diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..b346ef8
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,151 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+# For a library or package, you might want to ignore these files since the code is
+# intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# local
+jobs/
+local/
+.vscode/
+
+data/
+*.model
+*.npy
+*.jsonl
+*.pkl
+*.json
+__pycache__/
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..261eeb9
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,201 @@
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000..cfac740
--- /dev/null
+++ b/README.md
@@ -0,0 +1,140 @@
+# Large World Model (LWM)
+
+[[Project]](https://largeworldmodel.github.io/)
+[[Paper]](https://arxiv.org/abs/2402.08268)
+[[Models]](https://huggingface.co/LargeWorldModel)
+
+**Large World Model (LWM)** is a general-purpose large-context multimodal autoregressive model. It is trained on a large dataset of diverse long videos and books using RingAttention, and can perform language, image, and video understanding and generation.
+
+
+## Approach
+
+
+
+
+
+
+ LWM can retrieval facts across 1M context with high accuracy.
+
+
+
+
+
+
+ LWM can answer questions over 1 hour YouTube video.
+
+
+
+
+
+
+ LWM can chat with images.
+
+
+
+
+
+
+ LWM can generate videos and images from text.
+
+
",
+ eos_token="",
+ sp_model_kwargs: Optional[Dict[str, Any]] = None,
+ add_bos_token=False,
+ add_eos_token=False,
+ **kwargs,
+ ):
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+ super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, **kwargs)
+ self.vocab_file = vocab_file
+ self.add_bos_token = add_bos_token
+ self.add_eos_token = add_eos_token
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+
+ with tempfile.NamedTemporaryFile() as tfile:
+ with open_file(self.vocab_file, 'rb') as fin:
+ tfile.write(fin.read())
+ tfile.flush()
+ tfile.seek(0)
+ self.sp_model.Load(tfile.name)
+ """ Initialisation"""
+ self.add_special_tokens(dict(
+ unk_token=unk_token,
+ bos_token=bos_token,
+ eos_token=eos_token,
+ ))
+ self.pad_token_id = self.unk_token_id
+
+ @property
+ def vocab_size(self):
+ """Returns vocab size"""
+ return self.sp_model.get_piece_size()
+
+ @property
+ def bos_token_id(self) -> Optional[int]:
+ return self.sp_model.bos_id()
+
+ @property
+ def eos_token_id(self) -> Optional[int]:
+ return self.sp_model.eos_id()
+
+ def get_vocab(self):
+ """Returns vocab as a dict"""
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+ vocab.update(self.added_tokens_encoder)
+ return vocab
+
+ def _tokenize(self, text):
+ """Returns a tokenized string."""
+ return self.sp_model.encode(text, out_type=str)
+
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ return self.sp_model.piece_to_id(token)
+
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ token = self.sp_model.IdToPiece(index)
+ return token
+
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (string) in a single string."""
+ current_sub_tokens = []
+ out_string = ""
+ prev_is_special = False
+ for token in tokens:
+ # make sure that special tokens are not decoded using sentencepiece model
+ if token in self.all_special_tokens:
+ if not prev_is_special:
+ out_string += " "
+ out_string += self.sp_model.decode(current_sub_tokens) + token
+ prev_is_special = True
+ current_sub_tokens = []
+ else:
+ current_sub_tokens.append(token)
+ prev_is_special = False
+ out_string += self.sp_model.decode(current_sub_tokens)
+ return out_string.strip()
+
+ def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
+ """
+ Save the vocabulary and special tokens file to a directory.
+ Args:
+ save_directory (`str`):
+ The directory in which to save the vocabulary.
+ Returns:
+ `Tuple(str)`: Paths to the files saved.
+ """
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+ out_vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+ copyfile(self.vocab_file, out_vocab_file)
+ elif not os.path.isfile(self.vocab_file):
+ with open(out_vocab_file, "wb") as fi:
+ content_spiece_model = self.sp_model.serialized_model_proto()
+ fi.write(content_spiece_model)
+
+ return (out_vocab_file,)
+
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+ if self.add_bos_token:
+ bos_token_ids = [self.bos_token_id]
+ else:
+ bos_token_ids = []
+
+ output = bos_token_ids + token_ids_0
+
+ if token_ids_1 is not None:
+ output = output + token_ids_1
+
+ if self.add_eos_token:
+ output = output + [self.eos_token_id]
+
+ return output
+
+ def get_special_tokens_mask(
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+ ) -> List[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+ Args:
+ token_ids_0 (`List[int]`):
+ List of IDs.
+ token_ids_1 (`List[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+ Returns:
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ if token_ids_1 is None:
+ return [1] + ([0] * len(token_ids_0)) + [1]
+ return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+ ) -> List[int]:
+ """
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
+ use of token type ids, therefore a list of zeros is returned.
+ Args:
+ token_ids_0 (`List[int]`):
+ List of IDs.
+ token_ids_1 (`List[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ Returns:
+ `List[int]`: List of zeros.
+ """
+ eos = [self.eos_token_id]
+
+ if token_ids_1 is None:
+ return len(token_ids_0 + eos) * [0]
+ return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
diff --git a/lwm/ring_attention.py b/lwm/ring_attention.py
new file mode 100644
index 0000000..a5d38a1
--- /dev/null
+++ b/lwm/ring_attention.py
@@ -0,0 +1,1989 @@
+"""This module contains ring attention forward and backward pass, supporting both blockwise computation and TPU-compatible fused attention.
+It features blockwise computation for feedforward networks to reduce memory cost.
+For more details, refer to 'RingAttention' at https://arxiv.org/abs/2305.19370 and 'Blockwise Parallel Transformers' at https://arxiv.org/abs/2310.01889.
+"""
+
+import numpy as np
+import flax.linen as nn
+import jax
+import jax.lax as lax
+import jax.numpy as jnp
+from jax.experimental import pallas as pl
+from jax.experimental.pallas import tpu as pltpu
+from einops import rearrange
+from functools import partial
+import dataclasses
+import functools
+from typing import Any, NamedTuple
+
+
+def _ring_attention_fwd(q, k, v, attn_bias, segment_ids, axis_name, float32_logits, blockwise_kwargs):
+ if float32_logits:
+ q, k = q.astype(jnp.float32), k.astype(jnp.float32)
+ batch, q_len, num_heads, dim_per_head = q.shape
+ batch, kv_len, num_heads, dim_per_head = k.shape
+ numerator = jnp.zeros((batch, q_len, num_heads, dim_per_head)).astype(q.dtype)
+ denominator = jnp.zeros((batch, num_heads, q_len)).astype(q.dtype)
+ axis_size = lax.psum(1, axis_name)
+ q_block_size, kv_block_size = q_len, kv_len # assumes this function is pre-sharded inside shard_map
+ query_chunk_size = blockwise_kwargs["query_chunk_size"]
+ key_chunk_size = blockwise_kwargs["key_chunk_size"]
+
+ def scan_kv_block(carry, idx):
+ prev_max_score, numerator, denominator, k, v = carry
+ q_block_idx = lax.axis_index(axis_name)
+ k_block_idx = (lax.axis_index(axis_name) - idx) % axis_size
+ q_chunk_idx_start = q_block_idx * (q_block_size // query_chunk_size)
+ k_chunk_idx_start = k_block_idx * (kv_block_size // key_chunk_size)
+ numerator, denominator, max_score = _blockwise_attention_fwd(q, k, v, (numerator, denominator, prev_max_score), q_chunk_idx_start, k_chunk_idx_start, bias=attn_bias, segment_ids=segment_ids, **blockwise_kwargs)
+ k, v = map(lambda x: lax.ppermute(x, axis_name, perm=[(i, (i + 1) % axis_size) for i in range(axis_size)]), (k, v))
+ return (max_score, numerator, denominator, k, v), None
+ prev_max_score = jnp.full((batch, num_heads, q_len), -jnp.inf).astype(q.dtype)
+ (max_score, numerator, denominator, _, _), _ = lax.scan(scan_kv_block,
+ init=(prev_max_score, numerator, denominator, k, v), xs=jnp.arange(0, axis_size))
+ output = numerator / rearrange(denominator, 'b h q -> b q h')[..., None]
+ return output.astype(v.dtype), (output, q, k, v, attn_bias, segment_ids, denominator, max_score)
+
+def _ring_attention_bwd(axis_name, float32_logits, blockwise_kwargs, res, g):
+ del float32_logits
+ output, q, k, v, attn_bias, segment_ids, denominator, max_score = res
+ batch, q_len, num_heads, dim_per_head = q.shape
+ batch, kv_len, num_heads, dim_per_head = k.shape
+ axis_size = lax.psum(1, axis_name)
+ dq = jnp.zeros_like(q, dtype=q.dtype)
+ dk = jnp.zeros_like(k, dtype=k.dtype)
+ dv = jnp.zeros_like(v, dtype=k.dtype)
+ query_chunk_size = blockwise_kwargs["query_chunk_size"]
+ key_chunk_size = blockwise_kwargs["key_chunk_size"]
+ q_block_size, kv_block_size = q_len, kv_len # assumes this function is pre-sharded inside shard_map
+ def scan_kv_block(carry, idx):
+ dq, dk, dv, k, v = carry
+ q_block_idx = lax.axis_index(axis_name)
+ k_block_idx = (lax.axis_index(axis_name) - idx) % axis_size
+ q_chunk_idx_start = q_block_idx * (q_block_size // query_chunk_size)
+ k_chunk_idx_start = k_block_idx * (kv_block_size // key_chunk_size)
+ dq, dk, dv = _blockwise_attention_bwd(q, k, v, g, (dq, dk, dv, output, denominator, max_score), q_chunk_idx_start, k_chunk_idx_start, bias=attn_bias, segment_ids=segment_ids, **blockwise_kwargs)
+ k, v, dk, dv = map(lambda x: lax.ppermute(x, axis_name, perm=[(i,
+ (i + 1) % axis_size) for i in range(axis_size)]), (k, v, dk, dv))
+ return (dq, dk, dv, k, v), None
+ (dq, dk, dv, k, v), _ = lax.scan(scan_kv_block, init=(dq, dk, dv, k, v), xs=jnp.arange(0, axis_size))
+ dq, dk, dv = dq.astype(q.dtype), dk.astype(k.dtype), dv.astype(k.dtype)
+ return dq, dk, dv, None, None
+
+@partial(jax.custom_vjp, nondiff_argnums=[5, 6, 7])
+def ring_attention(q, k, v, attn_bias, segment_ids, axis_name, float32_logits, blockwise_kwargs):
+ y, _ = _ring_attention_fwd(q, k, v, attn_bias, segment_ids, axis_name, float32_logits, blockwise_kwargs)
+ return y
+
+ring_attention.defvjp(_ring_attention_fwd, _ring_attention_bwd)
+
+
+def _ring_attention_standard_fwd(q, k, v, attn_mask, axis_name, float32_logits):
+ if float32_logits:
+ q, k = q.astype(jnp.float32), k.astype(jnp.float32)
+ batch, q_len, num_heads, _ = q.shape
+ batch, kv_len, num_heads, dim_per_head = k.shape
+ numerator = jnp.zeros((batch, q_len, num_heads, dim_per_head)).astype(q.dtype)
+ denominator = jnp.zeros((batch, num_heads, q_len)).astype(q.dtype)
+ axis_size = lax.psum(1, axis_name)
+ scale = jnp.sqrt(q.shape[-1])
+ def scan_kv_block(carry, idx):
+ prev_max_score, numerator, denominator, k, v = carry
+ mask = lax.dynamic_slice_in_dim(attn_mask,
+ (lax.axis_index(axis_name) - idx) % axis_size * kv_len, kv_len, axis=-1)
+ attn_weights = jnp.einsum("bqhd,bkhd->bhqk", q, k) / scale
+ attn_weights = jnp.where(mask, attn_weights, jnp.finfo(attn_weights.dtype).min)
+ max_score = jnp.maximum(prev_max_score, jnp.max(attn_weights, axis=-1))
+ exp_weights = jnp.exp(attn_weights - max_score[..., None])
+ correction = rearrange(jnp.exp(prev_max_score - max_score), 'b h q -> b q h')[..., None]
+ numerator = numerator * correction + jnp.einsum("bhqk,bkhd->bqhd", exp_weights, v)
+ denominator = denominator * jnp.exp(prev_max_score - max_score) + jnp.sum(exp_weights, axis=-1)
+ k, v = map(lambda x: lax.ppermute(x, axis_name, perm=[(i,
+ (i + 1) % axis_size) for i in range(axis_size)]), (k, v))
+ return (max_score, numerator, denominator, k, v), None
+ prev_max_score = jnp.full((batch, num_heads, q_len), -jnp.inf).astype(q.dtype)
+ (max_score, numerator, denominator, _, _), _ = lax.scan(scan_kv_block,
+ init=(prev_max_score, numerator, denominator, k, v), xs=jnp.arange(0, axis_size))
+ output = numerator / rearrange(denominator, 'b h q -> b q h')[..., None]
+ return output.astype(v.dtype), (output, q, k, v, attn_mask, numerator, denominator, max_score)
+
+def _ring_attention_standard_bwd(axis_name, float32_logits, res, g):
+ del float32_logits
+ axis_size = lax.psum(1, axis_name)
+ output, q, k, v, attn_mask, numerator, denominator, max_score = res
+ dq = jnp.zeros_like(q, dtype=jnp.float32)
+ dk = jnp.zeros_like(k, dtype=jnp.float32)
+ dv = jnp.zeros_like(v, dtype=jnp.float32)
+ batch, kv_len, num_heads, dim_per_head = k.shape
+ scale = jnp.sqrt(q.shape[-1])
+ def scan_kv_block(carry, idx):
+ dq, dk, dv, k, v = carry
+ mask = lax.dynamic_slice_in_dim(attn_mask,
+ (lax.axis_index(axis_name) - idx) % axis_size * kv_len, kv_len, axis=-1)
+ attn_weights = jnp.einsum("bqhd,bkhd->bhqk", q, k) / scale
+ attn_weights = jnp.where(mask, attn_weights, jnp.finfo(attn_weights.dtype).min)
+ exp_weights = jnp.exp(attn_weights - max_score[..., None]) / denominator[..., None]
+ ds = jnp.einsum("bqhd,bkhd->bhqk", g, v)
+ dl = (ds - jnp.einsum("bqhd,bqhd->bhq", g, output)[..., None]) * exp_weights
+ dq = dq + jnp.einsum("bhqk,bkhd->bqhd", dl, k) / scale
+ dk = dk + jnp.einsum("bqhd,bhqk->bkhd", q, dl) / scale
+ dv = dv + jnp.einsum("bhqk,bqhd->bkhd", exp_weights, g)
+ k, v, dk, dv = map(lambda x: lax.ppermute(x, axis_name, perm=[(i,
+ (i + 1) % axis_size) for i in range(axis_size)]), (k, v, dk, dv))
+ return (dq, dk, dv, k, v), None
+ (dq, dk, dv, k, v), _ = lax.scan(scan_kv_block, init=(dq, dk, dv, k, v), xs=jnp.arange(0, axis_size))
+ dq, dk, dv = dq.astype(q.dtype), dk.astype(k.dtype), dv.astype(v.dtype)
+ return dq, dk, dv, None
+
+@partial(jax.custom_vjp, nondiff_argnums=[4, 5])
+def ring_attention_standard(q, k, v, attn_mask, axis_name, float32_logits=True):
+ y, _ = _ring_attention_standard_fwd(q, k, v, attn_mask, axis_name, float32_logits)
+ return y
+
+ring_attention_standard.defvjp(_ring_attention_standard_fwd, _ring_attention_standard_bwd)
+
+
+def _blockwise_attention_fwd(q, k, v, carry, q_chunk_idx_start, k_chunk_idx_start, bias, segment_ids, causal, query_chunk_size,
+ key_chunk_size, deterministic, dropout_rng, attn_pdrop, dtype, policy, precision, prevent_cse):
+ batch, q_len, num_heads, dim_per_head = q.shape
+ batch, kv_len, num_heads, dim_per_head = k.shape
+ batch, kv_len, num_heads, dim_per_head = v.shape
+ num_q = q_len // query_chunk_size
+ num_kv = kv_len // key_chunk_size
+ q = q.reshape((batch, num_q, query_chunk_size, num_heads, dim_per_head))
+ k = k.reshape((batch, num_kv, key_chunk_size, num_heads, dim_per_head))
+ v = v.reshape((batch, num_kv, key_chunk_size, num_heads, dim_per_head))
+ q, k, v = map(lambda x: jnp.moveaxis(x, 1, 0), (q, k, v))
+
+ numerator, denominator, max_score = carry
+ numerator = numerator.reshape((batch, num_q, query_chunk_size, num_heads, dim_per_head))
+ numerator = jnp.moveaxis(numerator, 1, 0)
+ denominator = denominator.reshape((batch, num_heads, num_q, query_chunk_size))
+ max_score = max_score.reshape((batch, num_heads, num_q, query_chunk_size))
+ denominator, max_score = map(lambda x: rearrange(x, 'b h n c -> n b h c'), (denominator, max_score))
+
+ scale = jnp.sqrt(q.shape[-1])
+ if not deterministic and attn_pdrop > 0.0:
+ attn_dropout_rng, dropout_rng = jax.random.split(dropout_rng)
+ attn_dropout = jax.random.bernoulli(attn_dropout_rng, attn_pdrop, (batch, num_heads, q_len, kv_len))
+ else:
+ attn_dropout = None
+ _chunk_bias_fn = partial(
+ _chunk_attention_bias,
+ query_chunk_size, key_chunk_size, bias, segment_ids, deterministic,
+ attn_dropout, attn_pdrop, causal, dtype)
