediateLosses
 
 
 @dataclass
diff --git a/src/models/world_model.py b/src/models/world_model.py
index 349423c..eecfe60 100644
--- a/src/models/world_model.py
+++ b/src/models/world_model.py
@@ -6,12 +6,12 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from ..dataset import Batch
-from .kv_caching import KeysValues
-from .slicer import Embedder, Head
-from .tokenizer import Tokenizer
-from .transformer import Transformer, TransformerConfig
-from ..utils import init_weights, LossWithIntermediateLosses
+from src.dataset import Batch
+from src.models.kv_caching import KeysValues
+from src.models.slicer import Embedder, Head
+from src.models.tokenizer import Tokenizer
+from src.models.transformer import Transformer, TransformerConfig
+from src.utils import init_weights, LossWithIntermediateLosses
 
 
 @dataclass
diff --git a/src/play.py b/src/play.py
index 7b18dee..7e3ab85 100644
--- a/src/play.py
+++ b/src/play.py
@@ -6,11 +6,11 @@ from hydra.utils import instantiate
 from omegaconf import DictConfig
 import torch
 
-from agent import Agent
-from envs import SingleProcessEnv, WorldModelEnv
-from game import AgentEnv, EpisodeReplayEnv, Game
-from models.actor_critic import ActorCritic
-from models.world_model import WorldModel
+from src.agent import Agent
+from src.envs import SingleProcessEnv, WorldModelEnv
+from src.game import AgentEnv, EpisodeReplayEnv, Game
+from src.models.actor_critic import ActorCritic
+from src.models.world_model import WorldModel
 
 
 @hydra.main(config_path="../config", config_name="trainer")
diff --git a/src/utils.py b/src/utils.py
index 1aaf3b2..d64273d 100644
--- a/src/utils.py
+++ b/src/utils.py
@@ -8,7 +8,7 @@ import numpy as np
 import torch
 import torch.nn as nn
 
-from .episode import Episode
+from src.episode import Episode
 
 from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
 

From 9b399031ca3af855c5d0ddcfef8acdf5e297094b Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Sat, 18 Nov 2023 16:32:59 +0800
Subject: [PATCH 17/30] document play.sh options

---
 scripts/play.sh       | 14 +++++++-------
 src/envs/wrappers.py  |  1 +
 src/game/agent_env.py |  8 ++++----
 3 files changed, 12 insertions(+), 11 deletions(-)

diff --git a/scripts/play.sh b/scripts/play.sh
index eff9abe..967db60 100755
--- a/scripts/play.sh
+++ b/scripts/play.sh
@@ -14,22 +14,22 @@ while [ "$1" != "" ]; do
             ;;
         -h | --header )
             header=1
-            ;;
+            ;; # adds banner with env metadata like action
         -r | --reconstruction )
             reconstruction=1
-            ;;
+            ;; # 3 panes [original_obs, resized_obs, reconstructed], doesn't do anything if any of -w -a or -e are set. shows quality of encoder decoder
         -s | --save-mode )
             save_mode=1
-            ;;
+            ;; # lets you save the episode to mp4
         -a | --agent-world-model )
             mode="agent_in_world_model"
-            ;;
+            ;; # the agent plays in the world model env, shows the quality of the dynamics model
         -e | --episode )
             mode="episode_replay"
-            ;;
+            ;; # replay train, test, or imagined episodes. shows quality of dynamics model
         -w | --world-model )
             mode="play_in_world_model"
-            ;;
+            ;; # human plays in world model
         * )
             echo Invalid usage : $1
             exit 1
@@ -37,4 +37,4 @@ while [ "$1" != "" ]; do
     shift
 done
 
-python src/play.py hydra.run.dir=. hydra.output_subdir=null +mode="${mode}" +fps="${fps}" +header="${header}" +reconstruction="${reconstruction}" +save_mode="${save_mode}"
+python -m pdb src/play.py hydra.run.dir=. hydra.output_subdir=null +mode="${mode}" +fps="${fps}" +header="${header}" +reconstruction="${reconstruction}" +save_mode="${save_mode}"
diff --git a/src/envs/wrappers.py b/src/envs/wrappers.py
index 4183483..fa586ac 100644
--- a/src/envs/wrappers.py
+++ b/src/envs/wrappers.py
@@ -39,6 +39,7 @@ def make_crafter(id, size=64, max_episode_steps=None, done_on_life_loss=False):
     # https://github.com/danijar/dreamerv2/blob/07d906e9c4322c6fc2cd6ed23e247ccd6b7c8c41/dreamerv2/common/envs.py#L242
     # https://github.com/footoredo/torchbeast/blob/12939569cc46b6a8616e4c25b138d97248cc8581/torchbeast/atari_wrappers.py#L301
     env = gym.make(id)
+    env = ResizeObsWrapper(env, (size, size))
     return env
 
 
diff --git a/src/game/agent_env.py b/src/game/agent_env.py
index 77831ad..19ea16b 100644
--- a/src/game/agent_env.py
+++ b/src/game/agent_env.py
@@ -4,14 +4,14 @@ from PIL import Image
 import torch
 from torchvision.transforms.functional import InterpolationMode, resize
 
-from agent import Agent
-from envs import SingleProcessEnv, WorldModelEnv
-from game.keymap import get_keymap_and_action_names
+from src.agent import Agent
+from src.envs import SingleProcessEnv, WorldModelEnv
+from src.game.keymap import get_keymap_and_action_names
 
 
 class AgentEnv:
     def __init__(self, agent: Agent, env: SingleProcessEnv, keymap_name: str, do_reconstruction: bool) -> None:
-        assert isinstance(env, SingleProcessEnv) or isinstance(env, WorldModelEnv)
+        assert isinstance(env, SingleProcessEnv) or isinstance(env, WorldModelEnv), f"{env}"
         self.agent = agent
         self.env = env
         _, self.action_names = get_keymap_and_action_names(keymap_name)

From 36ea66d0ae04ec8f7f25198300f300cdba8081a5 Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Sun, 19 Nov 2023 08:17:38 +0800
Subject: [PATCH 18/30] misc

---
 .vscode/launch.json               | 13 +----------
 config/trainer.yaml               |  4 ++--
 research_journal.md               | 36 +++++++++++++++++++++++++++++++
 src/game/keymap.py                | 32 +++++++++++++--------------
 src/models/tokenizer/tokenizer.py |  2 +-
 src/models/transformer.py         | 17 ++++++++++-----
 src/models/world_model.py         | 11 +++++++++-
 src/trainer.py                    |  2 +-
 8 files changed, 79 insertions(+), 38 deletions(-)

diff --git a/.vscode/launch.json b/.vscode/launch.json
index 9044a8c..dbe115f 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -14,7 +14,7 @@
             "autoReload": {"enable": true,},
             "env": {"WANDB_MODE":"disabled"},
             "args": [
-                "'wandb.mode=disabled",
+                // "'wandb.mode=disabled",
                 // "env.train.id=BreakoutNoFrameskip-v4",
                 "env.train.id=CrafterReward-v1",
                 // # make it start early
@@ -34,19 +34,8 @@
             "console": "integratedTerminal",
             "justMyCode": false,
             "autoReload": {"enable": true,},
-            "env": {"WANDB_MODE":"disabled"},
             "args": [
-                "'wandb.mode=disabled",
-                // "env.train.id=BreakoutNoFrameskip-v4",
                 "env.train.id=CrafterReward-v1",
-                // # make it start early
-                "training.tokenizer.start_after_epochs=1",
-                "training.world_model.start_after_epochs=1",
-                "training.actor_critic.start_after_epochs=1",
-                
-                "training.tokenizer.steps_per_epoch=10",
-                "training.world_model.steps_per_epoch=10",
-                "training.actor_critic.steps_per_epoch=10",
             ]
         }
     ]
diff --git a/config/trainer.yaml b/config/trainer.yaml
index 390a3ac..b4fe7c5 100644
--- a/config/trainer.yaml
+++ b/config/trainer.yaml
@@ -60,7 +60,7 @@ training:
     start_after_epochs: 5
     steps_per_epoch: 200
   world_model:
-    batch_num_samples: 8 # pretrained models use lots of 
+    batch_num_samples: 8  # pretrained models use lots of ram
     grad_acc_steps: 1
     max_grad_norm: 10.0
     weight_decay: 0.01
@@ -70,7 +70,7 @@ training:
     batch_num_samples: 16
     grad_acc_steps: 1
     max_grad_norm: 10.0
-    start_after_epochs: 50
+    start_after_epochs: 1500
     steps_per_epoch: 40
     imagine_horizon: ${common.sequence_length}
     burn_in: 20
diff --git a/research_journal.md b/research_journal.md
index 0d40317..b7e2a98 100644
--- a/research_journal.md
+++ b/research_journal.md
@@ -318,3 +318,39 @@ Ok so it's all just the
 # 2023-11-18 06:17:55
 