+ def scan_attention(_, scan):
+ q_chunk, numerator_chunk, denominator_chunk, max_score_chunk, q_chunk_idx = scan
+ @partial(jax.checkpoint, prevent_cse=prevent_cse, policy=policy)
+ def scan_kv_block(carry, scan):
+ k_chunk, value_chunk, k_chunk_idx = scan
+ numerator_chunk, denominator_chunk, prev_max_score_chunk = carry
+ attn_weights = jnp.einsum('bqhd,bkhd->bhqk', q_chunk, k_chunk, precision=precision) / scale
+ bias_chunk = _chunk_bias_fn(q_chunk_idx_start + q_chunk_idx, k_chunk_idx_start + k_chunk_idx)
+ attn_weights = attn_weights + bias_chunk
+
+ max_score_chunk = jnp.maximum(prev_max_score_chunk, jnp.max(attn_weights, axis=-1))
+ max_score_chunk = lax.stop_gradient(max_score_chunk)
+ exp_weights = jnp.exp(attn_weights - max_score_chunk[..., None])
+ exp_values = jnp.einsum('bhqk,bkhd->bqhd', exp_weights, value_chunk, precision=precision)
+ correction = rearrange(jnp.exp(prev_max_score_chunk - max_score_chunk), 'b h q -> b q h')[..., None]
+ numerator_chunk = numerator_chunk * correction + exp_values
+ denominator_chunk = denominator_chunk * jnp.exp(prev_max_score_chunk - max_score_chunk) + exp_weights.sum(axis=-1)
+ return (numerator_chunk, denominator_chunk, max_score_chunk), None
+
+ def skip_upper_half(carry, args):
+ key_chunk, value_chunk, k_chunk_idx = args
+ skip_block = jnp.array(False)
+ if causal:
+ skip_block = q_chunk_idx_start + q_chunk_idx < k_chunk_idx_start + k_chunk_idx
+ return jax.lax.cond(
+ skip_block,
+ lambda carry, args: (carry, None),
+ scan_kv_block,
+ carry,
+ args
+ )
+
+ (numerator_chunk, denominator_chunk, max_score_chunk), _ = lax.scan(
+ skip_upper_half, init=(numerator_chunk, denominator_chunk, max_score_chunk), xs=(k, v, jnp.arange(0, num_kv))
+ )
+ output_chunk = numerator_chunk / rearrange(denominator_chunk, 'b h q -> b q h')[..., None].astype(dtype)
+ return (), (output_chunk, numerator_chunk, denominator_chunk, max_score_chunk)
+ _, (_, numerator, denominator, max_score) = lax.scan(scan_attention, init=(), xs=(q, numerator, denominator, max_score, jnp.arange(0, num_q)))
+
+ numerator = jnp.moveaxis(numerator, 1, 0)
+ numerator = numerator.reshape((batch, q_len, num_heads, dim_per_head))
+ denominator, max_score = map(lambda x: rearrange(x, 'n b h c -> b h n c'), (denominator, max_score))
+ denominator = denominator.reshape((batch, num_heads, q_len))
+ max_score = max_score.reshape((batch, num_heads, q_len))
+
+ return numerator, denominator, max_score
+
+def _blockwise_attention_bwd(q, k, v, g, carry, q_chunk_idx_start, k_chunk_idx_start, bias, segment_ids, causal, query_chunk_size, key_chunk_size, deterministic, dropout_rng, attn_pdrop, dtype, policy, precision, prevent_cse):
+ batch, q_len, num_heads, dim_per_head = q.shape
+ batch, kv_len, num_heads, dim_per_head = k.shape
+ batch, kv_len, num_heads, dim_per_head = v.shape
+ num_q = q_len // query_chunk_size
+ num_kv = kv_len // key_chunk_size
+ dq, dk, dv, output, denominator, max_score = carry
+
+ g = g.reshape((batch, num_q, query_chunk_size, num_heads, dim_per_head))
+ dq = dq.reshape((batch, num_q, query_chunk_size, num_heads, dim_per_head))
+ dk = dk.reshape((batch, num_kv, key_chunk_size, num_heads, dim_per_head))
+ dv = dv.reshape((batch, num_kv, key_chunk_size, num_heads, dim_per_head))
+ output = output.reshape((batch, num_q, query_chunk_size, num_heads, dim_per_head))
+ g, dq, dk, dv, output = map(lambda x: jnp.moveaxis(x, 1, 0), (g, dq, dk, dv, output))
+
+ denominator = denominator.reshape((batch, num_heads, num_q, query_chunk_size))
+ max_score = max_score.reshape((batch, num_heads, num_q, query_chunk_size))
+ denominator, max_score = map(lambda x: rearrange(x, 'b h n c -> n b h c'), (denominator, max_score))
+
+ q = q.reshape((batch, num_q, query_chunk_size, num_heads, dim_per_head))
+ k = k.reshape((batch, num_kv, key_chunk_size, num_heads, dim_per_head))
+ v = v.reshape((batch, num_kv, key_chunk_size, num_heads, dim_per_head))
+ q, k, v = map(lambda x: jnp.moveaxis(x, 1, 0), (q, k, v))
+
+ scale = jnp.sqrt(q.shape[-1])
+ if not deterministic and attn_pdrop > 0.0:
+ attn_dropout_rng, dropout_rng = jax.random.split(dropout_rng)
+ attn_dropout = jax.random.bernoulli(attn_dropout_rng, attn_pdrop, (batch, num_heads, q_len, kv_len))
+ else:
+ attn_dropout = None
+ _chunk_bias_fn = partial(
+ _chunk_attention_bias,
+ query_chunk_size, key_chunk_size, bias, segment_ids, deterministic,
+ attn_dropout, attn_pdrop, causal, dtype)
+ def scan_attention(carry, scan):
+ dk, dv = carry
+ q_chunk, dq_chunk, g_chunk, output_chunk, denominator_chunk, max_score_chunk, q_chunk_idx = scan
+ dl_part = jnp.einsum("bqhd,bqhd->bhq", g_chunk, output_chunk)[..., None]
+ @partial(jax.checkpoint, prevent_cse=prevent_cse, policy=policy)
+ def scan_kv_block(carry, scan):
+ k_chunk, value_chunk, k_chunk_idx = scan
+ dq_chunk = carry
+ attn_weights = jnp.einsum('bqhd,bkhd->bhqk', q_chunk, k_chunk, precision=precision) / scale
+ bias_chunk = _chunk_bias_fn(q_chunk_idx_start + q_chunk_idx, k_chunk_idx_start + k_chunk_idx)
+ attn_weights = attn_weights + bias_chunk
+ exp_weights = jnp.exp(attn_weights - max_score_chunk[..., None]) / denominator_chunk[..., None]
+
+ ds = jnp.einsum("bqhd,bkhd->bhqk", g_chunk, value_chunk)
+ dl = (ds - dl_part) * exp_weights
+ dq_chunk = dq_chunk + jnp.einsum("bhqk,bkhd->bqhd", dl, k_chunk) / scale
+ dk_chunk = jnp.einsum("bqhd,bhqk->bkhd", q_chunk, dl) / scale
+ dv_chunk = jnp.einsum("bhqk,bqhd->bkhd", exp_weights, g_chunk)
+ return dq_chunk, (dk_chunk, dv_chunk)
+
+ def skip_upper_half(carry, args):
+ key_chunk, value_chunk, k_chunk_idx = args
+ skip_block = jnp.array(False)
+ if causal:
+ skip_block = q_chunk_idx_start + q_chunk_idx < k_chunk_idx_start + k_chunk_idx
+ return lax.cond(
+ skip_block,
+ lambda carry, args: (
+ carry, (
+ jnp.zeros((batch, key_chunk_size, num_heads, dim_per_head), dtype=dk.dtype),
+ jnp.zeros((batch, key_chunk_size, num_heads, dim_per_head), dtype=dk.dtype),
+ )
+ ),
+ scan_kv_block,
+ carry,
+ args
+ )
+
+ dq_chunk, (dk_part, dv_part) = lax.scan(
+ skip_upper_half, init=dq_chunk, xs=(k, v, jnp.arange(0, num_kv))
+ )
+ return (dk + dk_part, dv + dv_part), dq_chunk
+ (dk, dv), dq = lax.scan(scan_attention, init=(dk, dv), xs=(q, dq, g, output, denominator, max_score, jnp.arange(0, num_q)))
+
+ dq, dk, dv = map(lambda x: jnp.moveaxis(x, 1, 0), (dq, dk, dv))
+ dq = dq.reshape((batch, q_len, num_heads, dim_per_head))
+ dk = dk.reshape((batch, kv_len, num_heads, dim_per_head))
+ dv = dv.reshape((batch, kv_len, num_heads, dim_per_head))
+
+ return dq, dk, dv
+
+
+# Blockwise feedforward network for memory-efficient training
+def blockwise_ffn(remat_ffn, inputs, chunk_size, deterministic):
+ inputs = rearrange(inputs, 'b (c n) d -> b c n d', c=chunk_size)
+ def scan_ffn(remat_ffn, carry, hidden_states):
+ outputs = remat_ffn(hidden_states, deterministic=deterministic)
+ return carry, outputs
+ scan_axis = inputs.ndim - 2
+ _, output = nn.scan(
+ scan_ffn,
+ variable_broadcast="params",
+ split_rngs={"params": False, "dropout": True},
+ in_axes=scan_axis,
+ out_axes=scan_axis,
+ )(remat_ffn, None, inputs)
+ output = rearrange(output, 'b c n d -> b (c n) d')
+ return output
+
+
+def _chunk_attention_bias(query_chunk_size, key_chunk_size,
+ bias, segment_ids, deterministic, attn_dropout, attn_pdrop, causal,
+ dtype, query_chunk_idx, key_chunk_idx):
+ query_offset = query_chunk_idx * query_chunk_size
+ key_offset = key_chunk_idx * key_chunk_size
+ chunk_bias = jnp.zeros((1, 1, 1, 1), dtype=dtype)
+ if bias is not None:
+ chunk_bias = lax.dynamic_slice(
+ bias,
+ start_indices=(0, 0, 0, key_offset),
+ slice_sizes=(*bias.shape[:2], min(bias.shape[-2], query_chunk_size), min(bias.shape[-1], key_chunk_size)),
+ )
+
+ if segment_ids is not None:
+ q_segment_ids = lax.dynamic_slice(
+ segment_ids,
+ start_indices=(0, query_offset),
+ slice_sizes=(segment_ids.shape[0], query_chunk_size)
+ )
+ k_segment_ids = lax.dynamic_slice(
+ segment_ids,
+ start_indices=(0, key_offset),
+ slice_sizes=(segment_ids.shape[0], key_chunk_size)
+ )
+ segment_ids_mask = q_segment_ids[:, :, None] != k_segment_ids[:, None, :]
+ segment_ids_mask = segment_ids_mask[:, None] # B1QK
+ segment_ids_bias = segment_ids_mask * jnp.finfo(dtype).min
+ chunk_bias += segment_ids_bias
+
+ if causal:
+ query_idx = lax.broadcasted_iota(dtype=jnp.int32, shape=(query_chunk_size, 1), dimension=0)
+ key_idx = lax.broadcasted_iota(dtype=jnp.int32, shape=(1, key_chunk_size), dimension=1)
+ offset = query_offset - key_offset
+ query_idx += offset
+ causal_mask_value = (query_idx < key_idx) * jnp.finfo(dtype).min
+ chunk_bias += causal_mask_value.reshape(1, 1, *causal_mask_value.shape)
+
+ if not deterministic and attn_pdrop > 0.0:
+ attn_dropout_slice = lax.dynamic_slice(
+ attn_dropout,
+ start_indices=(0, 0, query_offset, key_offset),
+ slice_sizes=(
+ *attn_dropout.shape[:2],
+ min(attn_dropout.shape[-2], query_chunk_size),
+ min(attn_dropout.shape[-1], key_chunk_size),
+ ),
+ )
+ chunk_bias += attn_dropout_slice * jnp.finfo(dtype).min
+ return chunk_bias.astype(dtype)
+
+
+def _ring_flash_attention_fwd_tpu(q, k, v, attn_bias, segment_ids, axis_name, float32_logits, blockwise_kwargs):
+ if float32_logits:
+ q, k = q.astype(jnp.float32), k.astype(jnp.float32)
+ q, k, v = map(lambda x: rearrange(x, 'b q h d -> b h q d'), [q, k, v])
+ batch, num_heads, q_len, dim_per_head = q.shape
+ batch, num_heads, kv_len, dim_per_head = k.shape
+ attn_bias = attn_bias[:, 0, 0] # (batch, k_len)
+
+ o = jnp.zeros((batch, num_heads, q_len, dim_per_head)).astype(q.dtype)
+ l = jnp.zeros((batch, num_heads, q_len)).astype(q.dtype)
+ m = jnp.full((batch, num_heads, q_len), -jnp.inf).astype(q.dtype)
+
+ axis_size = lax.psum(1, axis_name)
+ q_block_size, kv_block_size = q_len, kv_len # assumes this function is pre-sharded inside shard_map
+ query_chunk_size = blockwise_kwargs["query_chunk_size"]
+ key_chunk_size = blockwise_kwargs["key_chunk_size"]
+ if segment_ids is not None:
+ q_segment_ids = lax.dynamic_slice_in_dim(
+ segment_ids,
+ lax.axis_index(axis_name) * q_len, q_len, axis=-1
+ )
+
+ block_sizes = BlockSizes(
+ block_q=query_chunk_size,
+ block_k_major=key_chunk_size,
+ block_k=key_chunk_size,
+ block_b=1,
+ block_q_major_dkv=query_chunk_size,
+ block_k_major_dkv=key_chunk_size,
+ block_k_dkv=key_chunk_size,
+ block_q_dkv=query_chunk_size,
+ block_k_major_dq=key_chunk_size,
+ block_k_dq=key_chunk_size,
+ block_q_dq=query_chunk_size,
+ )
+
+ scale = q.shape[-1] ** -0.5
+ def scan_kv_block(carry, idx):
+ o, l, m, k, v = carry
+ attn_bias_slice = lax.dynamic_slice_in_dim(attn_bias,
+ (lax.axis_index(axis_name) - idx) % axis_size * kv_len, kv_len, axis=-1
+ )
+ attn_bias_slice = None # TODO
+ if segment_ids is not None:
+ kv_segment_ids = lax.dynamic_slice_in_dim(
+ segment_ids,
+ (lax.axis_index(axis_name) - idx) % axis_size * kv_len, kv_len, axis=-1
+ )
+ segment_ids_slice = SegmentIds(q=q_segment_ids, kv=kv_segment_ids)
+ else:
+ segment_ids_slice = None
+ q_block_idx = lax.axis_index(axis_name)
+ k_block_idx = (lax.axis_index(axis_name) - idx) % axis_size
+ q_chunk_idx_start = q_block_idx * (q_block_size // query_chunk_size)
+ k_chunk_idx_start = k_block_idx * (kv_block_size // key_chunk_size)
+ o, l, m = _flash_attention_fwd(
+ q, k, v,
+ carry=(o, l, m),
+ q_chunk_idx_start=q_chunk_idx_start,
+ k_chunk_idx_start=k_chunk_idx_start,
+ ab=attn_bias_slice,
+ segment_ids=segment_ids_slice,
+ save_residuals=False,
+ causal=blockwise_kwargs["causal"],
+ sm_scale=scale,
+ block_sizes=block_sizes,
+ debug=False
+ )
+ k, v = map(lambda x: lax.ppermute(x, axis_name, perm=[(i, (i + 1) % axis_size) for i in range(axis_size)]), (k, v))
+ return (o, l, m, k, v), None
+ (o, l, m, _, _), _ = lax.scan(scan_kv_block,
+ init=(o, l, m, k, v), xs=jnp.arange(0, axis_size))
+ output = rearrange(o.astype(v.dtype), 'b h q d -> b q h d')
+ return output, (o, q, k, v, attn_bias, segment_ids, l, m)
+
+def _ring_flash_attention_bwd_tpu(axis_name, float32_logits, blockwise_kwargs, res, g):
+ del float32_logits
+ o, q, k, v, attn_bias, segment_ids, l, m = res
+ batch, num_heads, kv_len, dim_per_head = k.shape
+ axis_size = lax.psum(1, axis_name)
+ dq = jnp.zeros_like(q, dtype=jnp.float32)
+ dk = jnp.zeros_like(k, dtype=jnp.float32)
+ dv = jnp.zeros_like(v, dtype=jnp.float32)
+ query_chunk_size = blockwise_kwargs["query_chunk_size"]
+ key_chunk_size = blockwise_kwargs["key_chunk_size"]
+ q_block_size, kv_block_size = q.shape[2], k.shape[2] # assumes this function is pre-sharded inside shard_map
+ scale = q.shape[-1] ** -0.5
+
+ if segment_ids is not None:
+ q_segment_ids = lax.dynamic_slice_in_dim(
+ segment_ids,
+ lax.axis_index(axis_name) * q_block_size, q_block_size, axis=-1
+ )
+
+ g = rearrange(g, 'b q h d -> b h q d')
+
+ block_sizes = BlockSizes(
+ block_q=query_chunk_size,
+ block_k_major=key_chunk_size,
+ block_k=key_chunk_size,
+ block_b=1,
+ block_q_major_dkv=query_chunk_size,
+ block_k_major_dkv=key_chunk_size,
+ block_k_dkv=key_chunk_size,
+ block_q_dkv=query_chunk_size,
+ block_k_major_dq=key_chunk_size,
+ block_k_dq=key_chunk_size,
+ block_q_dq=query_chunk_size,
+ )
+
+ def scan_kv_block(carry, idx):
+ dq, dk, dv, k, v = carry
+ attn_bias_slice = lax.dynamic_slice_in_dim(attn_bias,
+ (lax.axis_index(axis_name) - idx) % axis_size * kv_len, kv_len, axis=-1
+ )
+ attn_bias_slice = None # TODO
+ if segment_ids is not None:
+ kv_segment_ids = lax.dynamic_slice_in_dim(
+ segment_ids,
+ (lax.axis_index(axis_name) - idx) % axis_size * kv_len, kv_len, axis=-1
+ )
+ segment_ids_slice = SegmentIds(q=q_segment_ids, kv=kv_segment_ids)
+ else:
+ segment_ids_slice = None
+ q_block_idx = lax.axis_index(axis_name)
+ k_block_idx = (lax.axis_index(axis_name) - idx) % axis_size
+ q_chunk_idx_start = q_block_idx * (q_block_size // query_chunk_size)
+ k_chunk_idx_start = k_block_idx * (kv_block_size // key_chunk_size)
+ dq_i, dk_i, dv_i, = _flash_attention_bwd(
+ save_residuals=False,
+ causal=blockwise_kwargs["causal"],
+ sm_scale=scale,
+ block_sizes=block_sizes,
+ debug=False,
+ q_chunk_idx_start=q_chunk_idx_start,
+ k_chunk_idx_start=k_chunk_idx_start,
+ residuals=(q, k, v, attn_bias_slice, segment_ids_slice, o, l, m),
+ do=g
+ )
+ dq += dq_i
+ dk += dk_i
+ dv += dv_i
+ k, v, dk, dv = map(lambda x: lax.ppermute(x, axis_name, perm=[(i,
+ (i + 1) % axis_size) for i in range(axis_size)]), (k, v, dk, dv))
+ return (dq, dk, dv, k, v), None
+ (dq, dk, dv, k, v), _ = lax.scan(scan_kv_block, init=(dq, dk, dv, k, v), xs=jnp.arange(0, axis_size))
+ dq, dk, dv = dq.astype(q.dtype), dk.astype(k.dtype), dv.astype(v.dtype)
+ dq, dk, dv = map(lambda x: rearrange(x, 'b h q d -> b q h d'), (dq, dk, dv))
+ return dq, dk, dv, None, None
+
+@partial(jax.custom_vjp, nondiff_argnums=[5, 6, 7])
+def ring_flash_attention_tpu(q, k, v, attn_bias, segment_ids, axis_name, float32_logits, blockwise_kwargs):
+ y, _ = _ring_flash_attention_fwd_tpu(q, k, v, attn_bias, segment_ids, axis_name, float32_logits, blockwise_kwargs)
+ return y
+
+ring_flash_attention_tpu.defvjp(_ring_flash_attention_fwd_tpu, _ring_flash_attention_bwd_tpu)
+
+# TPU-compatible fused attention functions for RingAttention
+DEFAULT_MASK_VALUE = -0.7 * float(jnp.finfo(jnp.dtype("float32")).max)
+NUM_LANES = 128
+NUM_SUBLANES = 8
+
+class SegmentIds(NamedTuple):
+ """SegmentIds for Q and KV sequences.
+
+ SegmentIds are used to generate segment mask, which prevents attention between
+ different segments in the input sequence. Each array is a list of ids
+ (integers).
+ Only the token with the same id can attend to each other.
+
+ Attributes:
+ q: segment ids along the Q sequence.
+ kv: segment ids along the KV sequence.
+ """
+
+ q: jax.Array # [q_seq_len]
+ kv: jax.Array # [kv_seq_len]
+
+
+@dataclasses.dataclass(frozen=True)
+class BlockSizes:
+ block_q: int
+ block_k_major: int
+ block_k: int
+ block_b: int
+
+ block_q_major_dkv: int | None = None
+ block_k_major_dkv: int | None = None
+ block_k_dkv: int | None = None
+ block_q_dkv: int | None = None
+
+ block_k_major_dq: int | None = None
+ block_k_dq: int | None = None
+ block_q_dq: int | None = None
+
+ def __post_init__(self):
+ def verify_major_minor(prefix, suffix, major, minor):
+ if minor > major:
+ raise ValueError(
+ f"{prefix}{suffix}={minor} should be smaller than"
+ f" {prefix}_major{suffix}={major}"
+ )
+ if major % minor != 0:
+ raise ValueError(
+ f"{prefix}{suffix}={minor} should divide"
+ f" {prefix}_major{suffix}={major}"
+ )
+
+ verify_major_minor("block_k", "", self.block_k_major, self.block_k)
+ if self.block_q_major_dkv is not None and self.block_q_dkv is not None:
+ verify_major_minor(
+ "block_q", "_dkv", self.block_q_major_dkv, self.block_q_dkv
+ )
+ if self.block_k_major_dkv is not None and self.block_k_dkv is not None:
+ verify_major_minor(
+ "block_k", "_dkv", self.block_k_major_dkv, self.block_k_dkv
+ )
+ if self.block_k_major_dq is not None and self.block_k_dq is not None:
+ verify_major_minor("block_k", "_dq", self.block_k_major_dq, self.block_k_dq)
+
+ @property
+ def has_backward_blocks(self) -> bool:
+ backward_blocks = (
+ self.block_q_major_dkv,
+ self.block_k_major_dkv,
+ self.block_q_dkv,
+ self.block_k_dkv,
+ self.block_k_major_dq,
+ self.block_k_dq,
+ self.block_q_dq,
+ )
+ return all(b is not None for b in backward_blocks)
+
+ @classmethod
+ def get_default(cls, batch_size, num_heads, q_seq_len, kv_len, d_model):
+ del batch_size, num_heads, q_seq_len, kv_len, d_model # Unused.
+ return BlockSizes(
+ block_q=128,
+ block_k_major=128,
+ block_k=128,
+ block_b=1,
+ block_q_major_dkv=128,
+ block_k_major_dkv=128,
+ block_k_dkv=128,
+ block_q_dkv=128,
+ block_k_major_dq=128,
+ block_k_dq=128,
+ block_q_dq=128,
+ )
+
+
+def _flash_attention(
+ q,
+ k,
+ v,
+ carry,
+ q_chunk_idx_start,
+ k_chunk_idx_start,
+ ab,
+ segment_ids,
+ save_residuals,
+ causal,
+ sm_scale,
+ block_sizes,
+ debug,
+):
+ return _flash_attention_impl(
+ q,
+ k,
+ v,
+ carry,
+ q_chunk_idx_start,
+ k_chunk_idx_start,
+ ab,
+ segment_ids,
+ save_residuals,
+ causal,
+ sm_scale,
+ block_sizes.block_b,
+ block_sizes.block_q,
+ block_sizes.block_k_major,
+ block_sizes.block_k,
+ debug,
+ )
+
+
+def _flash_attention_fwd(
+ q,
+ k,
+ v,
+ carry,
+ q_chunk_idx_start,
+ k_chunk_idx_start,
+ ab,
+ segment_ids,
+ save_residuals,
+ causal,
+ sm_scale,
+ block_sizes,
+ debug,
+):
+ if save_residuals:
+ raise NotImplementedError("Higher-order AD not supported")
+ o, l, m = _flash_attention(
+ q,
+ k,
+ v,
+ carry,
+ q_chunk_idx_start,
+ k_chunk_idx_start,
+ ab,
+ segment_ids,
+ True,
+ causal,
+ sm_scale,
+ block_sizes,
+ debug,
+ )
+ return o, l, m
+
+
+def _flash_attention_bwd(
+ save_residuals: bool,
+ causal: bool,
+ sm_scale: float,
+ block_sizes: BlockSizes,
+ debug: bool,
+ q_chunk_idx_start,
+ k_chunk_idx_start,
+ residuals,
+ do,
+):
+ """VJP rule for FlashAttention."""
+ if save_residuals:
+ raise NotImplementedError("Higher-order AD not supported")
+ (q, k, v, ab, segment_ids, o, l, m) = residuals
+ if not block_sizes.has_backward_blocks:
+ raise ValueError(
+ "Program is being differentiated, but not all backward blocks are"
+ " specified"
+ )
+
+ di = jnp.sum(
+ o.astype(jnp.float32) * do.astype(jnp.float32), axis=-1
+ ) # [batch_size, num_heads, q_seq_len]
+
+ dk, dv = _flash_attention_bwd_dkv(
+ q_chunk_idx_start,
+ k_chunk_idx_start,
+ q,
+ k,
+ v,
+ ab,
+ segment_ids,
+ l,
+ m,
+ do,
+ di,
+ block_q_major=block_sizes.block_q_major_dkv,
+ block_k_major=block_sizes.block_k_major_dkv,
+ block_k=block_sizes.block_k_dkv,
+ block_q=block_sizes.block_q_dkv,
+ sm_scale=sm_scale,
+ causal=causal,
+ mask_value=DEFAULT_MASK_VALUE,
+ debug=debug,
+ )
+
+ dq, ds = _flash_attention_bwd_dq(
+ q_chunk_idx_start,
+ k_chunk_idx_start,
+ q,
+ k,
+ v,
+ ab,
+ segment_ids,
+ l,
+ m,
+ do,
+ di,
+ block_q_major=block_sizes.block_q_dq,
+ block_k_major=block_sizes.block_k_major_dq,
+ block_k=block_sizes.block_k_dq,
+ sm_scale=sm_scale,
+ causal=causal,
+ mask_value=DEFAULT_MASK_VALUE,
+ debug=debug,
+ )
+ return dq, dk, dv
+
+
+MIN_BLOCK_SIZE = 128
+TRANS_B_DIM_NUMBERS = (((1,), (1,)), ((), ()))
+
+
+def below_or_on_diag(r, r_blk_size, c, c_blk_size):
+ # A block is considered below or on diagonal as long as the bottom left
+ # corner of the block is below or on diagonal.
+ return ((r + 1) * r_blk_size - 1) > (c * c_blk_size)
+
+
+def _flash_attention_kernel(
+ q_idx_chunk_start, k_idx_chunk_start, q_tile_ref, *args, **kwargs
+):
+ block_b = q_tile_ref.shape[0]
+ # If we're not going to tile the softmax, then we can avoid a bunch of VPU ops.
+ if kwargs["block_k"] == kwargs["kv_seq_len"]:
+ assert False
+ kernel = _flash_attention_kernel_single_batch_single_step
+ else:
+ kernel = _flash_attention_kernel_single_batch
+ for batch_idx in range(block_b):
+ kernel(
+ (batch_idx, 0),
+ q_idx_chunk_start,
+ k_idx_chunk_start,
+ q_tile_ref,
+ *args,
+ **kwargs,
+ )
+
+
+def _flash_attention_kernel_single_batch(
+ batch_idx: tuple[int, ...],
+ q_chunk_idx_start_ref,
+ k_chunk_idx_start_ref,
+ q_tile_ref,
+ k_tile_ref,
+ v_tile_ref,
+ acc_tile_ref,
+ l_tile_ref,
+ m_tile_ref,
+ ab_tile_ref,
+ q_segment_ids_tile_ref,
+ kv_segment_ids_tile_ref, # Input arrays
+ o_tile_ref, # Output arrays
+ m_scratch_ref,
+ l_scratch_ref,
+ acc_scratch_ref,
+ l_ref: Any | None = None,
+ m_ref: Any | None = None,
+ *,
+ causal,
+ sm_scale,
+ block_k,
+ kv_seq_len,
+ mask_value,
+ block_q,
+):
+ block_k_major = k_tile_ref.shape[2]
+ block_q = q_tile_ref.shape[2]
+ head_dim = q_tile_ref.shape[-1]
+
+ kv_seq_idx = pl.program_id(3)
+
+ @pl.when(kv_seq_idx == 0)
+ def start_new_sequence():
+ m_scratch_ref[batch_idx] = m_tile_ref[batch_idx]
+ l_scratch_ref[batch_idx] = l_tile_ref[batch_idx]
+ acc_scratch_ref[batch_idx] = acc_tile_ref[batch_idx]
+
+ q_chunk_idx_start = q_chunk_idx_start_ref[0]
+ k_chunk_idx_start = k_chunk_idx_start_ref[0]
+
+ q_seq_idx = pl.program_id(2)
+ if causal:
+ should_run = below_or_on_diag(
+ q_seq_idx + q_chunk_idx_start,
+ block_q,
+ kv_seq_idx + k_chunk_idx_start,
+ block_k_major,
+ )
+ else:
+ should_run = True
+
+ @pl.when(should_run)
+ def run():
+ @functools.partial(
+ lax.fori_loop, 0, block_k_major // block_k, init_val=None, unroll=True
+ )
+ def body(i, _):
+ m_prev = m_scratch_ref[batch_idx]
+ l_prev = l_scratch_ref[batch_idx]
+ q = q_tile_ref[batch_idx] # [block_q, head_dim]
+ start_k = i * block_k
+ k = pl.load(
+ k_tile_ref, (*batch_idx, pl.dslice(start_k, block_k), slice(None))
+ ) # [block_k, head_dim]
+
+ s = jax.lax.dot_general(
+ q, k, TRANS_B_DIM_NUMBERS, preferred_element_type=jnp.float32
+ ) # [block_q, block_k]
+
+ # Add attention bias if needed.
+ if ab_tile_ref is not None:
+ ab = pl.load(
+ ab_tile_ref,
+ (batch_idx[0], pl.dslice(0, block_q), pl.dslice(start_k, block_k)),
+ ).astype(jnp.float32)
+ s += ab
+
+ if sm_scale != 1.0:
+ s *= sm_scale
+
+ mask = None
+ if q_segment_ids_tile_ref is not None:
+ repeats, rem = divmod(block_k, NUM_LANES)
+ if rem:
+ raise NotImplementedError(
+ f"kv block size must be a multiple of {NUM_LANES}"
+ )
+ q_segment_ids = pltpu.repeat(
+ q_segment_ids_tile_ref[batch_idx[0]], repeats, axis=1
+ ) # [block_q, block_k].
+ kv_segment_ids = pl.load(
+ kv_segment_ids_tile_ref,
+ (batch_idx[0], pl.dslice(1), pl.dslice(start_k, block_k)),
+ ) # [1, block_k].
+ mask = jnp.equal(q_segment_ids, kv_segment_ids).astype(jnp.bool_)
+
+ if causal:
+ mask_shape = (block_q, block_k)
+ row_ids = jax.lax.broadcasted_iota(jnp.int32, mask_shape, 0)
+ row_ids += (q_seq_idx + q_chunk_idx_start) * block_q
+ col_ids = jax.lax.broadcasted_iota(jnp.int32, mask_shape, 1)
+ col_ids += (kv_seq_idx + k_chunk_idx_start) * block_k_major + start_k
+ causal_mask = col_ids <= row_ids
+ mask = (
+ causal_mask if mask is None else jnp.logical_and(mask, causal_mask)
+ )
+
+ s = s if mask is None else s + jnp.where(mask, 0.0, mask_value)
+
+ m_curr = jnp.max(s, axis=1)[:, None] # Row max, shape [block_q, 1].
+ m_next = jnp.maximum(m_prev, m_curr) # Shape [block_q, 128].
+
+ block_k_repeats, rem = divmod(block_k, MIN_BLOCK_SIZE)
+ if rem:
+ raise NotImplementedError(
+ f"{block_k=} should be a multiple of {MIN_BLOCK_SIZE}"
+ )
+ p = jnp.exp(s - pltpu.repeat(m_next, block_k_repeats, 1))
+
+ alpha = jnp.exp(m_prev - m_next) # Shape [block_q, 128].
+
+ l_corr = alpha * l_prev
+
+ l_next = jnp.sum(p, axis=1)[:, None] + l_corr # Shape [block_q, 128]
+
+ head_dim_repeats, rem = divmod(head_dim, MIN_BLOCK_SIZE)
+ l_broadcast = lambda l: pltpu.repeat(l, head_dim_repeats, 1)
+ if rem:
+ if head_dim_repeats == 0:
+ l_broadcast = lambda l: l[:, :head_dim]
+ else:
+ raise NotImplementedError(
+ f"{head_dim=} should be a multiple of {MIN_BLOCK_SIZE} if larger"
+ )
+ l_scratch_ref[batch_idx] = l_next
+ m_scratch_ref[batch_idx] = m_next
+
+ l_next_inv_safe = jnp.where(l_next == 0.0, 1.0, 1.0 / l_next)
+ acc_scratch_ref[batch_idx] *= l_broadcast(l_corr * l_next_inv_safe)
+ v = pl.load(
+ v_tile_ref, (*batch_idx, pl.dslice(start_k, block_k), slice(None))
+ )
+ o_curr = jax.lax.dot(
+ p.astype(v.dtype), v, preferred_element_type=jnp.float32
+ )
+ acc_scratch_ref[batch_idx] += o_curr * l_broadcast(l_next_inv_safe)
+
+ @pl.when(kv_seq_idx == (kv_seq_len // block_k_major) - 1)
+ def store_output():
+ o_tile_ref[batch_idx] = acc_scratch_ref[batch_idx].astype(o_tile_ref.dtype)
+ if l_ref is not None:
+ l_ref[batch_idx] = l_scratch_ref[batch_idx].astype(l_ref.dtype)
+ if m_ref is not None:
+ m_ref[batch_idx] = m_scratch_ref[batch_idx].astype(m_ref.dtype)
+
+
+def _flash_attention_impl(
+ q,
+ k,
+ v,
+ carry,
+ q_chunk_idx_start,
+ k_chunk_idx_start,
+ ab,
+ segment_ids,
+ save_residuals,
+ causal,
+ sm_scale,
+ block_b,
+ block_q,
+ block_k_major,
+ block_k,
+ debug,
+):
+ assert block_k_major == block_k, (block_k_major, block_k)
+ batch_size, num_heads, q_seq_len, head_dim = q.shape
+ _, _, kv_seq_len, _ = k.shape
+ acc, l_prev, m_prev = carry
+ l_prev, m_prev = map(
+ lambda x: jnp.broadcast_to(x[..., None], (*x.shape, MIN_BLOCK_SIZE)),
+ (l_prev, m_prev),
+ )
+ q_chunk_idx_start, k_chunk_idx_start = (
+ q_chunk_idx_start[None],
+ k_chunk_idx_start[None],
+ )
+ _verify_block("block_q", "q_seq_len", block_q, q_seq_len, should_divide=False)
+ _verify_block("block_k_major", "kv_seq_len", block_k_major, kv_seq_len)
+ _verify_block("block_k", "kv_seq_len", block_k, kv_seq_len)
+ _verify_block("block_b", "batch", block_b, batch_size, should_divide=False)
+
+ grid = (
+ pl.cdiv(batch_size, block_b),
+ num_heads,
+ pl.cdiv(q_seq_len, block_q),
+ kv_seq_len // block_k_major,
+ )
+
+ def q_index_map(batch_index, head_index, q_seq_index, _, q_idx_ref, k_idx_ref):
+ return (batch_index, head_index, q_seq_index, 0)
+
+ def kv_index_map(
+ batch_index, head_index, q_seq_index, kv_seq_index, q_idx_ref, k_idx_ref
+ ):
+ if causal:
+ # If the kv block is skipped, prefetch the next valid kv block, i.e. the
+ # 0th one to be used for the next block_q rows.