 It trained overnight, now I would like to view a replay
+
+Hmm "delta-IRIS" ∆-IRIS 
+https://openreview.net/forum?id=o8IDoZggqO
+∆-IRIS encodes
+new frames by attending to the ongoing trajectory, effec-
+tively describing deltas between timesteps.
+This new ap-
+proach drastically reduces the number of tokens to encode
+frames, since they are not encoded independently as in IRIS.
+In the Crafter benchmark (Hafner, 2022), ∆-IRIS unlocks
+16 out of 22 objectives at the 10M frames mark
+
+
+# 2023-11-18 16:16:16
+
+Why is it not learning? It's because the dynamics model is total BS!!!
+
+- [ ] Well lets try training it for longer then. It's cheap to train so..
+- [ ] also maybe train tokenizer and model together? I have a lot of frozen layers, including the embeddings... so might be better
+  - [ ] oh no we do have an unforzen embedder before the transformer or more layers
+  - maybe I need a higher rank lora? after all I'm changing a lot from text tokens
+  - maybe no tokens, bypass to embedder?
+
+
+# 2023-11-19 06:45:34
+
+So I tried just trainign the world model for 200 epochs. And with a post_embedding layer. It helped the flickering. But not enougth to actually go for the obvious local minima of the next state equals the last
+
+
+
+  
+idea
+- bypass embedding?, but wait dreamerv3 needed quant z... 
+  - yes I am bypassing it by passing in the input_embeds... but maybe I shouldn't
+- use same embedding everywhere. e.g. model embedding in encoder decoder?
+  - Our embedings is (embed_tokens): Embedding(32000, 2048). So we would need to encode to 32000!
diff --git a/src/game/keymap.py b/src/game/keymap.py
index 735eb8c..e912868 100644
--- a/src/game/keymap.py
+++ b/src/game/keymap.py
@@ -107,22 +107,22 @@ EMPTY_KEYMAP = {
 }
 
 CRAFTER_KEYMAP = {
-    pygame.K_a: 'move_left',
-    pygame.K_d: 'move_right',
-    pygame.K_w: 'move_up',
-    pygame.K_s: 'move_down',
-    pygame.K_SPACE: 'do',
-    pygame.K_TAB: 'sleep',
+    pygame.K_a: 1,
+    pygame.K_d: 2,
+    pygame.K_w: 3,
+    pygame.K_s: 4,
+    pygame.K_SPACE: 5,
+    pygame.K_TAB: 6,
 
-    pygame.K_r: 'place_stone',
-    pygame.K_t: 'place_table',
-    pygame.K_f: 'place_furnace',
-    pygame.K_p: 'place_plant',
+    pygame.K_r: 7,
+    pygame.K_t: 8,
+    pygame.K_f: 9,
+    pygame.K_p: 10,
 
-    pygame.K_1: 'make_wood_pickaxe',
-    pygame.K_2: 'make_stone_pickaxe',
-    pygame.K_3: 'make_iron_pickaxe',
-    pygame.K_4: 'make_wood_sword',
-    pygame.K_5: 'make_stone_sword',
-    pygame.K_6: 'make_iron_sword',
+    pygame.K_1: 11,
+    pygame.K_2: 12,
+    pygame.K_3: 13,
+    pygame.K_4: 14,
+    pygame.K_5: 15,
+    pygame.K_6: 16,
 }
diff --git a/src/models/tokenizer/tokenizer.py b/src/models/tokenizer/tokenizer.py
index 1b8723d..e69c18e 100644
--- a/src/models/tokenizer/tokenizer.py
+++ b/src/models/tokenizer/tokenizer.py
@@ -28,7 +28,7 @@ class Tokenizer(nn.Module):
         self.vocab_size = vocab_size
         self.encoder = encoder
         self.pre_quant_conv = torch.nn.Conv2d(encoder.config.z_channels, embed_dim, 1)
-        self.embedding = nn.Embedding(vocab_size, embed_dim)
+        self.embedding = nn.Embedding(vocab_size, embed_dim) # TODO: use model embed?
         self.post_quant_conv = torch.nn.Conv2d(embed_dim, decoder.config.z_channels, 1)
         self.decoder = decoder
         self.embedding.weight.data.uniform_(-1.0 / vocab_size, 1.0 / vocab_size)
diff --git a/src/models/transformer.py b/src/models/transformer.py
index 5ccdc70..89a2b25 100644
--- a/src/models/transformer.py
+++ b/src/models/transformer.py
@@ -46,18 +46,15 @@ class Transformer(nn.Module):
         device = self.ln_f.weight.device  # Assumption that all submodules are on the same device
         return KeysValues(n, self.config.num_heads, max_tokens, self.config.embed_dim, self.config.num_layers, device)
 
-    # @torch.cuda.amp.autocast(dtype=torch.bfloat16)
     def forward(self, sequences: torch.Tensor, past_keys_values: Optional[KeysValues] = None) -> torch.Tensor:
         assert past_keys_values is None or len(past_keys_values) == self.config.num_layers
-        # with set_adapter(self.model, "dynamics"), disable_causal_mask(), torch.cuda.amp.autocast(dtype=torch.bfloat16):
         with torch.cuda.amp.autocast(dtype=torch.bfloat16):
-            # sequences = sequences.to(torch.bfloat16)
             outputs = self.model(
                 inputs_embeds=sequences,
                 return_dict=True,
                 output_hidden_states=True,
             )
-        x = outputs.logits#.to(torch.float32)
+        x = outputs.logits
         x = self.ln_f(x)
         
         # fake it, since it's used to keep track of steps
@@ -114,7 +111,7 @@ def load_pretrained_model(config, device="cuda:0"):
     base_model_peft.add_adapter("dynamics", peft_config)
     base_model_peft.set_adapter("dynamics")
     print(base_model_peft.print_trainable_parameters())
-    disable_causal_mask()
+    disable_causal_mask_always()
     return base_model_peft
 
 @contextmanager
@@ -130,6 +127,16 @@ def set_adapter(model, adapter_name):
     finally:
         model.set_adapter(old_adapter_name)
 
+def disable_causal_mask_always():
+    import transformers.models.llama.modeling_llama as modeling
+
+    decoder_fn = modeling._make_causal_mask
+
+    def encoder_fn(*args, **kwargs):
+        return torch.zeros_like(decoder_fn(*args, **kwargs))
+
+    modeling._make_causal_mask = encoder_fn
+
 @contextmanager
 def disable_causal_mask():
     import transformers.models.llama.modeling_llama as modeling
diff --git a/src/models/world_model.py b/src/models/world_model.py
index eecfe60..723b733 100644
--- a/src/models/world_model.py
+++ b/src/models/world_model.py
@@ -41,6 +41,13 @@ class WorldModel(nn.Module):
             block_masks=[act_tokens_pattern, obs_tokens_pattern],
             embedding_tables=nn.ModuleList([nn.Embedding(act_vocab_size, config.embed_dim), nn.Embedding(obs_vocab_size, config.embed_dim)])
         )
+        self.post_embed = nn.Sequential(
+            nn.Linear(config.embed_dim, config.embed_dim),
+            nn.ReLU(),
+            nn.Linear(config.embed_dim, config.embed_dim),
+            nn.ReLU(),
+            nn.Linear(config.embed_dim, config.embed_dim)
+        )
 
         self.head_observations = Head(
             max_blocks=config.max_blocks,
@@ -86,7 +93,8 @@ class WorldModel(nn.Module):
         prev_steps = 0 if past_keys_values is None else past_keys_values.size
 
         sequences = self.embedder(tokens, num_steps, prev_steps) + self.pos_emb(prev_steps + torch.arange(num_steps, device=tokens.device))
-
+        # [batch=8, num_steps=170, embed_size=2048]
+        sequences = self.post_embed(sequences)
         x = self.transformer(sequences, past_keys_values)
 
         logits_observations = self.head_observations(x, num_steps=num_steps, prev_steps=prev_steps)
@@ -98,6 +106,7 @@ class WorldModel(nn.Module):
     def compute_loss(self, batch: Batch, tokenizer: Tokenizer, **kwargs: Any) -> LossWithIntermediateLosses:
 
         with torch.no_grad():
+            # [B=8, S=10, Colors=3, H=64, W=64] -> [B=8, S=10, 16]
             obs_tokens = tokenizer.encode(batch['observations'], should_preprocess=True).tokens  # (BL, K)
 
         act_tokens = rearrange(batch['actions'], 'b l -> b l 1')
diff --git a/src/trainer.py b/src/trainer.py
index 9402c60..af0460c 100644
--- a/src/trainer.py
+++ b/src/trainer.py
@@ -137,11 +137,11 @@ class Trainer:
 
         if epoch > cfg_tokenizer.start_after_epochs:
             metrics_tokenizer = self.train_component(self.agent.tokenizer, self.optimizer_tokenizer, sequence_length=1, sample_from_start=True, **cfg_tokenizer)
-        self.agent.tokenizer.eval()
 
         if epoch > cfg_world_model.start_after_epochs:
             metrics_world_model = self.train_component(self.agent.world_model, self.optimizer_world_model, sequence_length=self.cfg.common.sequence_length, sample_from_start=True, tokenizer=self.agent.tokenizer, **cfg_world_model)
         self.agent.world_model.eval()
+        self.agent.tokenizer.eval()
 
         if epoch > cfg_actor_critic.start_after_epochs:
             metrics_actor_critic = self.train_component(self.agent.actor_critic, self.optimizer_actor_critic, sequence_length=1 + self.cfg.training.actor_critic.burn_in, sample_from_start=False, tokenizer=self.agent.tokenizer, world_model=self.agent.world_model, **cfg_actor_critic)

From 7098b679559a7e696540d8b698fbd5921f67d28f Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Sun, 19 Nov 2023 08:48:36 +0800
Subject: [PATCH 19/30] 1