+ next_kv_index = lax.select(
+ below_or_on_diag(
+ q_seq_index + q_idx_ref[0],
+ block_q,
+ kv_seq_index + k_idx_ref[0],
+ block_k_major,
+ ),
+ kv_seq_index,
+ 0,
+ )
+ else:
+ next_kv_index = kv_seq_index
+ return (batch_index, head_index, next_kv_index, 0)
+
+ def ab_index_map(
+ batch_index, head_index, q_seq_index, kv_seq_index, q_idx_ref, k_idx_ref
+ ):
+ if causal:
+ should_run = below_or_on_diag(
+ q_seq_index + q_idx_ref[0],
+ block_q,
+ kv_seq_index + k_idx_ref[0],
+ block_k_major,
+ )
+ next_kv_index = lax.select(should_run, kv_seq_index, 0)
+ else:
+ next_kv_index = kv_seq_index
+
+ return (batch_index, 0, next_kv_index)
+
+ def o_index_map(batch_index, head_index, q_seq_index, _, q_idx_ref, k_idx_ref):
+ return (batch_index, head_index, q_seq_index, 0)
+
+ def lm_index_map(batch_index, head_index, q_seq_index, _, q_idx_ref, k_idx_ref):
+ return (batch_index, head_index, q_seq_index, 0)
+
+ kernel = functools.partial(
+ _flash_attention_kernel,
+ causal=causal,
+ mask_value=DEFAULT_MASK_VALUE,
+ sm_scale=sm_scale,
+ block_k=block_k,
+ kv_seq_len=kv_seq_len,
+ block_q=block_q,
+ )
+ out_shape = [jax.ShapeDtypeStruct(shape=q.shape, dtype=q.dtype)]
+ out_specs = [pl.BlockSpec(o_index_map, (block_b, 1, block_q, head_dim))]
+
+ if block_k != kv_seq_len:
+ scratch_shape = functools.partial(jax.ShapeDtypeStruct, dtype=jnp.float32)
+ m_scratch = scratch_shape((block_b, 1, block_q, MIN_BLOCK_SIZE))
+ l_scratch = scratch_shape((block_b, 1, block_q, MIN_BLOCK_SIZE))
+ acc_scratch = scratch_shape((block_b, 1, block_q, head_dim))
+ out_shape += [m_scratch, l_scratch, acc_scratch]
+ out_specs += [
+ pl.BlockSpec(lambda *_: (0, 0, 0, 0), m_scratch.shape),
+ pl.BlockSpec(lambda *_: (0, 0, 0, 0), l_scratch.shape),
+ pl.BlockSpec(lambda *_: (0, 0, 0, 0), acc_scratch.shape),
+ ]
+ else:
+ assert False
+ out_shape += [None, None, None]
+ out_specs += [None, None, None]
+
+ if save_residuals:
+ out_specs = [
+ *out_specs,
+ pl.BlockSpec(lm_index_map, (block_b, 1, block_q, MIN_BLOCK_SIZE)),
+ pl.BlockSpec(lm_index_map, (block_b, 1, block_q, MIN_BLOCK_SIZE)),
+ ]
+ l = jax.ShapeDtypeStruct(
+ (batch_size, num_heads, q_seq_len, MIN_BLOCK_SIZE), dtype=jnp.float32
+ )
+ m = jax.ShapeDtypeStruct(
+ (batch_size, num_heads, q_seq_len, MIN_BLOCK_SIZE), dtype=jnp.float32
+ )
+ out_shape = (*out_shape, l, m)
+
+ ab_block_spec = (
+ pl.BlockSpec(ab_index_map, (block_b, block_q, block_k_major))
+ if ab is not None
+ else None
+ )
+
+ if ab is not None:
+ ab = ab[:, None].repeat(block_q, axis=1)
+
+ q_segment_ids_spec = kv_segment_ids_spec = None
+ q_segment_ids = kv_segment_ids = None
+ if segment_ids is not None:
+
+ def q_segment_ids_index_map(
+ batch_index, head_index, q_seq_index, _, q_idx_ref, k_idx_ref
+ ):
+ del head_index
+ return (batch_index, q_seq_index, 0)
+
+ def kv_segment_ids_index_map(
+ batch_index, head_index, q_seq_index, kv_seq_index, q_idx_ref, k_idx_ref
+ ):
+ del head_index
+ if causal:
+ next_kv_index = lax.select(
+ below_or_on_diag(
+ q_seq_index + q_idx_ref[0],
+ block_q,
+ kv_seq_index + k_idx_ref[0],
+ block_k_major,
+ ),
+ kv_seq_index,
+ 0,
+ )
+ else:
+ next_kv_index = kv_seq_index
+ return (batch_index, 0, next_kv_index)
+
+ q_segment_ids_spec = pl.BlockSpec(
+ q_segment_ids_index_map, (block_b, block_q, NUM_LANES)
+ )
+ kv_segment_ids_spec = pl.BlockSpec(
+ kv_segment_ids_index_map, (block_b, NUM_SUBLANES, block_k_major)
+ )
+
+ q_segment_ids = jax.lax.broadcast_in_dim(
+ segment_ids.q,
+ (batch_size, q_seq_len, NUM_LANES),
+ (
+ 0,
+ 1,
+ ),
+ )
+ kv_segment_ids = jax.lax.broadcast_in_dim(
+ segment_ids.kv,
+ (batch_size, NUM_SUBLANES, kv_seq_len),
+ (
+ 0,
+ 2,
+ ),
+ )
+
+ in_specs = [
+ pl.BlockSpec(q_index_map, (block_b, 1, block_q, head_dim)),
+ pl.BlockSpec(kv_index_map, (block_b, 1, block_k_major, head_dim)),
+ pl.BlockSpec(kv_index_map, (block_b, 1, block_k_major, head_dim)),
+ pl.BlockSpec(q_index_map, (block_b, 1, block_q, head_dim)),
+ pl.BlockSpec(lm_index_map, (block_b, 1, block_q, MIN_BLOCK_SIZE)),
+ pl.BlockSpec(lm_index_map, (block_b, 1, block_q, MIN_BLOCK_SIZE)),
+ ab_block_spec,
+ q_segment_ids_spec,
+ kv_segment_ids_spec,
+ ]
+
+ o, *aux = pl.pallas_call(
+ kernel,
+ out_shape=out_shape,
+ grid_spec=pltpu.PrefetchScalarGridSpec(
+ num_scalar_prefetch=2, in_specs=in_specs, out_specs=out_specs, grid=grid
+ ),
+ debug=debug,
+ mosaic_params=dict(
+ dimension_semantics=("parallel", "parallel", "parallel", "arbitrary")
+ ),
+ )(
+ q_chunk_idx_start,
+ k_chunk_idx_start,
+ q,
+ k,
+ v,
+ acc,
+ l_prev,
+ m_prev,
+ ab,
+ q_segment_ids,
+ kv_segment_ids,
+ )
+ if save_residuals:
+ l, m = (v[..., 0] for v in aux[-2:])
+ return (o, l, m)
+ else:
+ return o
+
+
+def _flash_attention_dkv_kernel(
+ q_chunk_idx_start_ref,
+ k_chunk_idx_start_ref,
+ q_tile_ref,
+ k_tile_ref,
+ v_tile_ref,
+ ab_tile_ref,
+ q_segment_ids_tile_ref,
+ kv_segment_ids_tile_ref,
+ l_tile_ref,
+ m_tile_ref,
+ do_tile_ref,
+ di_tile_ref,
+ dk_tile_ref,
+ dv_tile_ref,
+ dk_scratch_ref,
+ dv_scratch_ref,
+ *,
+ sm_scale: float,
+ causal: bool,
+ mask_value: float,
+ q_seq_len: int,
+ block_q: int,
+ block_k: int,
+):
+ _, _, block_q_major, _ = q_tile_ref.shape
+ _, _, block_k_major, _ = k_tile_ref.shape
+
+ q_seq_index = pl.program_id(axis=3)
+ kv_seq_index = pl.program_id(axis=2)
+
+ q_chunk_idx_start = q_chunk_idx_start_ref[0]
+ k_chunk_idx_start = k_chunk_idx_start_ref[0]
+
+ @pl.when(q_seq_index == 0)
+ def start_new_sequence():
+ dk_scratch_ref[:, :] = jnp.zeros(dk_scratch_ref.shape, dk_scratch_ref.dtype)
+ dv_scratch_ref[:, :] = jnp.zeros(dv_scratch_ref.shape, dv_scratch_ref.dtype)
+
+ def q_body(j, _):
+ start_q = j * block_q
+
+ def k_body(i, _):
+ start_k = i * block_k
+ k = pl.load(k_tile_ref, (0, 0, pl.ds(start_k, block_k), slice(None)))
+ v = pl.load(v_tile_ref, (0, 0, pl.ds(start_k, block_k), slice(None)))
+ q = pl.load(
+ q_tile_ref, (0, 0, pl.ds(start_q, block_q), slice(None))
+ ) # [block_q, head_dim]
+ l = pl.load(
+ l_tile_ref, (0, 0, pl.ds(start_q, block_q), slice(None))
+ ) # [block_q, 128]
+ m = pl.load(
+ m_tile_ref, (0, 0, pl.ds(start_q, block_q), slice(None))
+ ) # [block_q, 128]
+ do = pl.load(
+ do_tile_ref, (0, 0, pl.ds(start_q, block_q), slice(None))
+ ) # [block_q, 128]
+ di = pl.load(
+ di_tile_ref, (0, 0, pl.ds(start_q, block_q), slice(None))
+ ).astype(
+ jnp.float32
+ ) # [block_q, 128]
+
+ capped_logits = lax.dot_general(
+ q, k, TRANS_B_DIM_NUMBERS, preferred_element_type=jnp.float32
+ ) # [block_q_major, block_k]
+
+ if ab_tile_ref is not None:
+ ab = pl.load(
+ ab_tile_ref,
+ (
+ 0,
+ pl.dslice(0, block_q),
+ pl.dslice(i * block_k, block_k),
+ ),
+ ).astype(jnp.float32)
+ capped_logits += ab
+
+ if sm_scale != 1.0:
+ capped_logits *= sm_scale
+
+ mask = None
+ if q_segment_ids_tile_ref is not None:
+ repeats, rem = divmod(block_k, NUM_LANES)
+ if rem:
+ raise NotImplementedError()
+ q_segment_ids = pl.load(
+ q_segment_ids_tile_ref, (0, pl.ds(start_q, block_q), slice(None))
+ ) # [block_q, NUM_LANES].
+ q_segment_ids = pltpu.repeat(
+ q_segment_ids, repeats, axis=1
+ ) # [block_q, block_k].
+ kv_segment_ids = pl.load(
+ kv_segment_ids_tile_ref, (slice(None), 0, pl.ds(start_k, block_k))
+ ) # [1, block_k].
+ mask = jnp.equal(q_segment_ids, kv_segment_ids).astype(jnp.bool_)
+
+ if causal:
+ mask_shape = (block_q, block_k)
+ row_ids = jax.lax.broadcasted_iota(jnp.int32, mask_shape, 0)
+ row_ids += (q_seq_index + q_chunk_idx_start) * block_q_major + start_q
+ col_ids = jax.lax.broadcasted_iota(jnp.int32, mask_shape, 1)
+ col_ids += (kv_seq_index + k_chunk_idx_start) * block_k_major + start_k
+ causal_mask = col_ids <= row_ids
+ mask = (
+ causal_mask if mask is None else jnp.logical_and(mask, causal_mask)
+ )
+
+ capped_logits = (
+ capped_logits
+ if mask is None
+ else capped_logits + jnp.where(mask, 0.0, mask_value)
+ )
+
+ p = jnp.exp(
+ capped_logits - pltpu.repeat(m, block_k // MIN_BLOCK_SIZE, axis=1)
+ )
+ p = p * pltpu.repeat(
+ 1 / l, block_k // MIN_BLOCK_SIZE, axis=1
+ ) # [block_q_major, block_k_major]
+ dv = lax.dot(p.T.astype(do.dtype), do, preferred_element_type=jnp.float32)
+ pl.store(
+ dv_scratch_ref,
+ (pl.ds(start_k, block_k), slice(None)),
+ pl.load(dv_scratch_ref, (pl.ds(start_k, block_k), slice(None)))
+ + dv.astype(dv_scratch_ref.dtype),
+ )
+
+ # di: [block_q, 128]
+ # do: [block_q, head_dim]
+ # v: [block_k_major, head_dim]
+ dp = lax.dot_general(
+ do, v, TRANS_B_DIM_NUMBERS, preferred_element_type=jnp.float32
+ )
+ ds = (dp - pltpu.repeat(di, block_k // MIN_BLOCK_SIZE, axis=1)) * p
+
+ if sm_scale != 1.0:
+ ds = ds * sm_scale
+
+ # ds: [block_q_major, block_k_major]
+ # q: [block_q_major, head_dim]
+ dk = lax.dot(ds.T.astype(do.dtype), q, preferred_element_type=jnp.float32)
+ pl.store(
+ dk_scratch_ref,
+ (pl.ds(start_k, block_k), slice(None)),
+ pl.load(dk_scratch_ref, (pl.ds(start_k, block_k), slice(None)))
+ + dk.astype(dk_scratch_ref.dtype),
+ )
+
+ lax.fori_loop(0, block_k_major // block_k, k_body, None, unroll=True)
+
+ if causal:
+ should_run = below_or_on_diag(
+ q_seq_index + q_chunk_idx_start,
+ block_q_major,
+ kv_seq_index + k_chunk_idx_start,
+ block_k_major,
+ )
+ else:
+ should_run = True
+
+ @pl.when(should_run)
+ def run():
+ lax.fori_loop(0, block_q_major // block_q, q_body, None, unroll=True)
+
+ @pl.when(q_seq_index == q_seq_len // block_q_major - 1)
+ def end_of_q_sequence():
+ dv_tile_ref[0, 0, :, :] = dv_scratch_ref[...].astype(dv_tile_ref)
+ dk_tile_ref[0, 0, :, :] = dk_scratch_ref[...].astype(dk_tile_ref)
+
+
+def _flash_attention_bwd_dkv(
+ q_chunk_idx_start,
+ k_chunk_idx_start,
+ q,
+ k,
+ v,
+ ab,
+ segment_ids,
+ l,
+ m,
+ do,
+ di,
+ *,
+ block_q_major: int | None,
+ block_q: int | None,
+ block_k_major: int | None,
+ block_k: int | None,
+ sm_scale: float,
+ causal: bool = False,
+ mask_value: float = DEFAULT_MASK_VALUE,
+ debug: bool = False,
+):
+ batch_size, num_heads, q_seq_len, head_dim = q.shape
+ _, _, kv_seq_len, _ = k.shape
+ q_chunk_idx_start, k_chunk_idx_start = (
+ q_chunk_idx_start[None],
+ k_chunk_idx_start[None],
+ )
+ _verify_block("block_q_major_dkv", "q_seq_len", block_q_major, q_seq_len)
+ _verify_block("block_q_dkv", "q_seq_len", block_q, q_seq_len)
+ _verify_block("block_k_major_dkv", "kv_seq_len", block_k_major, kv_seq_len)
+ _verify_block("block_k_dkv", "kv_seq_len", block_k, kv_seq_len)
+
+ # Broadcast out scalar values
+ m = jnp.broadcast_to(m[..., None], (*m.shape, MIN_BLOCK_SIZE))
+ l = jnp.broadcast_to(l[..., None], (*l.shape, MIN_BLOCK_SIZE))
+ # Preprocess contraction for bwd pass
+ di = jnp.broadcast_to(di[..., None], (*di.shape, MIN_BLOCK_SIZE))
+
+ # kv index needs to be before q index since q index is the contractng
+ # dimension.
+ grid = (
+ batch_size,
+ num_heads,
+ kv_seq_len // block_k_major,
+ q_seq_len // block_q_major,
+ )
+
+ def qo_index_map(
+ batch_index, head_index, kv_seq_index, q_seq_index, q_idx_ref, k_idx_ref
+ ):
+ if causal:
+ # If the q block is skipped, stay at the 0th q block.
+ next_q_index = lax.select(
+ below_or_on_diag(
+ q_seq_index + q_idx_ref[0],
+ block_q_major,
+ kv_seq_index + k_idx_ref[0],
+ block_k_major,
+ ),
+ q_seq_index,
+ 0,
+ )
+ else:
+ next_q_index = q_seq_index
+
+ return (batch_index, head_index, next_q_index, 0)
+
+ qo_spec = pl.BlockSpec(qo_index_map, (1, 1, block_q_major, head_dim))
+ assert qo_spec.block_shape is not None
+ assert q.ndim == len(qo_spec.block_shape)
+ do_spec = qo_spec
+ assert do.ndim == len(qo_spec.block_shape)
+
+ def kv_index_map(batch_index, head_index, kv_seq_index, _, q_idx_ref, k_idx_ref):
+ return (batch_index, head_index, kv_seq_index, 0)
+
+ kv_spec = pl.BlockSpec(kv_index_map, (1, 1, block_k_major, head_dim))
+ assert kv_spec.block_shape is not None
+ assert k.ndim == len(kv_spec.block_shape)
+ assert v.ndim == len(kv_spec.block_shape)
+
+ def lm_index_map(batch_index, head_index, _, q_seq_index, q_idx_ref, k_idx_ref):
+ return (batch_index, head_index, q_seq_index, 0)
+
+ lm_spec = pl.BlockSpec(lm_index_map, (1, 1, block_q_major, MIN_BLOCK_SIZE))
+ assert lm_spec.block_shape is not None
+ assert l.ndim == len(lm_spec.block_shape)
+ assert m.ndim == len(lm_spec.block_shape)
+
+ di_spec = pl.BlockSpec(qo_index_map, (1, 1, block_q_major, MIN_BLOCK_SIZE))
+ assert di_spec.block_shape is not None
+ assert di.ndim == len(di_spec.block_shape)
+
+ def ab_index_map(
+ batch_index, head_index, kv_seq_index, q_seq_index, q_idx_ref, k_idx_ref
+ ):
+ return (batch_index, 0, kv_seq_index)
+
+ if ab is not None:
+ ab = ab[:, None].repeat(block_q_major, axis=1)
+
+ dab_spec = (
+ pl.BlockSpec(ab_index_map, (1, block_q_major, block_k_major))
+ if ab is not None
+ else None
+ )
+
+ q_segment_ids_spec = kv_segment_ids_spec = None
+ q_segment_ids = kv_segment_ids = None
+ if segment_ids is not None:
+
+ def q_segment_ids_index_map(
+ batch_index, head_index, kv_seq_index, q_seq_index, q_idx_ref, k_idx_ref
+ ):
+ del head_index
+ if causal:
+ next_q_index = lax.select(
+ below_or_on_diag(
+ q_seq_index + q_idx_ref[0],
+ block_q_major,
+ kv_seq_index + k_idx_ref[0],
+ block_k_major,
+ ),
+ q_seq_index,
+ 0,
+ )
+ else:
+ next_q_index = q_seq_index
+ return (batch_index, next_q_index, 0)
+
+ def kv_segment_ids_index_map(
+ batch_index, head_index, kv_seq_index, _, q_idx_ref, k_idx_ref
+ ):
+ del head_index
+ return (batch_index, 0, kv_seq_index)
+
+ q_segment_ids_spec = pl.BlockSpec(
+ q_segment_ids_index_map, (1, block_q_major, NUM_LANES)
+ )
+ kv_segment_ids_spec = pl.BlockSpec(
+ kv_segment_ids_index_map, (1, NUM_SUBLANES, block_k_major)
+ )
+
+ q_segment_ids = jax.lax.broadcast_in_dim(
+ segment_ids.q,
+ (batch_size, q_seq_len, NUM_LANES),
+ (
+ 0,
+ 1,
+ ),
+ )
+ kv_segment_ids = jax.lax.broadcast_in_dim(
+ segment_ids.kv,
+ (batch_size, NUM_SUBLANES, kv_seq_len),
+ (
+ 0,
+ 2,
+ ),
+ )
+
+ in_specs = [
+ qo_spec,
+ kv_spec,
+ kv_spec,
+ dab_spec,
+ q_segment_ids_spec,
+ kv_segment_ids_spec,
+ lm_spec,
+ lm_spec,
+ do_spec,
+ di_spec,
+ ]
+
+ out_shapes = [
+ jax.ShapeDtypeStruct((batch_size, num_heads, kv_seq_len, head_dim), k.dtype),
+ jax.ShapeDtypeStruct((batch_size, num_heads, kv_seq_len, head_dim), v.dtype),
+ jax.ShapeDtypeStruct((block_k_major, head_dim), jnp.float32),
+ jax.ShapeDtypeStruct((block_k_major, head_dim), jnp.float32),
+ ]
+
+ def dkv_index_map(batch_index, head_index, kv_seq_index, _, q_idx_ref, k_idx_ref):
+ return (batch_index, head_index, kv_seq_index, 0)
+
+ dkv_spec = pl.BlockSpec(dkv_index_map, (1, 1, block_k_major, head_dim))
+ out_specs = [
+ dkv_spec,
+ dkv_spec,
+ pl.BlockSpec(lambda *_: (0, 0), (block_k_major, head_dim)),
+ pl.BlockSpec(lambda *_: (0, 0), (block_k_major, head_dim)),
+ ]
+
+ kernel = functools.partial(
+ _flash_attention_dkv_kernel,
+ block_q=block_q,
+ block_k=block_k,
+ sm_scale=sm_scale,
+ causal=causal,
+ mask_value=mask_value,
+ q_seq_len=q_seq_len,
+ )
+ name_scope = (
+ f"flash_mha_bwd_dkv_{block_q_major=}_{block_q=}_{block_k_major=}_{block_k=}"
+ )
+ with jax.named_scope(name_scope):
+ dk, dv, _, _ = pl.pallas_call(
+ kernel,
+ out_shape=out_shapes,
+ grid_spec=pltpu.PrefetchScalarGridSpec(
+ num_scalar_prefetch=2, in_specs=in_specs, out_specs=out_specs, grid=grid
+ ),
+ debug=debug,
+ mosaic_params=dict(
+ dimension_semantics=(
+ "parallel",
+ "parallel",
+ "parallel",
+ "arbitrary",
+ )
+ ),
+ )(
+ q_chunk_idx_start,
+ k_chunk_idx_start,
+ q,
+ k,
+ v,
+ ab,
+ q_segment_ids,
+ kv_segment_ids,
+ l,
+ m,
+ do,
+ di,
+ )
+ assert dk.shape == k.shape
+ assert dv.shape == v.shape
+ return dk, dv
+
+
+def _flash_attention_dq_kernel(
+ q_chunk_idx_start_ref,
+ k_chunk_idx_start_ref,
+ q_tile_ref,
+ k_tile_ref,
+ v_tile_ref,
+ ab_tile_ref,
+ q_segment_ids_tile_ref,
+ kv_segment_ids_tile_ref,
+ l_tile_ref,
+ m_tile_ref,
+ do_tile_ref,
+ di_tile_ref,
+ dq_tile_ref,
+ dq_scratch_ref,
+ ds_tile_ref,
+ *,
+ sm_scale: float,
+ causal: bool,
+ mask_value: float,
+ kv_seq_len: int,
+ block_k: int,
+):
+ _, _, block_k_major, _ = k_tile_ref.shape
+ _, _, block_q_major, _ = q_tile_ref.shape
+
+ kv_seq_index = pl.program_id(axis=3)
+ q_seq_index = pl.program_id(axis=2)
+
+ q_chunk_idx_start = q_chunk_idx_start_ref[0]
+ k_chunk_idx_start = k_chunk_idx_start_ref[0]
+
+ @pl.when(kv_seq_index == 0)
+ def start_new_sequence():
+ dq_scratch_ref[:, :] = jnp.zeros(dq_scratch_ref.shape, dq_scratch_ref.dtype)
+
+ def body(i, _):
+ k_slice = pl.ds(i * block_k, block_k)
+ q = q_tile_ref[0, 0, :, :]
+ k = pl.load(
+ k_tile_ref,
+ (0, 0, k_slice, slice(None)),
+ ) # [block_k, head_dim]
+ v = pl.load(
+ v_tile_ref,
+ (0, 0, k_slice, slice(None)),
+ ) # [block_k, head_dim]
+ l = l_tile_ref[0, 0, :, :] # [block_q_major, 128]
+ m = m_tile_ref[0, 0, :, :] # [block_q_major, 128]
+ do = do_tile_ref[0, 0, :, :] # [block_q_major, head_dim]
+ di = di_tile_ref[0, 0, :].astype(jnp.float32) # [block_q_major, 128]
+
+ capped_logits = jax.lax.dot_general(
+ q, k, TRANS_B_DIM_NUMBERS, preferred_element_type=jnp.float32
+ )
+
+ if ab_tile_ref is not None:
+ ab = pl.load(
+ ab_tile_ref,
+ (0, pl.dslice(0, block_q_major), pl.dslice(i * block_k, block_k)),
+ ).astype(jnp.float32)
+ capped_logits += ab
+
+ if sm_scale != 1.0:
+ capped_logits *= sm_scale
+
+ mask = None
+ if q_segment_ids_tile_ref is not None:
+ repeats, rem = divmod(block_k, NUM_LANES)
+ if rem:
+ raise NotImplementedError(
+ f"kv block size must be a multiple of {NUM_LANES}"
+ )
+ q_segment_ids = pltpu.repeat(
+ q_segment_ids_tile_ref[0], repeats, axis=1
+ ) # [block_q, block_k].
+ kv_segment_ids = pl.load(
+ kv_segment_ids_tile_ref, (slice(None), 0, k_slice)
+ ) # [1, block_k].
+ mask = jnp.equal(q_segment_ids, kv_segment_ids).astype(jnp.bool_)
+
+ if causal:
+ mask_shape = (block_q_major, block_k)
+ row_ids = jax.lax.broadcasted_iota(jnp.int32, mask_shape, 0)
+ row_ids += (q_seq_index + q_chunk_idx_start) * block_q_major
+ col_ids = jax.lax.broadcasted_iota(jnp.int32, mask_shape, 1)
+ col_ids += (kv_seq_index + k_chunk_idx_start) * block_k_major + i * block_k
+ causal_mask = col_ids <= row_ids
+ mask = causal_mask if mask is None else jnp.logical_and(mask, causal_mask)
+ capped_logits = (
+ capped_logits
+ if mask is None
+ else capped_logits + jnp.where(mask, 0.0, mask_value)
+ )
+
+ p = jnp.exp(capped_logits - pltpu.repeat(m, block_k // MIN_BLOCK_SIZE, axis=1))
+ p = p * pltpu.repeat(
+ 1 / l, block_k // MIN_BLOCK_SIZE, axis=1
+ ) # [block_q_major, block_k]
+
+ # di: [block_q_major, 128]
+ # do: [block_q_major, head_dim]
+ # v: [block_k_major, head_dim]
+ dp = jax.lax.dot_general(
+ do,
+ v,
+ TRANS_B_DIM_NUMBERS,
+ preferred_element_type=jnp.float32,
+ )
+ ds = (dp - pltpu.repeat(di, block_k // MIN_BLOCK_SIZE, axis=1)) * p
+
+ if sm_scale != 1.0:
+ ds = ds * sm_scale
+
+ if ds_tile_ref is not None:
+ pl.store(
+ ds_tile_ref,
+ (0, pl.dslice(None), pl.dslice(i * block_k, block_k)),
+ ds.astype(ds_tile_ref.dtype),
+ )
+
+ # dp: [block_q_major, block_k]
+ # k: [block_k, head_dim]
+ dq_scratch_ref[:, :] += lax.dot(
+ ds.astype(k.dtype),
+ k,
+ preferred_element_type=jnp.float32,
+ ).astype(dq_scratch_ref.dtype)
+
+ if causal:
+ should_run = below_or_on_diag(
+ q_seq_index + q_chunk_idx_start,
+ block_q_major,
+ kv_seq_index + k_chunk_idx_start,
+ block_k_major,
+ )
+ should_not_run = lax.select(should_run, False, True)
+ else:
+ should_run = True
+ should_not_run = False # type: ignore
+
+ @pl.when(should_run)
+ def run():
+ lax.fori_loop(0, block_k_major // block_k, body, None, unroll=True)
+
+ @pl.when(should_not_run)
+ def zero_out_ds():
+ if ds_tile_ref is not None:
+ ds_tile_ref[...] = jnp.zeros_like(ds_tile_ref)
+
+ @pl.when(kv_seq_index == kv_seq_len // block_k_major - 1)
+ def end_of_kv_sequence():
+ dq_tile_ref[0, 0, :, :] = dq_scratch_ref[...].astype(dq_tile_ref)
+ dq_scratch_ref[...] = jnp.zeros_like(dq_scratch_ref)
+
+
+def _flash_attention_bwd_dq(
+ q_chunk_idx_start,
+ k_chunk_idx_start,
+ q,
+ k,
+ v,
+ ab,
+ segment_ids,
+ l,
+ m,
+ do,
+ di,
+ *,
+ block_q_major: int | None,
+ block_k_major: int | None,
+ block_k: int | None,
+ sm_scale: float,
+ causal: bool,
+ mask_value: float,
+ debug: bool,
+):
+ batch_size, num_heads, q_seq_len, head_dim = q.shape
+ _, _, kv_seq_len, _ = k.shape
+ q_chunk_idx_start, k_chunk_idx_start = (
+ q_chunk_idx_start[None],
+ k_chunk_idx_start[None],
+ )
+ _verify_block("block_q_dq", "q_seq_len", block_q_major, q_seq_len)
+ _verify_block("block_k_major_dq", "kv_seq_len", block_k_major, kv_seq_len)
+ _verify_block("block_k_dq", "block_k", block_k, kv_seq_len)
+
+ # Broadcast out scalar values
+ m = jnp.broadcast_to(m[..., None], (*m.shape, MIN_BLOCK_SIZE))
+ l = jnp.broadcast_to(l[..., None], (*l.shape, MIN_BLOCK_SIZE))
+ # Preprocess contraction for bwd pass
+ di = jnp.broadcast_to(di[..., None], (*di.shape, block_k_major))
+
+ grid = (
+ batch_size,
+ num_heads,
+ q_seq_len // block_q_major,
+ kv_seq_len // block_k_major,
+ )
+
+ def qo_index_map(batch_index, head_index, q_seq_index, _, q_idx_ref, k_idx_ref):
+ return (batch_index, head_index, q_seq_index, 0)
+
+ qo_spec = pl.BlockSpec(qo_index_map, (1, 1, block_q_major, head_dim))
+ do_spec = qo_spec
+
+ def kv_index_map(
+ batch_index, head_index, q_seq_index, kv_seq_index, q_idx_ref, k_idx_ref
+ ):
+ if causal:
+ # If the kv block is skipped, prefetch the next valid kv block, i.e. the
+ # 0th one to be used for the next block_q rows.
+ next_kv_index = lax.select(
+ below_or_on_diag(
+ q_seq_index + q_idx_ref[0],
+ block_q_major,
+ kv_seq_index + k_idx_ref[0],
+ block_k_major,
+ ),
+ kv_seq_index,
+ 0,
+ )
+ else:
+ next_kv_index = kv_seq_index
+ return (batch_index, head_index, next_kv_index, 0)
+
+ kv_spec = pl.BlockSpec(kv_index_map, (1, 1, block_k_major, head_dim))
+ assert kv_spec.block_shape is not None
+ assert k.ndim == len(kv_spec.block_shape)
+ assert v.ndim == len(kv_spec.block_shape)
+
+ def lm_index_map(batch_index, head_index, q_seq_index, _, q_idx_ref, k_idx_ref):
+ return (batch_index, head_index, q_seq_index, 0)
+
+ lm_spec = pl.BlockSpec(lm_index_map, (1, 1, block_q_major, MIN_BLOCK_SIZE))
+ assert lm_spec.block_shape is not None
+ assert l.ndim == len(lm_spec.block_shape)
+ assert m.ndim == len(lm_spec.block_shape)
+
+ di_spec = pl.BlockSpec(qo_index_map, (1, 1, block_q_major, MIN_BLOCK_SIZE))
+ assert di_spec.block_shape is not None
+ assert di.ndim == len(di_spec.block_shape)
+
+ def ab_index_map(
+ batch_index, head_index, q_seq_index, kv_seq_index, q_idx_ref, k_idx_ref
+ ):
+ return (batch_index, 0, kv_seq_index)
+
+ if ab is not None:
+ ab = ab[:, None].repeat(block_q_major, axis=1)
+
+ dab_spec = (
+ pl.BlockSpec(ab_index_map, (1, block_q_major, block_k_major))
+ if ab is not None
+ else None
+ )
+
+ q_segment_ids_spec = kv_segment_ids_spec = None
+ q_segment_ids = kv_segment_ids = None
+ if segment_ids is not None:
+
+ def q_segment_ids_index_map(
+ batch_index, head_index, q_seq_index, _, q_idx_ref, k_idx_ref
+ ):
+ del head_index
+ return (batch_index, q_seq_index, 0)
+
+ def kv_segment_ids_index_map(
+ batch_index, head_index, q_seq_index, kv_seq_index, q_idx_ref, k_idx_ref
+ ):
+ del head_index
+ if causal:
+ # If the kv block is skipped, prefetch the next valid kv block, i.e. the
+ # 0th one to be used for the next block_q rows.