---
 config/tokenizer/default.yaml     | 6 +++---
 config/world_model/default.yaml   | 4 ++--
 research_journal.md               | 2 +-
 src/models/tokenizer/tokenizer.py | 4 ++--
 src/models/world_model.py         | 1 +
 src/play.py                       | 5 +++--
 src/trainer.py                    | 7 +++++--
 7 files changed, 17 insertions(+), 12 deletions(-)

diff --git a/config/tokenizer/default.yaml b/config/tokenizer/default.yaml
index 64e2bac..2df2b4d 100644
--- a/config/tokenizer/default.yaml
+++ b/config/tokenizer/default.yaml
@@ -1,14 +1,14 @@
 _target_: src.models.tokenizer.Tokenizer
 
-vocab_size: 2048
-embed_dim: 2048
+vocab_size: 32000 # change to llm vocab dim
+embed_dim: 2048  # change this to whatever the embedding dimension is in your pretrained llm 2048 for llama. 2560 for stablelm
 encoder:
   _target_: src.models.tokenizer.Encoder
   config:
     _target_: src.models.tokenizer.EncoderDecoderConfig
     resolution: 64
     in_channels: 3
-    z_channels: 2048
+    z_channels: 32000
     ch: 64
     ch_mult: [1, 1, 1, 1, 1]
     num_res_blocks: 2
diff --git a/config/world_model/default.yaml b/config/world_model/default.yaml
index 08ede7e..76894ac 100644
--- a/config/world_model/default.yaml
+++ b/config/world_model/default.yaml
@@ -2,8 +2,8 @@ _target_: src.models.TransformerConfig
 max_blocks: 10  # this is the rollout length when training policy
 num_layers: 1
 num_heads: 1
-embed_dim: 2048  # change this to whatever the embedding dimension is in your pretrained llm 2048 for llama. 2560 for stablelm
+embed_dim: ${..tokenizer.embed_dim}
 dropout: 0.1
 model_name: "PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T"
 rank: 32
-tokens_per_block: 17
+tokens_per_block: 17 # how much info we can encode
diff --git a/research_journal.md b/research_journal.md
index b7e2a98..d7bbb80 100644
--- a/research_journal.md
+++ b/research_journal.md
@@ -352,5 +352,5 @@ So I tried just trainign the world model for 200 epochs. And with a post_embeddi
 idea
 - bypass embedding?, but wait dreamerv3 needed quant z... 
   - yes I am bypassing it by passing in the input_embeds... but maybe I shouldn't
-- use same embedding everywhere. e.g. model embedding in encoder decoder?
+- [x] use same embedding everywhere. e.g. model embedding in encoder decoder?
   - Our embedings is (embed_tokens): Embedding(32000, 2048). So we would need to encode to 32000!
diff --git a/src/models/tokenizer/tokenizer.py b/src/models/tokenizer/tokenizer.py
index e69c18e..332c1fe 100644
--- a/src/models/tokenizer/tokenizer.py
+++ b/src/models/tokenizer/tokenizer.py
@@ -23,12 +23,12 @@ class TokenizerEncoderOutput:
 
 
 class Tokenizer(nn.Module):
-    def __init__(self, vocab_size: int, embed_dim: int, encoder: Encoder, decoder: Decoder, with_lpips: bool = True) -> None:
+    def __init__(self, embedding, vocab_size: int, embed_dim: int, encoder: Encoder, decoder: Decoder, with_lpips: bool = True) -> None:
         super().__init__()
         self.vocab_size = vocab_size
         self.encoder = encoder
         self.pre_quant_conv = torch.nn.Conv2d(encoder.config.z_channels, embed_dim, 1)
-        self.embedding = nn.Embedding(vocab_size, embed_dim) # TODO: use model embed?
+        self.embedding = embedding # nn.Embedding(vocab_size, embed_dim) # TODO: use model embed?
         self.post_quant_conv = torch.nn.Conv2d(embed_dim, decoder.config.z_channels, 1)
         self.decoder = decoder
         self.embedding.weight.data.uniform_(-1.0 / vocab_size, 1.0 / vocab_size)
diff --git a/src/models/world_model.py b/src/models/world_model.py
index 723b733..cb3e798 100644
--- a/src/models/world_model.py
+++ b/src/models/world_model.py
@@ -92,6 +92,7 @@ class WorldModel(nn.Module):
         assert num_steps <= self.config.max_tokens
         prev_steps = 0 if past_keys_values is None else past_keys_values.size
 
+        # TODO: replace wth model embedder?
         sequences = self.embedder(tokens, num_steps, prev_steps) + self.pos_emb(prev_steps + torch.arange(num_steps, device=tokens.device))
         # [batch=8, num_steps=170, embed_size=2048]
         sequences = self.post_embed(sequences)
diff --git a/src/play.py b/src/play.py
index 7e3ab85..28c3f07 100644
--- a/src/play.py
+++ b/src/play.py
@@ -33,8 +33,9 @@ def main(cfg: DictConfig):
         keymap = 'episode_replay'
 
     else:
-        tokenizer = instantiate(cfg.tokenizer)
-        world_model = WorldModel(obs_vocab_size=tokenizer.vocab_size, act_vocab_size=test_env.num_actions, config=instantiate(cfg.world_model))
+        # tokenizer = instantiate(cfg.tokenizer)
+        world_model = WorldModel(obs_vocab_size=cfg.tokenizer.vocab_size, act_vocab_size=test_env.num_actions, config=instantiate(cfg.world_model))
+        tokenizer = Tokenizer(embedding=embedding, **cfg.tokenizer.embedding)
         actor_critic = ActorCritic(**cfg.actor_critic, act_vocab_size=test_env.num_actions)
         agent = Agent(tokenizer, world_model, actor_critic).to(device)
         agent.load(Path('checkpoints/last.pt'), device)        
diff --git a/src/trainer.py b/src/trainer.py
index af0460c..c337d1f 100644
--- a/src/trainer.py
+++ b/src/trainer.py
@@ -22,6 +22,7 @@ from src.make_reconstructions import make_reconstructions_from_batch
 from src.models.actor_critic import ActorCritic
 from src.models.world_model import WorldModel
 from src.utils import configure_optimizer, EpisodeDirManager, set_seed
+from src.models.tokenizer import Tokenizer
 
 
 class Trainer:
@@ -80,8 +81,10 @@ class Trainer:
         assert self.cfg.training.should or self.cfg.evaluation.should
         env = train_env if self.cfg.training.should else test_env
 
-        tokenizer = instantiate(cfg.tokenizer)
-        world_model = WorldModel(obs_vocab_size=tokenizer.vocab_size, act_vocab_size=env.num_actions, config=instantiate(cfg.world_model))
+        # tokenizer = instantiate(cfg.tokenizer)
+        world_model = WorldModel(obs_vocab_size=cfg.tokenizer.vocab_size, act_vocab_size=env.num_actions, config=instantiate(cfg.world_model))
+        embedding = world_model.transformer.embedding
+        tokenizer = Tokenizer(embedding=embedding, **cfg.tokenizer.embedding)
         actor_critic = ActorCritic(**cfg.actor_critic, act_vocab_size=env.num_actions)
         self.agent = Agent(tokenizer, world_model, actor_critic).to(self.device)
         print(f'{sum(p.numel() for p in self.agent.tokenizer.parameters())} parameters in agent.tokenizer')

From 328d28165134d48c195c0ef1c24678caea90ec57 Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Sun, 19 Nov 2023 10:50:48 +0800
Subject: [PATCH 20/30] using frozen transformer embedding: runs

- use frozen transformer encoder where possible (obs, worldmodel, tokenizer, etc)
- it's frozen
- a huge vocab
---
 config/tokenizer/default.yaml     |  6 +--
 config/world_model/default.yaml   | 11 ++---
 research_journal.md               |  3 ++
 src/models/tokenizer/tokenizer.py |  6 +--
 src/models/transformer.py         | 29 ++++++-----
 src/models/world_model.py         | 23 ++++++---
 src/play.py                       | 82 +++++++++++++++++++++++--------
 src/trainer.py                    | 11 +++--
 8 files changed, 116 insertions(+), 55 deletions(-)

diff --git a/config/tokenizer/default.yaml b/config/tokenizer/default.yaml
index 2df2b4d..9029ef4 100644
--- a/config/tokenizer/default.yaml
+++ b/config/tokenizer/default.yaml
@@ -1,14 +1,14 @@
 _target_: src.models.tokenizer.Tokenizer
 