+ next_kv_index = lax.select(
+ below_or_on_diag(
+ q_seq_index + q_idx_ref[0],
+ block_q_major,
+ kv_seq_index + k_idx_ref[0],
+ block_k_major,
+ ),
+ kv_seq_index,
+ 0,
+ )
+ else:
+ next_kv_index = kv_seq_index
+ return (batch_index, 0, next_kv_index)
+
+ q_segment_ids_spec = pl.BlockSpec(
+ q_segment_ids_index_map, (1, block_q_major, NUM_LANES)
+ )
+ kv_segment_ids_spec = pl.BlockSpec(
+ kv_segment_ids_index_map, (1, NUM_SUBLANES, block_k_major)
+ )
+
+ q_segment_ids = jax.lax.broadcast_in_dim(
+ segment_ids.q,
+ (batch_size, q_seq_len, NUM_LANES),
+ (
+ 0,
+ 1,
+ ),
+ )
+ kv_segment_ids = jax.lax.broadcast_in_dim(
+ segment_ids.kv,
+ (batch_size, NUM_SUBLANES, kv_seq_len),
+ (
+ 0,
+ 2,
+ ),
+ )
+
+ in_specs = [
+ qo_spec,
+ kv_spec,
+ kv_spec,
+ dab_spec,
+ q_segment_ids_spec,
+ kv_segment_ids_spec,
+ lm_spec,
+ lm_spec,
+ do_spec,
+ di_spec,
+ ]
+
+ out_shapes = [
+ jax.ShapeDtypeStruct(q.shape, q.dtype),
+ jax.ShapeDtypeStruct((block_q_major, head_dim), jnp.float32),
+ jax.ShapeDtypeStruct(ab.shape, ab.dtype) if ab is not None else None,
+ ]
+ dq_spec = pl.BlockSpec(qo_index_map, (1, 1, block_q_major, head_dim))
+ out_specs = [
+ dq_spec,
+ pl.BlockSpec(lambda *_: (0, 0), (block_q_major, head_dim)),
+ dab_spec,
+ ]
+
+ kernel = functools.partial(
+ _flash_attention_dq_kernel,
+ sm_scale=sm_scale,
+ causal=causal,
+ mask_value=mask_value,
+ block_k=block_k,
+ kv_seq_len=kv_seq_len,
+ )
+ name_scope = f"flash_mha_bwd_dq_{block_q_major=}_{block_k_major=}_{block_k=}"
+ with jax.named_scope(name_scope):
+ dq, _, ds = pl.pallas_call(
+ kernel,
+ out_shape=out_shapes,
+ grid_spec=pltpu.PrefetchScalarGridSpec(
+ num_scalar_prefetch=2, in_specs=in_specs, out_specs=out_specs, grid=grid
+ ),
+ debug=debug,
+ mosaic_params=dict(
+ dimension_semantics=(
+ "parallel",
+ "parallel",
+ "parallel",
+ "arbitrary",
+ )
+ ),
+ )(
+ q_chunk_idx_start,
+ k_chunk_idx_start,
+ q,
+ k,
+ v,
+ ab,
+ q_segment_ids,
+ kv_segment_ids,
+ l,
+ m,
+ do,
+ di,
+ )
+
+ return dq, ds
+
+
+def _verify_block(block_name, dim_name, block, dim, should_divide=True):
+ if block > dim:
+ raise ValueError(
+ f"{block_name}={block} should be smaller or equal to {dim_name}={dim}"
+ )
+ if should_divide and dim % block != 0:
+ raise ValueError(
+ f"{dim_name}={dim} should be divisible by {block_name}={block}"
+ )
diff --git a/lwm/train.py b/lwm/train.py
new file mode 100644
index 0000000..1e1fe28
--- /dev/null
+++ b/lwm/train.py
@@ -0,0 +1,396 @@
+import pprint
+import os
+from functools import partial
+
+from tqdm import tqdm, trange
+import numpy as np
+from absl.app import run
+import absl.logging as logging
+import tux
+
+import jax
+import jax.numpy as jnp
+from jax.experimental.pjit import pjit
+from jax.sharding import PartitionSpec as PS
+from flax.training.train_state import TrainState
+
+from lwm.data import DatasetFactory
+from tux import (
+ JaxRNG, JaxDistributedConfig, next_rng, match_partition_rules,
+ cross_entropy_loss_and_accuracy, global_norm, get_float_dtype_by_name,
+ set_random_seed, average_metrics, get_mask,
+ make_shard_and_gather_fns, with_sharding_constraint, define_flags_with_default,
+ OptimizerFactory, StreamingCheckpointer
+)
+from lwm.llama import LLaMAConfig, FlaxLLaMAForCausalLMModule
+from lwm.vision_llama import VideoLLaMAConfig, FlaxVideoLLaMAForCausalLMModule
+
+
+FLAGS, FLAGS_DEF = define_flags_with_default(
+ modality='text',
+ use_data_sharded_loader=True,
+ seed=42,
+ mesh_dim='1,-1,1,1',
+ dtype='fp32',
+ total_steps=10000,
+ load_llama_config='',
+ update_llama_config='',
+ load_checkpoint='',
+ load_dataset_state='',
+ log_freq=50,
+ save_model_freq=0,
+ save_milestone_freq=0,
+ eval_steps=0,
+ tokenizer=VideoLLaMAConfig.get_tokenizer_config(),
+ train_dataset=DatasetFactory.get_default_config(),
+ eval_dataset=DatasetFactory.get_default_config(),
+ optimizer=OptimizerFactory.get_default_config(),
+ checkpointer=StreamingCheckpointer.get_default_config(),
+ llama=VideoLLaMAConfig.get_default_config(),
+ logger=tux.WandBLogger.get_default_config(),
+ log_all_worker=False,
+ jax_distributed=JaxDistributedConfig.get_default_config(),
+ autoresume=False,
+)
+
+
+def main(argv):
+ JaxDistributedConfig.initialize(FLAGS.jax_distributed)
+ variant = tux.get_user_flags(FLAGS, FLAGS_DEF)
+ flags_config_dict = tux.user_flags_to_config_dict(FLAGS, FLAGS_DEF)
+
+ logger = tux.WandBLogger(
+ config=FLAGS.logger,
+ variant=variant,
+ enable=FLAGS.log_all_worker or (jax.process_index() == 0),
+ )
+ set_random_seed(FLAGS.seed)
+
+ if jax.process_index() == 0:
+ output_dir = logger.output_dir
+ else:
+ output_dir = os.path.join(logger.output_dir, logger.experiment_id)
+
+ if FLAGS.modality == 'text':
+ config_cls = LLaMAConfig
+ llama_cls = FlaxLLaMAForCausalLMModule
+ elif FLAGS.modality == 'vision,text':
+ config_cls = VideoLLaMAConfig
+ llama_cls = FlaxVideoLLaMAForCausalLMModule
+ else:
+ raise ValueError(f"Unsupported modality: {FLAGS.modality}")
+
+ mesh = config_cls.get_jax_mesh(FLAGS.mesh_dim)
+ node_info = config_cls.get_ranks_and_size(mesh)
+
+ tokenizer = config_cls.get_tokenizer(FLAGS.tokenizer)
+ dataset = DatasetFactory.load_dataset(FLAGS.train_dataset, tokenizer, node_info=node_info)
+ if FLAGS.autoresume and tux.check_exists(output_dir):
+ logging.info('Found existing output. Resuming dataset from latest checkpoint...')
+ resume_path = f"{output_dir}/dataset.pkl"
+ dataset.load_state_dict(tux.load_pickle(resume_path))
+ elif FLAGS.load_dataset_state != '':
+ dataset.load_state_dict(tux.load_pickle(FLAGS.load_dataset_state))
+
+ if FLAGS.eval_steps > 0:
+ eval_dataset = DatasetFactory.load_dataset(
+ FLAGS.eval_dataset, dataset.tokenizer
+ )
+ eval_iterator = iter(eval_dataset)
+
+ seq_length = dataset.seq_length
+
+ if FLAGS.load_llama_config != '':
+ llama_config = config_cls.load_config(FLAGS.load_llama_config)
+ updates = config_cls(**FLAGS.llama)
+ llama_config.update(dict(
+ remat_block=updates.remat_block,
+ remat_attention=updates.remat_attention,
+ remat_mlp=updates.remat_mlp,
+ scan_attention=updates.scan_attention,
+ scan_mlp=updates.scan_mlp,
+ scan_query_chunk_size=updates.scan_query_chunk_size,
+ scan_key_chunk_size=updates.scan_key_chunk_size,
+ scan_mlp_chunk_size=updates.scan_mlp_chunk_size,
+ scan_layers=updates.scan_layers,
+ param_scan_axis=updates.param_scan_axis,
+ ))
+ else:
+ llama_config = config_cls(**FLAGS.llama)
+
+ if FLAGS.update_llama_config != '':
+ llama_config.update(dict(eval(FLAGS.update_llama_config)))
+
+ llama_config.update(dict(
+ bos_token_id=dataset.tokenizer.bos_token_id,
+ eos_token_id=dataset.tokenizer.eos_token_id,
+ ))
+ if llama_config.vocab_size < dataset.vocab_size:
+ llama_config.update(dict(vocab_size=dataset.vocab_size))
+ llama_config.update(dict(mesh_dim=FLAGS.mesh_dim))
+
+ model = llama_cls(
+ llama_config, dtype=get_float_dtype_by_name(FLAGS.dtype)
+ )
+
+ optimizer, optimizer_info = OptimizerFactory.get_optimizer(
+ FLAGS.optimizer,
+ get_mask(config_cls.get_weight_decay_exclusions()),
+ None,
+ )
+
+ def create_trainstate_from_params(params):
+ return TrainState.create(params=params, tx=optimizer, apply_fn=None)
+
+ def init_fn(rng):
+ rng_generator = JaxRNG(rng)
+ batch = 512
+ if FLAGS.modality == 'text':
+ params = model.init(
+ input_ids=jnp.zeros((batch, seq_length), dtype=jnp.int32),
+ position_ids=jnp.zeros((batch, seq_length), dtype=jnp.int32),
+ attention_mask=jnp.ones((batch, seq_length), dtype=jnp.int32),
+ rngs=rng_generator(llama_config.rng_keys()),
+ )
+ elif FLAGS.modality == 'vision,text':
+ params = model.init(
+ input_ids=jnp.zeros((batch, seq_length), dtype=jnp.int32),
+ vision_masks=jnp.zeros((batch, seq_length), dtype=bool),
+ position_ids=jnp.zeros((batch, seq_length), dtype=jnp.int32),
+ attention_mask=jnp.ones((batch, seq_length), dtype=jnp.int32),
+ rngs=rng_generator(llama_config.rng_keys()),
+ )
+ else:
+ raise ValueError(f"Unsupported modality: {FLAGS.modality}")
+ return TrainState.create(params=params, tx=optimizer, apply_fn=None)
+
+ def train_step(train_state, rng, batch):
+ rng_generator = JaxRNG(rng)
+ batch = with_sharding_constraint(batch, PS(('dp', 'fsdp'), 'sp'))
+ def loss_and_accuracy(params):
+ if FLAGS.modality == 'text':
+ logits = model.apply(
+ params,
+ batch['input_tokens'],
+ deterministic=False,
+ rngs=rng_generator(llama_config.rng_keys()),
+ ).logits
+ loss, acc = cross_entropy_loss_and_accuracy(
+ logits,
+ batch['target_tokens'],
+ batch['loss_masks']
+ )
+ metrics = dict(acc=acc)
+ return loss, metrics
+ elif FLAGS.modality == 'vision,text':
+ vision_logits, text_logits = model.apply(
+ params,
+ batch['input_tokens'],
+ batch['input_vision_masks'],
+ deterministic=False,
+ rngs=rng_generator(llama_config.rng_keys()),
+ ).logits
+ vision_loss, vision_acc = cross_entropy_loss_and_accuracy(
+ vision_logits,
+ jnp.where(batch['target_vision_masks'], batch['target_tokens'], 0),
+ batch['loss_masks'] * batch['target_vision_masks']
+ )
+ text_loss, text_acc = cross_entropy_loss_and_accuracy(
+ text_logits,
+ jnp.where(batch['target_vision_masks'], 0, batch['target_tokens']),
+ batch['loss_masks'] * (1.0 - batch['target_vision_masks'])
+ )
+ loss = 0.5 * (vision_loss + text_loss)
+
+ metrics = dict(
+ vision_loss=vision_loss,
+ vision_acc=vision_acc,
+ text_loss=text_loss,
+ text_acc=text_acc,
+ )
+ else:
+ raise ValueError(f"Unsupported modality: {FLAGS.modality}")
+ return loss, metrics
+ grad_fn = jax.value_and_grad(loss_and_accuracy, has_aux=True)
+ (loss, loss_metrics), grads = grad_fn(train_state.params)
+ train_state = train_state.apply_gradients(grads=grads)
+ metrics = dict(
+ loss=loss,
+ learning_rate=optimizer_info['learning_rate_schedule'](train_state.step),
+ param_norm=global_norm(train_state.params),
+ gradient_norm=global_norm(grads),
+ **loss_metrics
+ )
+ return train_state, rng_generator(), metrics
+
+ def eval_step(train_state, rng, batch):
+ rng_generator = JaxRNG(rng)
+ batch = with_sharding_constraint(batch, PS(('dp', 'fsdp'), 'sp'))
+ if FLAGS.modality == 'text':
+ logits = model.apply(
+ train_state.params,
+ batch['input_tokens'],
+ deterministic=True,
+ rngs=rng_generator(llama_config.rng_keys()),
+ ).logits
+ loss, acc = cross_entropy_loss_and_accuracy(
+ logits,
+ batch['target_tokens'],
+ batch['loss_masks']
+ )
+ metrics = dict(
+ eval_loss=loss,
+ eval_acc=acc,
+ )
+ elif FLAGS.modality == 'vision,text':
+ vision_logits, text_logits = model.apply(
+ train_state.params,
+ batch['input_tokens'],
+ batch['input_vision_masks'],
+ deterministic=True,
+ rngs=rng_generator(llama_config.rng_keys()),
+ ).logits
+ vision_loss, vision_acc = cross_entropy_loss_and_accuracy(
+ vision_logits,
+ jnp.where(batch['target_vision_masks'], batch['target_tokens'], 0),
+ batch['loss_masks'] * batch['target_vision_masks']
+ )
+ text_loss, text_acc = cross_entropy_loss_and_accuracy(
+ text_logits,
+ jnp.where(batch['target_vision_masks'], 0, batch['target_tokens']),
+ batch['loss_masks'] * (1.0 - batch['target_vision_masks'])
+ )
+ loss = 0.5 * (vision_loss + text_loss)
+ metrics = dict(
+ eval_loss=loss,
+ eval_vision_accuracy=vision_acc,
+ eval_vision_loss=vision_loss,
+ eval_text_accuracy=text_acc,
+ eval_text_loss=text_loss,
+ )
+ return rng_generator(), metrics
+
+ train_state_shapes = jax.eval_shape(init_fn, next_rng())
+ train_state_partition = match_partition_rules(
+ config_cls.get_partition_rules(llama_config.scan_layers, llama_config.param_scan_axis), train_state_shapes
+ )
+
+ shard_fns, gather_fns = make_shard_and_gather_fns(
+ train_state_partition, train_state_shapes
+ )
+ checkpointer = StreamingCheckpointer(
+ FLAGS.checkpointer, logger.output_dir,
+ enable=jax.process_index() == 0,
+ )
+
+ sharded_init_fn = pjit(
+ init_fn,
+ in_shardings=PS(),
+ out_shardings=train_state_partition
+ )
+
+ sharded_create_trainstate_from_params = pjit(
+ create_trainstate_from_params,
+ in_shardings=(train_state_partition.params, ),
+ out_shardings=train_state_partition,
+ donate_argnums=(0, ),
+ )
+
+ if FLAGS.use_data_sharded_loader:
+ batch_spec = PS(('dp', 'fsdp'), 'sp')
+ else:
+ batch_spec = PS()
+ sharded_train_step = pjit(
+ train_step,
+ in_shardings=(train_state_partition, PS(), batch_spec),
+ out_shardings=(train_state_partition, PS(), PS()),
+ donate_argnums=(0, 1),
+ )
+
+ sharded_eval_step = pjit(
+ eval_step,
+ in_shardings=(train_state_partition, PS(), PS()),
+ out_shardings=(PS(), PS()),
+ donate_argnums=(1,),
+ )
+
+ def save_checkpoint(train_state, milestone=False):
+ step = int(jax.device_get(train_state.step))
+ metadata = dict(
+ step=step,
+ variant=variant,
+ flags=flags_config_dict,
+ llama_config=llama_config.to_dict(),
+ )
+ checkpointer.save_all(
+ train_state=train_state,
+ gather_fns=gather_fns,
+ metadata=metadata,
+ dataset=dataset.get_state_dict(),
+ milestone=milestone,
+ )
+
+ with mesh:
+ train_state, restored_params = None, None
+
+ if FLAGS.autoresume and tux.check_exists(output_dir):
+ logging.info('Found existing output. Resuming model from latest checkpoint...')