-vocab_size: 32000 # change to llm vocab dim
-embed_dim: 2048  # change this to whatever the embedding dimension is in your pretrained llm 2048 for llama. 2560 for stablelm
+vocab_size: ${..world_model.vocab_size}
+embed_dim: ${..world_model.embed_dim}
 encoder:
   _target_: src.models.tokenizer.Encoder
   config:
     _target_: src.models.tokenizer.EncoderDecoderConfig
     resolution: 64
     in_channels: 3
-    z_channels: 32000
+    z_channels: ${...vocab_size}
     ch: 64
     ch_mult: [1, 1, 1, 1, 1]
     num_res_blocks: 2
diff --git a/config/world_model/default.yaml b/config/world_model/default.yaml
index 76894ac..2b631a9 100644
--- a/config/world_model/default.yaml
+++ b/config/world_model/default.yaml
@@ -1,9 +1,8 @@
 _target_: src.models.TransformerConfig
 max_blocks: 10  # this is the rollout length when training policy
-num_layers: 1
-num_heads: 1
-embed_dim: ${..tokenizer.embed_dim}
-dropout: 0.1
-model_name: "PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T"
-rank: 32
 tokens_per_block: 17 # how much info we can encode
+dropout: 0.1
+rank: 32 # lora rank
+model_name: "PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T"
+vocab_size: 32000 # change to llm vocab dim
+embed_dim: 2048  # change this to whatever the embedding dimension is in your pretrained llm 2048 for llama. 2560 for stablelm
diff --git a/research_journal.md b/research_journal.md
index d7bbb80..fd40272 100644
--- a/research_journal.md
+++ b/research_journal.md
@@ -354,3 +354,6 @@ idea
   - yes I am bypassing it by passing in the input_embeds... but maybe I shouldn't
 - [x] use same embedding everywhere. e.g. model embedding in encoder decoder?
   - Our embedings is (embed_tokens): Embedding(32000, 2048). So we would need to encode to 32000!
+
+
+ok we need to freeze it, and change dtype
diff --git a/src/models/tokenizer/tokenizer.py b/src/models/tokenizer/tokenizer.py
index 332c1fe..2504b42 100644
--- a/src/models/tokenizer/tokenizer.py
+++ b/src/models/tokenizer/tokenizer.py
@@ -23,15 +23,15 @@ class TokenizerEncoderOutput:
 
 
 class Tokenizer(nn.Module):
-    def __init__(self, embedding, vocab_size: int, embed_dim: int, encoder: Encoder, decoder: Decoder, with_lpips: bool = True) -> None:
+    def __init__(self, transformer_embedding: nn.Embedding, vocab_size: int, embed_dim: int, encoder: Encoder, decoder: Decoder, with_lpips: bool = True) -> None:
         super().__init__()
         self.vocab_size = vocab_size
         self.encoder = encoder
         self.pre_quant_conv = torch.nn.Conv2d(encoder.config.z_channels, embed_dim, 1)
-        self.embedding = embedding # nn.Embedding(vocab_size, embed_dim) # TODO: use model embed?
+        self.embedding = transformer_embedding  # pretrained transformer embedding
         self.post_quant_conv = torch.nn.Conv2d(embed_dim, decoder.config.z_channels, 1)
         self.decoder = decoder
-        self.embedding.weight.data.uniform_(-1.0 / vocab_size, 1.0 / vocab_size)
+        # self.embedding.weight.data.uniform_(-1.0 / vocab_size, 1.0 / vocab_size)
         self.lpips = LPIPS().eval() if with_lpips else None
 
     def __repr__(self) -> str:
diff --git a/src/models/transformer.py b/src/models/transformer.py
index 89a2b25..22a949a 100644
--- a/src/models/transformer.py
+++ b/src/models/transformer.py
@@ -16,23 +16,27 @@ from .kv_caching import KeysValues, KVCache
 
 @dataclass
 class TransformerConfig:
-
-    max_blocks: int
-
-    num_layers: int
-    num_heads: int
-    embed_dim: int
-    tokens_per_block: int
-    
     # model_name: str = "stabilityai/stablelm-3b-4e1t"
     # https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T
+    vocab_size: int = 32000
+    embed_dim: int = 2048
+    
+    max_blocks: int = 20
+    tokens_per_block: int = 17
+    
     model_name: str = "PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T"
-    dropout: float = 0
+    dropout: float = 0.1
     rank: int = 32
 
     @property
     def max_tokens(self):
         return self.tokens_per_block * self.max_blocks
+    
+    
+def freeze(n: nn.Module):
+    for p in n.parameters():
+        p.requires_grad = False
+    return n
 
 
 class Transformer(nn.Module):
@@ -41,13 +45,14 @@ class Transformer(nn.Module):
         self.config = config
         self.model = load_pretrained_model(config)
         self.ln_f = nn.Linear(self.model.config.vocab_size, config.embed_dim)
+        self.embedding = freeze(self.model.base_model.model.model.embed_tokens.to(torch.float)) # HACK: custom path to embeddings layer for model
 
     def generate_empty_keys_values(self, n: int, max_tokens: int) -> KeysValues:
         device = self.ln_f.weight.device  # Assumption that all submodules are on the same device
-        return KeysValues(n, self.config.num_heads, max_tokens, self.config.embed_dim, self.config.num_layers, device)
+        return KeysValues(n, 1, max_tokens, self.config.embed_dim, 1, device)
 
     def forward(self, sequences: torch.Tensor, past_keys_values: Optional[KeysValues] = None) -> torch.Tensor:
-        assert past_keys_values is None or len(past_keys_values) == self.config.num_layers
+        assert past_keys_values is None or len(past_keys_values) == 1
         with torch.cuda.amp.autocast(dtype=torch.bfloat16):
             outputs = self.model(
                 inputs_embeds=sequences,
@@ -60,8 +65,6 @@ class Transformer(nn.Module):
         # fake it, since it's used to keep track of steps
         if past_keys_values is not None:
             k_size = past_keys_values[0]._k_cache._cache.size()
-            # k_size = (x.shape[0], x.shape[1], x.shape[1], 1)
-            # v_size = past_keys_values[0]._v_cache._cache.size()
             v_size = (k_size[0], k_size[1], x.shape[1], k_size[3])
             past_keys_values[0].update(torch.rand(v_size), torch.rand(v_size))
         return x
diff --git a/src/models/world_model.py b/src/models/world_model.py
index cb3e798..9999ff4 100644
--- a/src/models/world_model.py
+++ b/src/models/world_model.py
@@ -33,20 +33,26 @@ class WorldModel(nn.Module):
         act_tokens_pattern = torch.zeros(self.config.tokens_per_block)
         act_tokens_pattern[-1] = 1
         obs_tokens_pattern = 1 - act_tokens_pattern
+        
+        self.transformer = Transformer(config)
+        transformer_embedding = self.transformer.embedding
 
         self.pos_emb = nn.Embedding(config.max_tokens, config.embed_dim)
+        self.act_emb = nn.Embedding(act_vocab_size, config.embed_dim)
 
         self.embedder = Embedder(
             max_blocks=config.max_blocks,
             block_masks=[act_tokens_pattern, obs_tokens_pattern],
-            embedding_tables=nn.ModuleList([nn.Embedding(act_vocab_size, config.embed_dim), nn.Embedding(obs_vocab_size, config.embed_dim)])
+            embedding_tables=nn.ModuleList([self.act_emb, transformer_embedding])
         )
+        
+        # why have this? Well I worry that the transformer can't adapt, since so much is frozen
         self.post_embed = nn.Sequential(
             nn.Linear(config.embed_dim, config.embed_dim),
             nn.ReLU(),
             nn.Linear(config.embed_dim, config.embed_dim),
-            nn.ReLU(),
-            nn.Linear(config.embed_dim, config.embed_dim)
+            # nn.ReLU(),
+            # nn.Linear(config.embed_dim, config.embed_dim)
         )
 
         self.head_observations = Head(
@@ -79,9 +85,15 @@ class WorldModel(nn.Module):
             )
         )
 
-        self.apply(init_weights)
+        # don't apply to transformer or transformer/obs embeddings
+        self.act_emb.apply(init_weights)
+        self.pos_emb.apply(init_weights)
+        self.post_embed.apply(init_weights)
+        self.head_observations.apply(init_weights)
+        self.head_rewards.apply(init_weights)
+        self.head_ends.apply(init_weights)
+        
         
-        self.transformer = Transformer(config)
 
     def __repr__(self) -> str:
         return "world_model"
@@ -92,7 +104,6 @@ class WorldModel(nn.Module):
         assert num_steps <= self.config.max_tokens
         prev_steps = 0 if past_keys_values is None else past_keys_values.size
 
-        # TODO: replace wth model embedder?
         sequences = self.embedder(tokens, num_steps, prev_steps) + self.pos_emb(prev_steps + torch.arange(num_steps, device=tokens.device))
         # [batch=8, num_steps=170, embed_size=2048]
         sequences = self.post_embed(sequences)
diff --git a/src/play.py b/src/play.py
index 28c3f07..e3486a5 100644
--- a/src/play.py
+++ b/src/play.py
@@ -1,4 +1,4 @@
-from functools import partial 
+from functools import partial
 from pathlib import Path
 
 import hydra
@@ -11,51 +11,91 @@ from src.envs import SingleProcessEnv, WorldModelEnv
 from src.game import AgentEnv, EpisodeReplayEnv, Game
 from src.models.actor_critic import ActorCritic
 from src.models.world_model import WorldModel
+from src.models.tokenizer import Tokenizer
 
 
 @hydra.main(config_path="../config", config_name="trainer")
 def main(cfg: DictConfig):
     device = torch.device(cfg.common.device)
-    assert cfg.mode in ('episode_replay', 'agent_in_env', 'agent_in_world_model', 'play_in_world_model')
+    assert cfg.mode in (
+        "episode_replay",
+        "agent_in_env",
+        "agent_in_world_model",
+        "play_in_world_model",
+    )
 
     env_fn = partial(instantiate, config=cfg.env.test)
     test_env = SingleProcessEnv(env_fn)
 
-    if cfg.mode.startswith('agent_in_'):
+    if cfg.mode.startswith("agent_in_"):
         h, w, _ = test_env.env.unwrapped.observation_space.shape
     else:
         h, w = 64, 64
     multiplier = 800 // h
     size = [h * multiplier, w * multiplier]
-    
-    if cfg.mode == 'episode_replay':
-        env = EpisodeReplayEnv(replay_keymap_name=cfg.env.keymap, episode_dir=Path('media/episodes'))
-        keymap = 'episode_replay'
+
+    if cfg.mode == "episode_replay":
+        env = EpisodeReplayEnv(
+            replay_keymap_name=cfg.env.keymap, episode_dir=Path("media/episodes")
+        )
+        keymap = "episode_replay"
 
     else:
         # tokenizer = instantiate(cfg.tokenizer)
-        world_model = WorldModel(obs_vocab_size=cfg.tokenizer.vocab_size, act_vocab_size=test_env.num_actions, config=instantiate(cfg.world_model))
-        tokenizer = Tokenizer(embedding=embedding, **cfg.tokenizer.embedding)
-        actor_critic = ActorCritic(**cfg.actor_critic, act_vocab_size=test_env.num_actions)
+        world_model = WorldModel(
+            obs_vocab_size=cfg.tokenizer.vocab_size,
+            act_vocab_size=test_env.num_actions,
+            config=instantiate(cfg.world_model),
+        )
+        transformer_embedding = world_model.transformer.embedding
+        tokenizer = Tokenizer(
+            transformer_embedding=transformer_embedding,
+            vocab_size=cfg.tokenizer.vocab_size,
+            embed_dim=cfg.tokenizer.embed_dim,
+            encoder=instantiate(cfg.tokenizer.encoder),
+            decoder=instantiate(cfg.tokenizer.decoder),
+        )
+        actor_critic = ActorCritic(
+            **cfg.actor_critic, act_vocab_size=test_env.num_actions
+        )
         agent = Agent(tokenizer, world_model, actor_critic).to(device)
-        agent.load(Path('checkpoints/last.pt'), device)        
+        agent.load(Path("checkpoints/last.pt"), device)
 
-        if cfg.mode == 'play_in_world_model':
-            env = WorldModelEnv(tokenizer=agent.tokenizer, world_model=agent.world_model, device=device, env=env_fn())
+        if cfg.mode == "play_in_world_model":
+            env = WorldModelEnv(
+                tokenizer=agent.tokenizer,
+                world_model=agent.world_model,
+                device=device,
+                env=env_fn(),
+            )
             keymap = cfg.env.keymap
-        
-        elif cfg.mode == 'agent_in_env':
-            env = AgentEnv(agent, test_env, cfg.env.keymap, do_reconstruction=cfg.reconstruction)
-            keymap = 'empty'
+
+        elif cfg.mode == "agent_in_env":
+            env = AgentEnv(
+                agent, test_env, cfg.env.keymap, do_reconstruction=cfg.reconstruction
+            )
+            keymap = "empty"
             if cfg.reconstruction:
                 size[1] *= 3
 
-        elif cfg.mode == 'agent_in_world_model':
-            wm_env = WorldModelEnv(tokenizer=agent.tokenizer, world_model=agent.world_model, device=device, env=env_fn())
+        elif cfg.mode == "agent_in_world_model":
+            wm_env = WorldModelEnv(
+                tokenizer=agent.tokenizer,
+                world_model=agent.world_model,
+                device=device,
+                env=env_fn(),
+            )
             env = AgentEnv(agent, wm_env, cfg.env.keymap, do_reconstruction=False)
-            keymap = 'empty'
+            keymap = "empty"
 
-    game = Game(env, keymap_name=keymap, size=size, fps=cfg.fps, verbose=bool(cfg.header), record_mode=bool(cfg.save_mode))
+    game = Game(
+        env,
+        keymap_name=keymap,
+        size=size,
+        fps=cfg.fps,
+        verbose=bool(cfg.header),
+        record_mode=bool(cfg.save_mode),
+    )
     game.run()
 
 
diff --git a/src/trainer.py b/src/trainer.py
index c337d1f..2b4e1b5 100644
--- a/src/trainer.py
+++ b/src/trainer.py
@@ -81,10 +81,15 @@ class Trainer:
         assert self.cfg.training.should or self.cfg.evaluation.should
         env = train_env if self.cfg.training.should else test_env
 
-        # tokenizer = instantiate(cfg.tokenizer)
         world_model = WorldModel(obs_vocab_size=cfg.tokenizer.vocab_size, act_vocab_size=env.num_actions, config=instantiate(cfg.world_model))
-        embedding = world_model.transformer.embedding
-        tokenizer = Tokenizer(embedding=embedding, **cfg.tokenizer.embedding)
+        transformer_embedding = world_model.transformer.embedding
+        tokenizer = Tokenizer(
+            transformer_embedding=transformer_embedding,
+            vocab_size=cfg.tokenizer.vocab_size,
+            embed_dim=cfg.tokenizer.embed_dim,
+            encoder=instantiate(cfg.tokenizer.encoder),
+            decoder=instantiate(cfg.tokenizer.decoder),
+        )
         actor_critic = ActorCritic(**cfg.actor_critic, act_vocab_size=env.num_actions)
         self.agent = Agent(tokenizer, world_model, actor_critic).to(self.device)
         print(f'{sum(p.numel() for p in self.agent.tokenizer.parameters())} parameters in agent.tokenizer')

From ecee11fc33834c8785d911eaf5b7a572bf57dd1a Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Sun, 19 Nov 2023 10:58:45 +0800
Subject: [PATCH 21/30] notes

---
 src/models/tokenizer/tokenizer.py | 2 ++
 src/models/world_model.py         | 1 +
 2 files changed, 3 insertions(+)

diff --git a/src/models/tokenizer/tokenizer.py b/src/models/tokenizer/tokenizer.py
index 2504b42..a96ec82 100644
--- a/src/models/tokenizer/tokenizer.py
+++ b/src/models/tokenizer/tokenizer.py
@@ -46,6 +46,8 @@ class Tokenizer(nn.Module):
     def compute_loss(self, batch: Batch, **kwargs: Any) -> LossWithIntermediateLosses:
         assert self.lpips is not None
         observations = self.preprocess_input(rearrange(batch['observations'], 'b t c h w -> (b t) c h w'))
+        # TODO: in the delta-IRIS paper (https://openreview.net/forum?id=o8IDoZggqO) they encode(x0, a0, x1) -> z1 and decode(x0, a0, z1). In esense the tokens only need to encode the change
+        # note they also do dynamics(x0, a0, z1) -> z2. decode(x1, a1, z2) -> x2
         z, z_quantized, reconstructions = self(observations, should_preprocess=False, should_postprocess=False)
 
         # Codebook loss. Notes:
diff --git a/src/models/world_model.py b/src/models/world_model.py
index 9999ff4..b346094 100644
--- a/src/models/world_model.py
+++ b/src/models/world_model.py
@@ -47,6 +47,7 @@ class WorldModel(nn.Module):
         )
         
         # why have this? Well I worry that the transformer can't adapt, since so much is frozen
+        # TODO: If I get the dynamics model working, maybe try without it
         self.post_embed = nn.Sequential(
             nn.Linear(config.embed_dim, config.embed_dim),
             nn.ReLU(),

From 6eb25883f6a4f3d84893aa0d395ee8d8690bd996 Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Sun, 19 Nov 2023 14:50:54 +0800
Subject: [PATCH 22/30] misc

---
 .vscode/launch.json | 4 ++--
 research_journal.md | 7 ++++---
 scripts/play.sh     | 3 ++-
 src/trainer.py      | 2 +-
 4 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/.vscode/launch.json b/.vscode/launch.json
index dbe115f..4554801 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -19,8 +19,8 @@
                 "env.train.id=CrafterReward-v1",
                 // # make it start early
                 "training.tokenizer.start_after_epochs=1",
-                "training.world_model.start_after_epochs=1",
-                "training.actor_critic.start_after_epochs=1",
+                "training.world_model.start_after_epochs=2",
+                "training.actor_critic.start_after_epochs=3",
                 "training.tokenizer.steps_per_epoch=10",
                 "training.world_model.steps_per_epoch=10",
                 "training.actor_critic.steps_per_epoch=10",
diff --git a/research_journal.md b/research_journal.md
index fd40272..eac579f 100644
--- a/research_journal.md
+++ b/research_journal.md
@@ -345,9 +345,6 @@ Why is it not learning? It's because the dynamics model is total BS!!!
 # 2023-11-19 06:45:34
 
 So I tried just trainign the world model for 200 epochs. And with a post_embedding layer. It helped the flickering. But not enougth to actually go for the obvious local minima of the next state equals the last
-
-
-
   
 idea
 - bypass embedding?, but wait dreamerv3 needed quant z... 
@@ -357,3 +354,7 @@ idea
 
 
 ok we need to freeze it, and change dtype
+OK it seems slightly better yay! Lets train it overnight and see
+
+next idea is to the delta-IRIS thing where the tokens only have to encode the diff(obs)
+
diff --git a/scripts/play.sh b/scripts/play.sh
index 967db60..45fed21 100755
--- a/scripts/play.sh
+++ b/scripts/play.sh
@@ -27,6 +27,7 @@ while [ "$1" != "" ]; do
         -e | --episode )
             mode="episode_replay"
             ;; # replay train, test, or imagined episodes. shows quality of dynamics model
+            # this is quick low resource way to check the dynamics model and agent while training
         -w | --world-model )
             mode="play_in_world_model"
             ;; # human plays in world model
@@ -37,4 +38,4 @@ while [ "$1" != "" ]; do
     shift
 done
 