+ resume_path = f"trainstate::{output_dir}/streaming_train_state"
+ train_state, restored_params = checkpointer.load_trainstate_checkpoint(
+ resume_path, train_state_shapes, shard_fns, max_buffer_size=32 * 2 ** 30
+ )
+ elif FLAGS.load_checkpoint != '':
+ train_state, restored_params = checkpointer.load_trainstate_checkpoint(
+ FLAGS.load_checkpoint, train_state_shapes, shard_fns, max_buffer_size=32 * 2 ** 30
+ )
+
+ if train_state is None and restored_params is None:
+ # Initialize from scratch
+ train_state = sharded_init_fn(next_rng())
+ elif train_state is None and restored_params is not None:
+ # Restore from params but initialize train_state
+ train_state = sharded_create_trainstate_from_params(restored_params)
+ del restored_params
+
+ start_step = int(jax.device_get(train_state.step))
+
+ if FLAGS.save_model_freq > 0:
+ save_checkpoint(train_state)
+
+ sharded_rng = next_rng()
+
+ step_counter = trange(start_step, FLAGS.total_steps, ncols=0)
+ for step, (batch, dataset_metrics) in zip(step_counter, dataset):
+ train_state, sharded_rng, metrics = sharded_train_step(
+ train_state, sharded_rng, batch
+ )
+ if step % FLAGS.log_freq == 0:
+ if FLAGS.eval_steps > 0:
+ eval_metric_list = []
+ for _ in range(FLAGS.eval_steps):
+ eval_batch, _ = next(eval_iterator)
+ sharded_rng, eval_metrics = sharded_eval_step(
+ train_state, sharded_rng, eval_batch
+ )
+ eval_metrics = jax.device_get(eval_metrics)
+ eval_metric_list.append(eval_metrics)
+ metrics.update(average_metrics(eval_metric_list))
+
+ log_metrics = {"step": step}
+ log_metrics.update(metrics)
+ log_metrics.update(dataset_metrics)
+ log_metrics = jax.device_get(log_metrics)
+ logger.log(log_metrics)
+ tqdm.write("\n" + pprint.pformat(log_metrics) + "\n")
+
+ if FLAGS.save_milestone_freq > 0 and (step + 1) % FLAGS.save_milestone_freq == 0:
+ save_checkpoint(train_state, milestone=True)
+ elif FLAGS.save_model_freq > 0 and (step + 1) % FLAGS.save_model_freq == 0:
+ save_checkpoint(train_state)
+
+ if FLAGS.save_model_freq > 0:
+ save_checkpoint(train_state)
+
+
+if __name__ == "__main__":
+ run(main)
diff --git a/lwm/vision_chat.py b/lwm/vision_chat.py
new file mode 100644
index 0000000..f398d11
--- /dev/null
+++ b/lwm/vision_chat.py
@@ -0,0 +1,254 @@
+from absl.app import run
+import math
+from tqdm import tqdm
+from PIL import Image
+import decord
+from functools import cached_property
+import numpy as np
+import jax
+from jax.experimental.pjit import pjit
+from jax.sharding import PartitionSpec as PS
+from transformers import GenerationConfig
+from tux import (
+ define_flags_with_default, StreamingCheckpointer, JaxDistributedConfig,
+ set_random_seed, get_float_dtype_by_name, JaxRNG, next_rng,
+ match_partition_rules, make_shard_and_gather_fns,
+ with_sharding_constraint, tree_apply, open_file
+)
+from lwm.vision_llama import VideoLLaMAConfig, FlaxVideoLLaMAForCausalLM
+from lwm.vqgan import VQGAN
+
+
+FLAGS, FLAGS_DEF = define_flags_with_default(
+ prompt="",
+ input_file="",
+ vqgan_checkpoint="",
+ temperature=0.2,
+ max_n_frames=8,
+ seed=1234,
+ mesh_dim='1,-1,1,1',
+ dtype='fp32',
+ load_llama_config='',
+ update_llama_config='',
+ load_checkpoint='',
+ tokenizer=VideoLLaMAConfig.get_tokenizer_config(),
+ llama=VideoLLaMAConfig.get_default_config(),
+ jax_distributed=JaxDistributedConfig.get_default_config(),
+)
+
+
+class Sampler:
+ def __init__(self):
+ self.mesh = VideoLLaMAConfig.get_jax_mesh(FLAGS.mesh_dim)
+ self.vqgan = VQGAN(FLAGS.vqgan_checkpoint, replicate=False)
+ self.prefix_tokenizer = VideoLLaMAConfig.get_tokenizer(
+ FLAGS.tokenizer, truncation_side='left', padding_side='left'
+ )
+ self.tokenizer = VideoLLaMAConfig.get_tokenizer(FLAGS.tokenizer)
+ self.n_tokens_per_frame = 257
+ self.min_buffer_size = 256
+ self.sharded_rng = next_rng()
+ self._load_model()
+
+ @property
+ def block_size(self):
+ return max(self.config.scan_query_chunk_size, self.config.scan_key_chunk_size) * self.mesh.shape['sp']
+
+ @property
+ def data_dim(self):
+ return self.mesh.shape['dp'] * self.mesh.shape['fsdp']
+
+ def _process_frame(self, image, size):
+ width, height = image.size
+ if width < height:
+ new_width = size
+ new_height = int(size * height / width)
+ else:
+ new_height = size
+ new_width = int(size * width / height)
+ image = image.resize((new_width, new_height))
+
+ left = (new_width - size) / 2
+ top = (new_height - size) / 2
+ right = (new_width + size) / 2
+ bottom = (new_height + size) / 2
+ image = image.crop((left, top, right, bottom))
+ return np.array(image, dtype=np.float32) / 127.5 - 1
+
+ def _read_process_vision(self, path, max_n_frames):
+ f = open_file(path, 'rb')
+ if path.endswith('.png') or path.endswith('.jpg'):
+ image = Image.open(f)
+ vision = self._process_frame(image, 256)[None]
+ else:
+ vr = decord.VideoReader(f, ctx=decord.cpu(0))
+ duration = len(vr)
+ if duration <= max_n_frames:
+ frame_id_list = list(range(duration))
+ else:
+ frame_id_list = np.linspace(0, duration - 1, max_n_frames, dtype=int).tolist()
+ video = vr.get_batch(frame_id_list).asnumpy()
+ vision = np.stack([self._process_frame(Image.fromarray(frame), 256) for frame in video])
+
+ B = 1
+ encodings = []
+ for i in range(0, len(vision), 1):
+ v = vision[i:i + B]
+ if len(v) % B == 0:
+ n_pad = 0
+ else:
+ n_pad = B - len(v) % B
+ v = np.pad(v, ((n_pad, 0), (0, 0), (0, 0), (0, 0)))
+ enc = jax.device_get(self.vqgan.encode(v))[1].astype(int)
+ enc = enc[n_pad:]
+ for t in range(len(enc)):
+ encodings.extend(enc[t].reshape(-1).tolist())
+ if t == len(enc) - 1:
+ encodings.append(8193)
+ else:
+ encodings.append(8192)
+ return encodings
+
+ def construct_input(self, prompts, max_n_frames):
+ max_input_length = max_n_frames * self.n_tokens_per_frame + self.min_buffer_size
+ max_input_length = int(math.ceil(max_input_length / self.block_size) * self.block_size)
+
+ vision_start = self.tokenizer.encode('')
+ vision_end = self.tokenizer.encode('')
+
+ input_ids = np.zeros((len(prompts), max_input_length), dtype=int)
+ vision_masks = np.zeros((len(prompts), max_input_length), dtype=bool)
+ attention_mask = np.zeros((len(prompts), max_input_length), dtype=int)
+ for i, prompt in enumerate(tqdm(prompts)):
+ vision = self._read_process_vision(prompt['input_path'], max_n_frames)
+ text_1 = self.tokenizer.encode(f"You are a helpful assistant. USER: {prompt['question']}\n")
+ tail = self.tokenizer.encode(" ASSISTANT:")
+
+ tokens, vm = [], []
+ tokens.extend(text_1)
+ vm.extend([False] * len(text_1))
+ tokens.extend(vision_start)
+ vm.extend([False] * len(vision_start))
+ tokens.extend(vision)
+ vm.extend([True] * len(vision))
+ tokens.extend(vision_end)
+ vm.extend([False] * len(vision_end))
+ tokens.extend(tail)
+ vm.extend([False] * len(tail))
+ assert len(tokens) < max_input_length, (len(tokens), max_input_length)
+ assert len(tokens) == len(vm)
+ input_ids[i, -len(tokens):] = tokens
+ vision_masks[i, -len(tokens):] = vm
+ attention_mask[i, -len(tokens):] = 1
+ return {
+ 'input_ids': input_ids,
+ 'vision_masks': vision_masks,
+ 'attention_mask': attention_mask
+ }
+
+
+ def _load_model(self):
+ if FLAGS.load_llama_config != '':
+ llama_config = VideoLLaMAConfig.load_config(FLAGS.load_llama_config)
+ updates = VideoLLaMAConfig(**FLAGS.llama)
+ llama_config.update(dict(
+ remat_block=updates.remat_block,
+ remat_attention=updates.remat_attention,
+ remat_mlp=updates.remat_mlp,
+ scan_attention=updates.scan_attention,
+ scan_mlp=updates.scan_mlp,
+ scan_query_chunk_size=updates.scan_query_chunk_size,
+ scan_key_chunk_size=updates.scan_key_chunk_size,
+ scan_mlp_chunk_size=updates.scan_mlp_chunk_size,
+ scan_layers=updates.scan_layers,
+ param_scan_axis=updates.param_scan_axis,
+ ))
+ else:
+ llama_config = VideoLLaMAConfig(**FLAGS.llama)
+
+ if FLAGS.update_llama_config != '':
+ llama_config.update(dict(eval(FLAGS.update_llama_config)))
+
+ llama_config.update(dict(
+ bos_token_id=self.tokenizer.bos_token_id,
+ eos_token_id=self.tokenizer.eos_token_id,
+ ))
+ llama_config.update(dict(mesh_dim=FLAGS.mesh_dim))
+ self.config = llama_config
+
+ self.model = FlaxVideoLLaMAForCausalLM(
+ llama_config,
+ input_shape=(512, self.block_size),
+ seed=FLAGS.seed,
+ _do_init=False,
+ dtype=get_float_dtype_by_name(FLAGS.dtype),
+ )
+
+ with jax.default_device(jax.devices("cpu")[0]):
+ _, self.params = StreamingCheckpointer.load_trainstate_checkpoint(
+ FLAGS.load_checkpoint, disallow_trainstate=True, max_buffer_size=32 * 2 ** 30
+ )
+ self.model_ps = match_partition_rules(
+ VideoLLaMAConfig.get_partition_rules(llama_config.scan_layers, llama_config.param_scan_axis), self.params
+ )
+ shard_fns, _ = make_shard_and_gather_fns(
+ self.model_ps, get_float_dtype_by_name(FLAGS.dtype)
+ )
+
+ with self.mesh:
+ self.params = tree_apply(shard_fns, self.params)
+
+ @cached_property
+ def _forward_generate(self):
+ def fn(params, rng, batch):
+ batch = with_sharding_constraint(batch, PS(('dp', 'fsdp'), 'sp'))
+ rng_generator = JaxRNG(rng)
+ output = self.model.generate(
+ batch['input_ids'],
+ vision_masks=batch['vision_masks'],
+ attention_mask=batch['attention_mask'],
+ params=params['params'],
+ prng_key=rng_generator(),
+ generation_config=GenerationConfig(
+ max_new_tokens=self.block_size,
+ pad_token_id=self.tokenizer.pad_token_id,
+ eos_token_id=self.tokenizer.eos_token_id,
+ temperature=FLAGS.temperature,
+ do_sample=True,
+ )
+ ).sequences[:, batch['input_ids'].shape[1]:]
+ return output, rng_generator()
+ return pjit(
+ fn,
+ in_shardings=(self.model_ps, PS(), PS()),
+ out_shardings=(PS(), PS())
+ )
+
+ def __call__(self, prompts, max_n_frames):
+ batch = self.construct_input(prompts, max_n_frames)
+ with self.mesh:
+ output, self.sharded_rng = self._forward_generate(
+ self.params, self.sharded_rng, batch
+ )
+ output = jax.device_get(output)
+ output_text = []
+ for text in list(self.tokenizer.batch_decode(output, skip_special_tokens=True)):
+ if self.tokenizer.eos_token in text:
+ text = text.split(self.tokenizer.eos_token, maxsplit=1)[0]
+ output_text.append(text)
+ return output_text
+
+def main(argv):
+ assert FLAGS.prompt != ''
+ assert FLAGS.input_file != ''
+
+ JaxDistributedConfig.initialize(FLAGS.jax_distributed)
+ set_random_seed(FLAGS.seed)
+
+ prompts = [{'input_path': FLAGS.input_file, 'question': FLAGS.prompt}]
+ sampler = Sampler()
+ output = sampler(prompts, FLAGS.max_n_frames)[0]
+ print(f"Question: {FLAGS.prompt}\nAnswer: {output}")
+
+if __name__ == "__main__":
+ run(main)
diff --git a/lwm/vision_generation.py b/lwm/vision_generation.py
new file mode 100644
index 0000000..c903402
--- /dev/null
+++ b/lwm/vision_generation.py
@@ -0,0 +1,258 @@
+from absl.app import run
+from tqdm import tqdm
+import imageio
+import numpy as np
+from PIL import Image
+from transformers import GenerationConfig
+import jax
+import jax.numpy as jnp
+from jax.experimental.pjit import pjit
+from jax.sharding import PartitionSpec as PS
+from tux import (
+ define_flags_with_default, StreamingCheckpointer, JaxDistributedConfig,
+ set_random_seed, get_float_dtype_by_name, JaxRNG,
+ match_partition_rules, make_shard_and_gather_fns,
+ with_sharding_constraint, tree_apply, next_rng
+)
+from lwm.vision_llama import VideoLLaMAConfig, FlaxVideoLLaMAForCausalLM
+from lwm.vqgan import VQGAN
+
+
+FLAGS, FLAGS_DEF = define_flags_with_default(
+ prompt='Fireworks over the city',
+ output_file='',
+ temperature_image=1.0,
+ temperature_video=1.0,
+ top_k_image=8192,
+ top_k_video=100,
+ cfg_scale_image=1.0,
+ cfg_scale_video=1.0,
+ vqgan_checkpoint='',
+ n_frames=1,
+ seed=1234,
+ mesh_dim='1,-1,1,1',
+ dtype='fp32',
+ load_llama_config='',
+ update_llama_config='',
+ load_checkpoint='',
+ tokenizer=VideoLLaMAConfig.get_tokenizer_config(),
+ llama=VideoLLaMAConfig.get_default_config(),
+ jax_distributed=JaxDistributedConfig.get_default_config(),
+)
+
+
+def main(argv):
+ assert FLAGS.output_file != ''
+ if FLAGS.output_file.endswith('mp4'):
+ assert FLAGS.n_frames > 1
+ elif FLAGS.output_file.endswith('png') or FLAGS.output_file.endswith('jpg'):
+ assert FLAGS.n_frames == 1
+ else:
+ raise ValueError(f"Unsupported output file extension: {FLAGS.output_file}")
+
+ JaxDistributedConfig.initialize(FLAGS.jax_distributed)
+ set_random_seed(FLAGS.seed)
+
+ tokens_per_frame = 257
+ vqgan = VQGAN(FLAGS.vqgan_checkpoint, replicate=False)
+ mesh = VideoLLaMAConfig.get_jax_mesh(FLAGS.mesh_dim)
+ tokenizer = VideoLLaMAConfig.get_tokenizer(FLAGS.tokenizer)
+ prefix_tokenizer = VideoLLaMAConfig.get_tokenizer(
+ FLAGS.tokenizer, truncation_side='left', padding_side='left'
+ )
+ if FLAGS.load_llama_config != '':
+ llama_config = VideoLLaMAConfig.load_config(FLAGS.load_llama_config)
+ updates = VideoLLaMAConfig(**FLAGS.llama)
+ llama_config.update(dict(
+ remat_block=updates.remat_block,
+ remat_attention=updates.remat_attention,
+ remat_mlp=updates.remat_mlp,
+ scan_attention=updates.scan_attention,
+ scan_mlp=updates.scan_mlp,
+ scan_query_chunk_size=updates.scan_query_chunk_size,
+ scan_key_chunk_size=updates.scan_key_chunk_size,
+ scan_mlp_chunk_size=updates.scan_mlp_chunk_size,
+ scan_layers=updates.scan_layers,
+ param_scan_axis=updates.param_scan_axis,
+ ))
+ else:
+ llama_config = VideoLLaMAConfig(**FLAGS.llama)
+
+ if FLAGS.update_llama_config != '':
+ llama_config.update(dict(eval(FLAGS.update_llama_config)))
+
+ llama_config.update(dict(
+ bos_token_id=tokenizer.bos_token_id,
+ eos_token_id=tokenizer.eos_token_id,
+ ))
+ llama_config.update(dict(mesh_dim=FLAGS.mesh_dim))
+
+ with jax.default_device(jax.devices("cpu")[0]):
+ _, params = StreamingCheckpointer.load_trainstate_checkpoint(
+ FLAGS.load_checkpoint, disallow_trainstate=True, max_buffer_size=32 * 2 ** 30
+ )
+ model = FlaxVideoLLaMAForCausalLM(
+ llama_config,
+ input_shape=(512, 8192),
+ seed=FLAGS.seed,
+ _do_init=False,
+ dtype=get_float_dtype_by_name(FLAGS.dtype),
+ )
+ model_ps = match_partition_rules(
+ VideoLLaMAConfig.get_partition_rules(llama_config.scan_layers, llama_config.param_scan_axis), params
+ )
+ shard_fns, _ = make_shard_and_gather_fns(
+ model_ps, get_float_dtype_by_name(FLAGS.dtype)
+ )
+
+ with mesh:
+ params = tree_apply(shard_fns, params)
+
+ def _forward_generate(params, rng, batch, n_tokens, cfg_scale, top_k, temperature):
+ batch = with_sharding_constraint(batch, PS(('dp', 'fsdp'), 'sp'))
+ cfg_scales = jnp.ones((batch['input_ids'].shape[0] // 2,), dtype=jnp.float32) * cfg_scale
+ cfg_scales = with_sharding_constraint(cfg_scales, PS(('dp', 'fsdp')))
+ rng_generator = JaxRNG(rng)
+ output = model.generate_vision(
+ batch['input_ids'],
+ cfg_scales,
+ attention_mask=batch['attention_mask'],
+ vision_masks=batch['vision_masks'],
+ params=params['params'],
+ prng_key=rng_generator(),
+ generation_config=GenerationConfig(
+ max_new_tokens=n_tokens,
+ min_new_tokens=n_tokens,
+ pad_token_id=tokenizer.pad_token_id,
+ temperature=temperature,
+ do_sample=True,
+ top_k=top_k,
+ )
+ ).sequences[:, batch['input_ids'].shape[1]:]
+ return output, rng_generator()
+ _sharded_forward_generate = pjit(
+ _forward_generate,
+ in_shardings=(model_ps, PS(), PS()),
+ out_shardings=(PS(), PS()),
+ static_argnums=(3, 4, 5, 6)
+ )
+
+ # Generate an image or first frame (for video)
+ def generate_first_frame(prompts, max_input_length):
+ nonlocal sharded_rng
+ uncond_prompts = [""] * len(prompts)
+ prompts = prompts + uncond_prompts
+ inputs = prefix_tokenizer(
+ prompts,
+ padding='max_length',
+ truncation=True,
+ max_length=max_input_length,
+ return_tensors='np'
+ )
+ batch = dict(
+ input_ids=inputs.input_ids,
+ attention_mask=inputs.attention_mask,
+ vision_masks=np.zeros(inputs.input_ids.shape, dtype=bool),
+ )
+ with mesh:
+ output, sharded_rng = _sharded_forward_generate(
+ params, sharded_rng, batch,
+ tokens_per_frame, FLAGS.cfg_scale_image,
+ FLAGS.top_k_image, FLAGS.temperature_image
+ )
+ output = jax.device_get(output)
+ output = np.split(output, 2, axis=0)[0]
+ output = output.reshape(len(prompts) // 2, tokens_per_frame)
+ image = vqgan.decode(output[:, :-1].reshape(-1, 16, 16))
+ image = ((jax.device_get(image) + 1) * 127.5).astype(np.uint8)
+ return output, image
+
+ sharded_rng = next_rng()
+ prompts = [FLAGS.prompt]
+ entries = []
+ for prompt in prompts:
+ entries.append({
+ 'caption': prompt,
+ 'prompt': f"You are a helpful assistant. USER: Generate an image of {prompt} ASSISTANT: ",
+ })
+
+ B = 1
+ images, image_encodings = [], []
+ for i in tqdm(list(range(0, len(entries), B))):
+ entries_i = entries[i:i + B]
+ prompts = [entry['prompt'] for entry in entries_i]
+ img_enc, img = generate_first_frame(prompts, max_input_length=128)
+ image_encodings.extend(img_enc)
+ images.extend(img)
+
+ if FLAGS.n_frames == 1:
+ image = images[0]
+ Image.fromarray(image).save(FLAGS.output_file)
+ return
+
+ # Generate the rest of the video
+ def generate_video_pred(prompts, images, max_input_length):
+ nonlocal sharded_rng
+ images = np.concatenate([images, images], axis=0)
+ uncond_prompts = [""] * len(prompts)
+ prompts = prompts + uncond_prompts
+ inputs = prefix_tokenizer(
+ prompts,
+ padding='max_length',
+ truncation=True,
+ max_length=max_input_length,
+ return_tensors='np'
+ )
+ batch = dict(
+ input_ids=np.concatenate([inputs.input_ids, images], axis=1),
+ attention_mask=np.concatenate([inputs.attention_mask, np.ones(images.shape, dtype=inputs.attention_mask.dtype)], axis=1),
+ vision_masks=np.concatenate([
+ np.zeros(inputs.input_ids.shape, dtype=bool),
+ np.ones(images.shape, dtype=bool)
+ ], axis=1),
+ )
+ with mesh:
+ output, sharded_rng = _sharded_forward_generate(
+ params, sharded_rng, batch,
+ (FLAGS.n_frames - 1) * tokens_per_frame, FLAGS.cfg_scale_video,
+ FLAGS.top_k_video, FLAGS.temperature_video
+ )
+ output = jax.device_get(output)
+ output = np.split(output, 2, axis=0)[0]
+ output = output.reshape(len(prompts) // 2, FLAGS.n_frames - 1, tokens_per_frame)
+ output = np.concatenate([images[:len(prompts) // 2, None], output], axis=1)
+ output = output[:, :, :-1].reshape(-1, FLAGS.n_frames, 16, 16)
+ vision = []
+ for v in output:
+ v = vqgan.decode(v)
+ v = ((jax.device_get(v) + 1) * 127.5).astype(np.uint8)
+ vision.append(v)
+ return vision
+
+ new_entries = []
+ for img_enc, entry in zip(image_encodings, entries):
+ new_entries.append({
+ 'caption': entry['caption'],
+ 'prompt': f"You are a helpful assistant. USER: Generate a video of {entry['caption']} ASSISTANT: ",
+ 'image': np.array(img_enc, dtype=np.int32),
+ })
+ entries = new_entries
+
+ B = 1
+ videos = []
+ for i in tqdm(list(range(0, len(entries), B))):
+ entries_i = entries[i:i + B]
+ prompts = [entry['prompt'] for entry in entries_i]
+ images = np.array([entry['image'] for entry in entries_i], dtype=np.int32)
+ videos.extend(generate_video_pred(prompts, images, max_input_length=128))
+
+ video = videos[0]
+ writer = imageio.get_writer(FLAGS.output_file, fps=4)
+ for frame in video:
+ writer.append_data(frame)
+ writer.close()
+
+ print('done')
+
+if __name__ == "__main__":
+ run(main)
diff --git a/lwm/vision_llama.py b/lwm/vision_llama.py
new file mode 100644
index 0000000..154bec5
--- /dev/null
+++ b/lwm/vision_llama.py
@@ -0,0 +1,734 @@
+from typing import Any, Dict, List, Optional, Tuple, Union
+import json
+import warnings
+import copy
+
+import jax
+import jax.numpy as jnp
+from jax import lax
+from jax.sharding import PartitionSpec as PS
+import flax.linen as nn
+from flax.core.frozen_dict import unfreeze, freeze
+from flax.traverse_util import flatten_dict, unflatten_dict
+
+from transformers.modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutput
+from transformers.modeling_flax_utils import ACT2FN, FlaxPreTrainedModel
+from transformers.generation.flax_utils import SampleState, FlaxLogitsProcessorList, FlaxSampleOutput, logger
+from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward
+from transformers import GenerationConfig
+
+from tux import load_pickle, open_file
+from lwm.llama import LLaMAConfig, LLAMA_STANDARD_CONFIGS, FlaxLLaMABlockCollection, RMSNorm
+
+
+VIDEO_LLAMA_STANDARD_CONFIGS = LLAMA_STANDARD_CONFIGS
+
+
+class VideoLLaMAConfig(LLaMAConfig):
+ model_type = "video_llama"
+
+ def __init__(self, vision_vocab_size=8448, tie_vision_embeddings=False, sample_mode='all', **kwargs):
+ super().__init__(**kwargs)
+ self.vision_vocab_size = vision_vocab_size # 8192 + 256
+ self.tie_vision_embeddings = tie_vision_embeddings
+ self.sample_mode = sample_mode
+
+ @staticmethod
+ def get_partition_rules(scan_layers=False, scan_axis=0):
+ """ Parition rules for GPTJ. Note that these rules are orderd, so that
+ the beginning rules match first. It is important to use
+ PartitionSpec() instead of None here because JAX does not treat
+ None as a pytree leaf.