-python -m pdb src/play.py hydra.run.dir=. hydra.output_subdir=null +mode="${mode}" +fps="${fps}" +header="${header}" +reconstruction="${reconstruction}" +save_mode="${save_mode}"
+python src/play.py hydra.run.dir=. hydra.output_subdir=null +mode="${mode}" +fps="${fps}" +header="${header}" +reconstruction="${reconstruction}" +save_mode="${save_mode}"
diff --git a/src/trainer.py b/src/trainer.py
index 2b4e1b5..6c3b8f0 100644
--- a/src/trainer.py
+++ b/src/trainer.py
@@ -200,7 +200,7 @@ class Trainer:
         if epoch > cfg_world_model.start_after_epochs:
             metrics_world_model = self.eval_component(self.agent.world_model, cfg_world_model.batch_num_samples, sequence_length=self.cfg.common.sequence_length, tokenizer=self.agent.tokenizer)
 
-        if epoch > cfg_actor_critic.start_after_epochs:
+        if epoch > cfg_world_model.start_after_epochs:
             self.inspect_imagination(epoch)
 
         if cfg_tokenizer.save_reconstructions:

From 6308397bb773cc1daa11a0418c0a41e49d4329b5 Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Mon, 20 Nov 2023 07:14:50 +0800
Subject: [PATCH 23/30] it works better but still flickers when imagining. hmm
 I do think I need the delta-iris stuff

---
 research_journal.md         | 30 ++++++++++++++++++++++++++++++
 src/agent.py                |  1 +
 src/envs/world_model_env.py |  1 +
 src/models/actor_critic.py  |  4 +---
 4 files changed, 33 insertions(+), 3 deletions(-)

diff --git a/research_journal.md b/research_journal.md
index eac579f..492e009 100644
--- a/research_journal.md
+++ b/research_journal.md
@@ -358,3 +358,33 @@ OK it seems slightly better yay! Lets train it overnight and see
 
 next idea is to the delta-IRIS thing where the tokens only have to encode the diff(obs)
 
+# 2023-11-19 16:50:45 
+
+Seems to be working! Now let's plan delta-IRIS
+
+
+So IRIS has
+- Encoder $E(x_0, a_0) = t_0$
+  ```py
+  obs_tokens = self.tokenizer.encode(observations, should_preprocess=True).tokens    # (B, C, H, W) -> (B, K)
+  ```
+- Embed $Emb(t_0) = z_0$
+    ```py
+    embedded_tokens = self.tokenizer.embedding(self.obs_tokens)     # (B, K, E)
+    z = rearrange(embedded_tokens, 'b (h w) e -> b e h w', h=int(np.sqrt(self.num_observations_tokens)))
+    ```
+- Dynamics $D(z_0, a_0) = z_1$
+    ```py
+    outputs_wm = self.world_model(token, past_keys_values=self.keys_values_wm)
+    ```
+- Decoder $D(z_0, a_0) = x_1$
+    ```py
+    rec = self.tokenizer.decode(z, should_postprocess=True)         # (B, C, H, W)
+    ```
+
+
+but we have tokens vs z
+
+Questions:
+- wait why are we just passing in "action_token" to the transformer and not obs? that must have obs in it right... right??? confirm
+- in iris-delta how did they pass everything in? I guess obs_prev was tokenized too? I think the slices are annoying so maybe I should just pass things seperatly
diff --git a/src/agent.py b/src/agent.py
index 5534232..ee884fd 100644
--- a/src/agent.py
+++ b/src/agent.py
@@ -34,4 +34,5 @@ class Agent(nn.Module):
         input_ac = obs if self.actor_critic.use_original_obs else torch.clamp(self.tokenizer.encode_decode(obs, should_preprocess=True, should_postprocess=True), 0, 1)
         logits_actions = self.actor_critic(input_ac).logits_actions[:, -1] / temperature
         act_token = Categorical(logits=logits_actions).sample() if should_sample else logits_actions.argmax(dim=-1)
+        # FIXME, is this really just an action and doesn't have an obs in?
         return act_token
diff --git a/src/envs/world_model_env.py b/src/envs/world_model_env.py
index 73e3852..4e447d4 100644
--- a/src/envs/world_model_env.py
+++ b/src/envs/world_model_env.py
@@ -69,6 +69,7 @@ class WorldModelEnv:
 
         for k in range(num_passes):  # assumption that there is only one action token.
 
+            # FIXME: hold on we are ONLY passing in the action token! should it not be obs too
             outputs_wm = self.world_model(token, past_keys_values=self.keys_values_wm)
             output_sequence.append(outputs_wm.output_sequence)
             if k == 0:
diff --git a/src/models/actor_critic.py b/src/models/actor_critic.py
index ec1256d..4d68c78 100644
--- a/src/models/actor_critic.py
+++ b/src/models/actor_critic.py
@@ -149,9 +149,7 @@ class ActorCritic(nn.Module):
             outputs_ac = self(obs)
             action_token = Categorical(logits=outputs_ac.logits_actions).sample()
             
-            # TODO this is really slow, I guess we need grad? does it help to put it in eval? no
-            # wm_env.world_model.eval()
-            
+            # FIXME shouldn't we pass in obs too?            
             obs, reward, done, _ = wm_env.step(action_token, should_predict_next_obs=(k < horizon - 1))
 
             all_actions.append(action_token)

From 56daac9761e6d3e7b16ec4bac2747d0018823ea6 Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Mon, 20 Nov 2023 07:31:31 +0800
Subject: [PATCH 24/30] rank, and unfreeze head

---
 src/models/transformer.py | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/src/models/transformer.py b/src/models/transformer.py
index 22a949a..6237c57 100644
--- a/src/models/transformer.py
+++ b/src/models/transformer.py
@@ -10,6 +10,7 @@ from einops import rearrange
 import torch
 import torch.nn as nn
 from torch.nn import functional as F
+from loguru import logger
 
 from .kv_caching import KeysValues, KVCache
 
@@ -98,8 +99,9 @@ def load_pretrained_model(config, device="cuda:0"):
         peft.TaskType.CAUSAL_LM,
         inference_mode=False,
         r=config.rank,
-        lora_alpha=8,
+        lora_alpha=config.rank*2, # Adjusting the LoRA rank is essential, and so is selecting an apt alpha value. A good heuristic is setting alpha at twice the rank's value. https://magazine.sebastianraschka.com/p/practical-tips-for-finetuning-llms
         lora_dropout=config.dropout,
+        # TODO: If you're incorporating LoRA, ensure it's applied across all layers, not just to the Key and Value matrices, to maximize model performance.
         target_modules=[
             "self_attn.q_proj",
             "self_attn.k_proj",
@@ -108,13 +110,19 @@ def load_pretrained_model(config, device="cuda:0"):
             "mlp.gate_proj",
             "mlp.up_proj",
             "mlp.down_proj",
+            # "wte", "embed_tokens",
+            "lm_head",
         ],
+        bias=True,
+        # tune the embedding layer and prediction head
+        modules_to_save = ["lm_head", "embed_tokens"],
     )
     base_model_peft = peft.get_peft_model(base_model, peft_config)
-    base_model_peft.add_adapter("dynamics", peft_config)
-    base_model_peft.set_adapter("dynamics")
-    print(base_model_peft.print_trainable_parameters())
+    base_model_peft.add_adapter(adapter_name="dynamics", peft_config=peft_config) # make an adapter
+    base_model_peft.set_adapter("dynamics") # use an adapter
     disable_causal_mask_always()
+    print(base_model_peft.print_trainable_parameters())
+    logger.info(f"loaded model {base_model_peft}")
     return base_model_peft
 
 @contextmanager

From 2763449f6400d95c30a228ed578e8f639efd386f Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Mon, 20 Nov 2023 10:01:56 +0800
Subject: [PATCH 25/30] bug fix

---
 config/trainer.yaml       |  2 +-
 src/models/transformer.py | 11 +++++------
 src/utils.py              |  4 +++-
 3 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/config/trainer.yaml b/config/trainer.yaml
index b4fe7c5..c52d41c 100644
--- a/config/trainer.yaml
+++ b/config/trainer.yaml
@@ -61,7 +61,7 @@ training:
     steps_per_epoch: 200
   world_model:
     batch_num_samples: 8  # pretrained models use lots of ram
-    grad_acc_steps: 1
+    grad_acc_steps: 2
     max_grad_norm: 10.0
     weight_decay: 0.01
     start_after_epochs: 25
diff --git a/src/models/transformer.py b/src/models/transformer.py
index 6237c57..cc91255 100644
--- a/src/models/transformer.py
+++ b/src/models/transformer.py
@@ -111,18 +111,17 @@ def load_pretrained_model(config, device="cuda:0"):
             "mlp.up_proj",
             "mlp.down_proj",
             # "wte", "embed_tokens",
-            "lm_head",
+            # "lm_head",
         ],
-        bias=True,
+        # bias="lora_only",
         # tune the embedding layer and prediction head
-        modules_to_save = ["lm_head", "embed_tokens"],
+        modules_to_save = ["lm_head",], # we want the classifier parameters to be trained too when fine-tuning the base model on our custom dataset. To ensure that the classifier parameters are also trained, we specify modules_to_save. 
     )
     base_model_peft = peft.get_peft_model(base_model, peft_config)
-    base_model_peft.add_adapter(adapter_name="dynamics", peft_config=peft_config) # make an adapter
-    base_model_peft.set_adapter("dynamics") # use an adapter
+    base_model_peft.add_adapter(adapter_name="dynamics", peft_config=peft_config) # make and set an adapter
     disable_causal_mask_always()
     print(base_model_peft.print_trainable_parameters())
-    logger.info(f"loaded model {base_model_peft}")
+    logger.debug(f"loaded model {base_model_peft}")
     return base_model_peft
 
 @contextmanager
diff --git a/src/utils.py b/src/utils.py
index d64273d..07ba101 100644
--- a/src/utils.py
+++ b/src/utils.py
@@ -3,7 +3,7 @@ import cv2
 from pathlib import Path
 import random
 import shutil
-
+from loguru import logger
 import numpy as np
 import torch
 import torch.nn as nn
@@ -15,6 +15,7 @@ from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
 
 def configure_optimizer(model, learning_rate, weight_decay, *blacklist_module_names):
     """Credits to https://github.com/karpathy/minGPT"""
+    # FIXME: check this is still good for LoRA
     # separate out all parameters to those that will and won't experience regularizing weight decay
     decay = set()
     no_decay = set()
@@ -39,6 +40,7 @@ def configure_optimizer(model, learning_rate, weight_decay, *blacklist_module_na
     param_dict = {pn: p for pn, p in model.named_parameters()}
     inter_params = decay & no_decay
     union_params = decay | no_decay
+    logger.debug(f"decay {decay} no_decay {no_decay}")
     assert len(inter_params) == 0, f"parameters {str(inter_params)} made it into both decay/no_decay sets!"
     assert len(param_dict.keys() - union_params) == 0, f"parameters {str(param_dict.keys() - union_params)} were not separated into either decay/no_decay set!"
 

From 72ee8066011849cdb0aa3adda35afa7d6e76fc7a Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Thu, 23 Nov 2023 09:08:10 +0800
Subject: [PATCH 26/30] try training tokenizer and world model together: result
 :poop:

---
 justfile                  |  8 ++++++
 src/models/world_model.py | 10 +++++---
 src/trainer.py            |  6 ++++-
 src/utils.py              | 53 ++++++++++++++++++++++++---------------
 4 files changed, 52 insertions(+), 25 deletions(-)

diff --git a/justfile b/justfile
index 1f26170..5f3c137 100644
--- a/justfile
+++ b/justfile
@@ -1,3 +1,4 @@
+set shell := ["zsh", "-cu"]
 
 breakout:
     python src/main.py env.train.id=BreakoutNoFrameskip-v4 
@@ -8,3 +9,10 @@ crafter:
 minihack:
     python src/main.py env.train.id=MiniHack-River-v0
 
+# watch the latest runs
+watch_latest:
+    . ./.venv/bin/activate
+    cd ./outputs && \
+    cd *([-1]) && \
+    cd *([-1]) && \
+    scripts/play.sh -e -r -h
diff --git a/src/models/world_model.py b/src/models/world_model.py
index b346094..42bbf81 100644
--- a/src/models/world_model.py
+++ b/src/models/world_model.py
@@ -40,6 +40,7 @@ class WorldModel(nn.Module):
         self.pos_emb = nn.Embedding(config.max_tokens, config.embed_dim)
         self.act_emb = nn.Embedding(act_vocab_size, config.embed_dim)
 
+        # FIXME: having slices is unclear. maybe it's better just to have obs and action embeddings?
         self.embedder = Embedder(
             max_blocks=config.max_blocks,
             block_masks=[act_tokens_pattern, obs_tokens_pattern],
@@ -101,7 +102,7 @@ class WorldModel(nn.Module):
 
     def forward(self, tokens: torch.LongTensor, past_keys_values: Optional[KeysValues] = None) -> WorldModelOutput:
 
-        num_steps = tokens.size(1)  # (B, T)
+        num_steps = tokens.size(1)  # (B=8, T=170) where often the last 10 are actons
         assert num_steps <= self.config.max_tokens
         prev_steps = 0 if past_keys_values is None else past_keys_values.size
 
@@ -118,12 +119,13 @@ class WorldModel(nn.Module):
 
     def compute_loss(self, batch: Batch, tokenizer: Tokenizer, **kwargs: Any) -> LossWithIntermediateLosses:
 
-        with torch.no_grad():
-            # [B=8, S=10, Colors=3, H=64, W=64] -> [B=8, S=10, 16]
-            obs_tokens = tokenizer.encode(batch['observations'], should_preprocess=True).tokens  # (BL, K)
+        # with torch.no_grad():
+        # [B=8, S=10, Colors=3, H=64, W=64] -> [B=8, S=10, 16]
+        obs_tokens = tokenizer.encode(batch['observations'], should_preprocess=True).tokens  # (BL, K)
 
         act_tokens = rearrange(batch['actions'], 'b l -> b l 1')
         tokens = rearrange(torch.cat((obs_tokens, act_tokens), dim=2), 'b l k1 -> b (l k1)')  # (B, L(K+1))
+        # So first 10 are observation, the last 10 tokens are actions
 
         outputs = self(tokens)
 
diff --git a/src/trainer.py b/src/trainer.py
index 6c3b8f0..30d0c83 100644
--- a/src/trainer.py
+++ b/src/trainer.py
@@ -97,7 +97,11 @@ class Trainer:
         print(f'{sum(p.numel() for p in self.agent.actor_critic.parameters())} parameters in agent.actor_critic')
 
         self.optimizer_tokenizer = torch.optim.Adam(self.agent.tokenizer.parameters(), lr=cfg.training.learning_rate)
-        self.optimizer_world_model = configure_optimizer(self.agent.world_model, cfg.training.learning_rate, cfg.training.world_model.weight_decay)
+        # self.optimizer_world_model = configure_optimizer([self.agent.tokenizer, self.agent.world_model], cfg.training.learning_rate, cfg.training.world_model.weight_decay)
+        self.optimizer_world_model = torch.optim.Adam(
+            list(self.agent.tokenizer.parameters())+list(self.agent.world_model.parameters()),
+            lr=cfg.training.learning_rate
+        )
         self.optimizer_actor_critic = torch.optim.Adam(self.agent.actor_critic.parameters(), lr=cfg.training.learning_rate)
 
         if cfg.initialization.path_to_checkpoint is not None:
diff --git a/src/utils.py b/src/utils.py
index 07ba101..0f09590 100644
--- a/src/utils.py
+++ b/src/utils.py
@@ -13,41 +13,54 @@ from src.episode import Episode
 from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
 
 
-def configure_optimizer(model, learning_rate, weight_decay, *blacklist_module_names):
+def configure_optimizer(models, learning_rate, weight_decay, *blacklist_module_names):
     """Credits to https://github.com/karpathy/minGPT"""
     # FIXME: check this is still good for LoRA
     # separate out all parameters to those that will and won't experience regularizing weight decay
     decay = set()
     no_decay = set()
+    decay_params = []
+    no_decay_params = []
+    param_dict = {}
     whitelist_weight_modules = (torch.nn.Linear, torch.nn.Conv1d)
     blacklist_weight_modules = tuple(ALL_LAYERNORM_LAYERS+[torch.nn.Embedding])
-    for mn, m in model.named_modules():
-        for pn, p in m.named_parameters():
-            fpn = '%s.%s' % (mn, pn) if mn else pn  # full param name
-            if any([fpn.startswith(module_name) for module_name in blacklist_module_names]):
-                no_decay.add(fpn)
-            elif 'bias' in pn:
-                # all biases will not be decayed
-                no_decay.add(fpn)
-            elif pn.endswith('weight') and isinstance(m, whitelist_weight_modules):
-                # weights of whitelist modules will be weight decayed
-                decay.add(fpn)
-            elif pn.endswith('weight') and isinstance(m, blacklist_weight_modules):
-                # weights of blacklist modules will NOT be weight decayed
-                no_decay.add(fpn)
+    for model in models:
+        for mn, m in model.named_modules():
+            for pn, p in m.named_parameters():
+                fpn = '%s.%s' % (mn, pn) if mn else pn  # full param name
+                if any([fpn.startswith(module_name) for module_name in blacklist_module_names]):
+                    no_decay.add(fpn)
+                    no_decay_params.append(p)
+                elif 'bias' in pn:
+                    # all biases will not be decayed
+                    no_decay.add(fpn)
+                    no_decay_params.append(p)
+                elif pn.endswith('weight') and isinstance(m, whitelist_weight_modules):
+                    # weights of whitelist modules will be weight decayed
+                    decay.add(fpn)
+                    decay_params.append(p)
+                elif pn.endswith('weight') and isinstance(m, blacklist_weight_modules):
+                    # weights of blacklist modules will NOT be weight decayed
+                    no_decay.add(fpn)
+                    no_decay_params.append(p)
+                else:
+                    logger.warning(f"Parameter {fpn} of module {mn} not handled!")
+                    # raise NotImplementedError(f"Parameter {fpn} of module {m} not handled!")
+                    decay.add(fpn)
+                    decay_params.append(p)
 
-    # validate that we considered every parameter
-    param_dict = {pn: p for pn, p in model.named_parameters()}
+        # validate that we considered every parameter
+        param_dict.update({pn: p for pn, p in model.named_parameters()})
     inter_params = decay & no_decay
     union_params = decay | no_decay
-    logger.debug(f"decay {decay} no_decay {no_decay}")
+    # logger.debug(f"decay {decay} no_decay {no_decay}")
     assert len(inter_params) == 0, f"parameters {str(inter_params)} made it into both decay/no_decay sets!"
     assert len(param_dict.keys() - union_params) == 0, f"parameters {str(param_dict.keys() - union_params)} were not separated into either decay/no_decay set!"
 
     # create the pytorch optimizer object
     optim_groups = [
-        {"params": [param_dict[pn] for pn in sorted(list(decay))], "weight_decay": weight_decay},
-        {"params": [param_dict[pn] for pn in sorted(list(no_decay))], "weight_decay": 0.0},
+        {"params": no_decay_params, "weight_decay": weight_decay},
+        {"params": decay_params, "weight_decay": 0.0},
     ]
     optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate)
     return optimizer

From c3878e4a4c02bc3b59edd92bb5e819366005f3a6 Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Thu, 23 Nov 2023 09:08:30 +0800
Subject: [PATCH 27/30] full finetune?

---
 src/models/transformer.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/src/models/transformer.py b/src/models/transformer.py
index cc91255..71dc34c 100644
--- a/src/models/transformer.py
+++ b/src/models/transformer.py
@@ -117,10 +117,11 @@ def load_pretrained_model(config, device="cuda:0"):
         # tune the embedding layer and prediction head
         modules_to_save = ["lm_head",], # we want the classifier parameters to be trained too when fine-tuning the base model on our custom dataset. To ensure that the classifier parameters are also trained, we specify modules_to_save. 
     )
-    base_model_peft = peft.get_peft_model(base_model, peft_config)
-    base_model_peft.add_adapter(adapter_name="dynamics", peft_config=peft_config) # make and set an adapter
+    base_model_peft = base_model
+    # base_model_peft = peft.get_peft_model(base_model, peft_config)
+    # base_model_peft.add_adapter(adapter_name="dynamics", peft_config=peft_config) # make and set an adapter
     disable_causal_mask_always()
-    print(base_model_peft.print_trainable_parameters())
+    # print(base_model_peft.print_trainable_parameters())
     logger.debug(f"loaded model {base_model_peft}")
     return base_model_peft
 

From 9155ecca95d7483e158844ca1fb26bb08ac8ce54 Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Fri, 24 Nov 2023 09:24:12 +0800
Subject: [PATCH 28/30] Fix embedding path in Transformer model

---
 src/models/transformer.py | 2 +-
 src/trainer.py            | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/models/transformer.py b/src/models/transformer.py
index 71dc34c..4f67824 100644
--- a/src/models/transformer.py
+++ b/src/models/transformer.py
@@ -46,7 +46,7 @@ class Transformer(nn.Module):
         self.config = config
         self.model = load_pretrained_model(config)
         self.ln_f = nn.Linear(self.model.config.vocab_size, config.embed_dim)
-        self.embedding = freeze(self.model.base_model.model.model.embed_tokens.to(torch.float)) # HACK: custom path to embeddings layer for model
+        self.embedding = freeze(self.model.base_model.embed_tokens.to(torch.float)) # HACK: custom path to embeddings layer for model
 
     def generate_empty_keys_values(self, n: int, max_tokens: int) -> KeysValues:
         device = self.ln_f.weight.device  # Assumption that all submodules are on the same device
diff --git a/src/trainer.py b/src/trainer.py
index 30d0c83..e73699a 100644
--- a/src/trainer.py
+++ b/src/trainer.py
@@ -149,11 +149,11 @@ class Trainer:
 
         if epoch > cfg_tokenizer.start_after_epochs:
             metrics_tokenizer = self.train_component(self.agent.tokenizer, self.optimizer_tokenizer, sequence_length=1, sample_from_start=True, **cfg_tokenizer)
+        self.agent.tokenizer.eval()
 
         if epoch > cfg_world_model.start_after_epochs:
             metrics_world_model = self.train_component(self.agent.world_model, self.optimizer_world_model, sequence_length=self.cfg.common.sequence_length, sample_from_start=True, tokenizer=self.agent.tokenizer, **cfg_world_model)
         self.agent.world_model.eval()
-        self.agent.tokenizer.eval()
 
         if epoch > cfg_actor_critic.start_after_epochs:
             metrics_actor_critic = self.train_component(self.agent.actor_critic, self.optimizer_actor_critic, sequence_length=1 + self.cfg.training.actor_critic.burn_in, sample_from_start=False, tokenizer=self.agent.tokenizer, world_model=self.agent.world_model, **cfg_actor_critic)

From 8ecdf35bd36b55c5a7b48b05fea6ae50085682b0 Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Sat, 25 Nov 2023 05:48:17 +0800
Subject: [PATCH 29/30] learns the best yet

---
 justfile            |  3 +++
 research_journal.md | 12 ++++++++++++
 src/trainer.py      |  2 +-
 3 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/justfile b/justfile
index 5f3c137..4c75710 100644
--- a/justfile
+++ b/justfile
@@ -16,3 +16,6 @@ watch_latest:
     cd *([-1]) && \
     cd *([-1]) && \
     scripts/play.sh -e -r -h
+
+default: 
+    just --list
diff --git a/research_journal.md b/research_journal.md
index 492e009..ed75ea4 100644
--- a/research_journal.md
+++ b/research_journal.md
@@ -388,3 +388,15 @@ but we have tokens vs z
 Questions:
 - wait why are we just passing in "action_token" to the transformer and not obs? that must have obs in it right... right??? confirm
 - in iris-delta how did they pass everything in? I guess obs_prev was tokenized too? I think the slices are annoying so maybe I should just pass things seperatly
+
+# 2023-11-24 10:56:40
+
+If I unfreeze the whole transformer, it seem to learn the most obvious dynamics (the next latent space is the same as the last). 
+
+To summarize
+- with Qlora it didn't learn that
+- with unfrozen head it didn't
+- when training transformer and obs embedding together it did not (frozen llm embeddings)
+
+
+no it didn't work with tokenizer sep hmm
diff --git a/src/trainer.py b/src/trainer.py
index e73699a..c3d6399 100644
--- a/src/trainer.py
+++ b/src/trainer.py
@@ -149,10 +149,10 @@ class Trainer:
 
         if epoch > cfg_tokenizer.start_after_epochs:
             metrics_tokenizer = self.train_component(self.agent.tokenizer, self.optimizer_tokenizer, sequence_length=1, sample_from_start=True, **cfg_tokenizer)
-        self.agent.tokenizer.eval()
 
         if epoch > cfg_world_model.start_after_epochs:
             metrics_world_model = self.train_component(self.agent.world_model, self.optimizer_world_model, sequence_length=self.cfg.common.sequence_length, sample_from_start=True, tokenizer=self.agent.tokenizer, **cfg_world_model)
+        self.agent.tokenizer.eval()
         self.agent.world_model.eval()
 
         if epoch > cfg_actor_critic.start_after_epochs:

From a82fe96158cc9122f9f63439271d1aa0d4780bc6 Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Sat, 27 Apr 2024 07:01:09 +0800
Subject: [PATCH 30/30] left overs

---
 config/trainer.yaml |  2 +-
 justfile            | 12 ++++++++++--
 research_journal.md |  7 ++++++-
 3 files changed, 17 insertions(+), 4 deletions(-)

diff --git a/config/trainer.yaml b/config/trainer.yaml
index c52d41c..3539b2e 100644
--- a/config/trainer.yaml
+++ b/config/trainer.yaml
@@ -70,7 +70,7 @@ training:
     batch_num_samples: 16
     grad_acc_steps: 1
     max_grad_norm: 10.0
-    start_after_epochs: 1500
+    start_after_epochs: 300
     steps_per_epoch: 40
     imagine_horizon: ${common.sequence_length}
     burn_in: 20
diff --git a/justfile b/justfile
index 4c75710..5f2f184 100644
--- a/justfile
+++ b/justfile
@@ -6,8 +6,8 @@ breakout:
 crafter:
     python src/main.py env.train.id=CrafterReward-v1
 
-minihack:
-    python src/main.py env.train.id=MiniHack-River-v0
+# minihack:
+#     python src/main.py env.train.id=MiniHack-River-v0
 
 # watch the latest runs
 watch_latest:
@@ -17,5 +17,13 @@ watch_latest:
     cd *([-1]) && \
     scripts/play.sh -e -r -h
 
+
+resume_latest:
+    . ./.venv/bin/activate
+    cd ./outputs && \
+    cd *([-1]) && \
+    cd *([-1]) && \
+    scripts/resume.sh
+
 default: 
     just --list
diff --git a/research_journal.md b/research_journal.md
index ed75ea4..b3478be 100644
--- a/research_journal.md
+++ b/research_journal.md
@@ -375,7 +375,7 @@ So IRIS has
     ```
 - Dynamics $D(z_0, a_0) = z_1$
     ```py
-    outputs_wm = self.world_model(token, past_keys_values=self.keys_values_wm)
+    outputs_wm = self.world_model(tokenRedmond AI, past_keys_values=self.keys_values_wm)
     ```
 - Decoder $D(z_0, a_0) = x_1$
     ```py
@@ -400,3 +400,8 @@ To summarize
 
 
 no it didn't work with tokenizer sep hmm
+
+Oh it did with whole transfrmer and tokenizer at same time https://wandb.ai/wassname/iris/runs/w7lvs4gi?workspace=user-wassname
+wandb:           world_model/eval/loss_obs ▇█▃▄▄▄▄▃▃▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:       world_model/eval/loss_rewards ▁█▃█▇▅▇▁▅▆▂▄▃▇▆▃▄▇▃▄▂▄▃▂▃▄▃
+wandb:         world_model/eval/total_loss ▂█▃██▅▇▁▅▆▁▃▂█▇▃▃▆▃▄▂▃▃▂▂▄▂