+ """
+ if scan_layers:
+ if scan_axis == 0:
+ return (
+ # embeddings
+ ("transformer/wte/embedding", PS("tp", ("fsdp", "sp"))),
+ ("transformer/vte/embedding", PS("tp", ("fsdp", "sp"))),
+ # atention
+ ("attention/(wq|wk|wv)/kernel", PS(None, ("fsdp", "sp"), "tp")),
+ ("attention/wo/kernel", PS(None, "tp", ("fsdp", "sp"))),
+ # mlp
+ ("feed_forward/w1/kernel", PS(None, ("fsdp", "sp"), "tp")),
+ ("feed_forward/w2/kernel", PS(None, "tp", ("fsdp", "sp"))),
+ ("feed_forward/w3/kernel", PS(None, ("fsdp", "sp"), "tp")),
+ # layer norms
+ ("attention_norm/kernel", PS(None, None)),
+ ("ffn_norm/kernel", PS(None, None)),
+ # output head
+ ("transformer/ln_f/kernel", PS(None)),
+ ("lm_head/kernel", PS(("fsdp", "sp"), "tp")),
+ ("vision_head/kernel", PS(("fsdp", "sp"), "tp")),
+ ('.*', PS(None)),
+ )
+ elif scan_axis == 1:
+ return (
+ # embeddings
+ ("transformer/wte/embedding", PS("tp", ("fsdp", "sp"))),
+ ("transformer/vte/embedding", PS("tp", ("fsdp", "sp"))),
+ # atention
+ ("attention/(wq|wk|wv)/kernel", PS(("fsdp", "sp"), None, "tp")),
+ ("attention/wo/kernel", PS("tp", None, ("fsdp", "sp"))),
+ # mlp
+ ("feed_forward/w1/kernel", PS(("fsdp", "sp"), None, "tp")),
+ ("feed_forward/w2/kernel", PS("tp", None, ("fsdp", "sp"))),
+ ("feed_forward/w3/kernel", PS(("fsdp", "sp"), None, "tp")),
+ # layer norms
+ ("attention_norm/kernel", PS(None, None)),
+ ("ffn_norm/kernel", PS(None, None)),
+ # output head
+ ("transformer/ln_f/kernel", PS(None)),
+ ("lm_head/kernel", PS(("fsdp", "sp"), "tp")),
+ ("vision_head/kernel", PS(("fsdp", "sp"), "tp")),
+ ('.*', PS(None)),
+ )
+ else:
+ raise ValueError(f"Invalid scan_axis {scan_axis}")
+ else:
+ return (
+ # embeddings
+ ("transformer/wte/embedding", PS("tp", ("fsdp", "sp"))),
+ ("transformer/vte/embedding", PS("tp", ("fsdp", "sp"))),
+ # atention
+ ("attention/(wq|wk|wv)/kernel", PS(("fsdp", "sp"), "tp")),
+ ("attention/wo/kernel", PS("tp", ("fsdp", "sp"))),
+ # mlp
+ ("feed_forward/w1/kernel", PS(("fsdp", "sp"), "tp")),
+ ("feed_forward/w2/kernel", PS("tp", ("fsdp", "sp"))),
+ ("feed_forward/w3/kernel", PS(("fsdp", "sp"), "tp")),
+ # layer norms
+ ("attention_norm/kernel", PS(None)),
+ ("ffn_norm/kernel", PS(None)),
+ # output head
+ ("transformer/ln_f/kernel", PS(None)),
+ ("lm_head/kernel", PS(("fsdp", "sp"), "tp")),
+ ("vision_head/kernel", PS(("fsdp", "sp"), "tp")),
+ ('.*', PS(None)),
+ )
+
+ @classmethod
+ def load_config(cls, path):
+ if path in VIDEO_LLAMA_STANDARD_CONFIGS:
+ return cls.from_dict(VIDEO_LLAMA_STANDARD_CONFIGS[path])
+ load_type, load_path = path.split('::', 1)
+ if load_type == 'pickle':
+ return cls.from_dict(load_pickle(load_path)['llama_config'])
+ elif load_type == 'json':
+ with open_file(load_path, 'r') as fin:
+ raw_config = fin.read()
+ return cls.from_dict(json.loads(raw_config))
+ else:
+ raise ValueError(f'Unsupported load config type: {load_type}')
+
+
+class FlaxVideoLLaMAPreTrainedModel(FlaxPreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ config_class = VideoLLaMAConfig
+ base_model_prefix = "transformer"
+ module_class: nn.Module = None
+
+ def __init__(
+ self,
+ config: VideoLLaMAConfig,
+ input_shape: Tuple = (4, 1),
+ seed: int = 0,
+ dtype: jnp.dtype = jnp.float32,
+ _do_init: bool = True,
+ **kwargs,
+ ):
+ module = self.module_class(config=config, dtype=dtype, **kwargs)
+ super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)
+
+ def init_cache(self, batch_size, max_length):
+ # init input variables to retrieve cache
+ input_ids = jnp.ones((batch_size, max_length))
+ attention_mask = jnp.ones_like(input_ids)
+ segment_ids = jnp.zeros_like(input_ids)
+ position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)
+ vision_masks = jnp.ones((batch_size, max_length), dtype=bool)
+
+ init_variables = self.module.init(
+ jax.random.PRNGKey(0), input_ids, vision_masks, attention_mask, segment_ids, position_ids, return_dict=False, init_cache=True
+ )
+ return init_variables["cache"]
+
+ def init_weights(self, rng, input_shape, params=None):
+ # init input tensors
+ input_ids = jnp.zeros(input_shape, dtype="i4")
+ attention_mask = jnp.ones_like(input_ids)
+ vision_masks = jnp.ones(input_ids.shape, dtype=bool)
+ segment_ids = jnp.zeros_like(input_ids)
+ position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape)
+ params_rng, dropout_rng = jax.random.split(rng)
+ rngs = {"params": params_rng, "dropout": dropout_rng}
+
+ random_params = self.module.init(rngs, input_ids, vision_masks, attention_mask, segment_ids, position_ids, return_dict=False)["params"]
+
+ if params is not None:
+ random_params = flatten_dict(unfreeze(random_params))
+ params = flatten_dict(unfreeze(params))
+ for missing_key in self._missing_keys:
+ params[missing_key] = random_params[missing_key]
+ self._missing_keys = set()
+ return freeze(unflatten_dict(params))
+ else:
+ return random_params
+
+ @add_start_docstrings_to_model_forward("")
+ def __call__(
+ self,
+ input_ids,
+ vision_masks,
+ attention_mask=None,
+ segment_ids=None,
+ position_ids=None,
+ params: dict = None,
+ past_key_values: dict = None,
+ dropout_rng: jax.random.PRNGKey = None,
+ train: bool = False,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ):
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+ batch_size, sequence_length = input_ids.shape
+
+ if position_ids is None:
+ if past_key_values is not None:
+ raise ValueError("Make sure to provide `position_ids` when passing `past_key_values`.")
+
+ position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
+
+ if attention_mask is None:
+ attention_mask = jnp.ones((batch_size, sequence_length))
+
+ if segment_ids is None:
+ segment_ids = jnp.zeros((batch_size, sequence_length))
+
+ # Handle any PRNG if needed
+ rngs = {}
+ if dropout_rng is not None:
+ rngs["dropout"] = dropout_rng
+
+ inputs = {"params": params or self.params}
+
+ # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be changed by FlaxGPTJAttention module
+ if past_key_values:
+ inputs["cache"] = past_key_values
+ mutable = ["cache"]
+ else:
+ mutable = False
+
+ outputs = self.module.apply(
+ inputs,
+ jnp.array(input_ids, dtype="i4"),
+ jnp.array(vision_masks, dtype="f4"),
+ jnp.array(attention_mask, dtype="i4"),
+ jnp.array(segment_ids, dtype="i4"),
+ jnp.array(position_ids, dtype="i4"),
+ not train,
+ False,
+ output_attentions,
+ output_hidden_states,
+ return_dict,
+ rngs=rngs,
+ mutable=mutable,
+ )
+
+ # add updated cache to model output
+ if past_key_values is not None and return_dict:
+ outputs, past_key_values = outputs
+ outputs["past_key_values"] = unfreeze(past_key_values["cache"])
+ return outputs
+ elif past_key_values is not None and not return_dict:
+ outputs, past_key_values = outputs
+ outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]
+
+ return outputs
+
+
+class FlaxVideoLLaMAModule(nn.Module):
+ config: VideoLLaMAConfig
+ dtype: jnp.dtype = jnp.float32
+ param_dtype: jnp.dtype=jnp.float32
+ precision: Optional[Union[jax.lax.Precision, str]]=None
+
+ def setup(self):
+ self.embed_dim = self.config.hidden_size
+
+ self.vte = nn.Embed(
+ self.config.vision_vocab_size,
+ self.config.hidden_size,
+ embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
+ dtype=self.dtype,
+ param_dtype=self.param_dtype,
+ )
+
+ self.wte = nn.Embed(
+ self.config.vocab_size,
+ self.config.hidden_size,
+ embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
+ dtype=self.dtype,
+ param_dtype=self.param_dtype,
+ )
+ self.dropout = nn.Dropout(rate=self.config.embd_pdrop)
+ self.h = FlaxLLaMABlockCollection(self.config, dtype=self.dtype, param_dtype=self.param_dtype, precision=self.precision)
+ self.ln_f = RMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps, dtype=self.dtype, param_dtype=self.param_dtype)
+
+ def __call__(
+ self,
+ input_ids,
+ vision_masks,
+ attention_mask,
+ segment_ids,
+ position_ids,
+ deterministic=True,
+ init_cache: bool = False,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ ):
+ input_ids = input_ids.astype("i4")
+
+ if input_ids.shape[1] == 1:
+ if self.config.sample_mode == 'text':
+ input_embeds = self.wte(input_ids)
+ elif self.config.sample_mode == 'vision':
+ input_embeds = self.vte(input_ids)
+ elif self.config.sample_mode == 'all':
+ raise NotImplementedError
+ else:
+ raise ValueError(f"Invalid sample_mode: {self.config.sample_mode}")
+ else:
+ input_text_embeds = self.wte(jnp.where(vision_masks, 0, input_ids))
+ input_vision_embeds = self.vte(jnp.where(vision_masks, input_ids, 0))
+ vision_masks = vision_masks[..., None].astype("f4") # 1 is vision, 0 is text
+ input_embeds = input_text_embeds * (1 - vision_masks) + input_vision_embeds * vision_masks
+
+ hidden_states = self.dropout(input_embeds, deterministic=deterministic)
+
+ outputs = self.h(
+ hidden_states,
+ attention_mask,
+ segment_ids,
+ position_ids=position_ids,
+ deterministic=deterministic,
+ init_cache=init_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+ hidden_states = self.ln_f(hidden_states)
+
+ if output_hidden_states:
+ all_hidden_states = outputs[1] + (hidden_states,)
+ outputs = (hidden_states, all_hidden_states) + outputs[2:]
+ else:
+ outputs = (hidden_states,) + outputs[1:]
+
+ if not return_dict:
+ return tuple(v for v in outputs if v is not None)
+
+ return FlaxBaseModelOutput(
+ last_hidden_state=hidden_states,
+ hidden_states=outputs[1],
+ attentions=outputs[-1],
+ )
+
+
+class FlaxVideoLLaMAForCausalLMModule(nn.Module):
+ config: VideoLLaMAConfig
+ dtype: jnp.dtype = jnp.float32
+ param_dtype: jnp.dtype=jnp.float32
+ precision: Optional[Union[jax.lax.Precision, str]]=None
+
+ def setup(self):
+ self.transformer = FlaxVideoLLaMAModule(self.config, dtype=self.dtype)
+ self.vision_head = nn.Dense(
+ self.config.vision_vocab_size,
+ dtype=self.dtype,
+ param_dtype=self.param_dtype,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
+ precision=self.precision,
+ )
+ self.lm_head = nn.Dense(
+ self.config.vocab_size,
+ dtype=self.dtype,
+ param_dtype=self.param_dtype,
+ use_bias=False,
+ kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
+ precision=self.precision,
+ )
+
+ def __call__(
+ self,
+ input_ids,
+ vision_masks,
+ attention_mask=None,
+ segment_ids=None,
+ position_ids=None,
+ deterministic: bool = True,
+ init_cache: bool = False,
+ output_attentions: bool = False,
+ output_hidden_states: bool = False,
+ return_dict: bool = True,
+ ):
+ batch_size, seq_length = input_ids.shape
+ if attention_mask is None:
+ attention_mask = jnp.ones_like(input_ids)
+ if segment_ids is None:
+ segment_ids = jnp.zeros_like(input_ids)
+ if position_ids is None:
+ position_ids = jnp.broadcast_to(
+ jnp.clip(jnp.cumsum(attention_mask, axis=-1) - 1, a_min=0),
+ (batch_size, seq_length)
+ )
+
+
+ outputs = self.transformer(
+ input_ids,
+ vision_masks,
+ attention_mask,
+ segment_ids,
+ position_ids,
+ deterministic=deterministic,
+ init_cache=init_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+
+ if self.config.tie_vision_embeddings:
+ shared_kernel = self.transformer.variables["params"]["vte"]["embedding"].T
+ vision_logits = self.vision_head.apply({"params": {"kernel": shared_kernel}}, hidden_states)
+ else:
+ vision_logits = self.vision_head(hidden_states)
+
+ if self.config.tie_word_embeddings:
+ shared_kernel = self.transformer.variables["params"]["wte"]["embedding"].T
+ lm_logits = self.lm_head.apply({"params": {"kernel": shared_kernel}}, hidden_states)
+ else:
+ lm_logits = self.lm_head(hidden_states)
+
+ if self.config.sample_mode == 'all':
+ if not return_dict:
+ return (vision_logits, lm_logits,) + outputs[1:]
+
+ return FlaxCausalLMOutput(logits=(vision_logits, lm_logits), hidden_states=outputs.hidden_states, attentions=outputs.attentions)
+ elif self.config.sample_mode == 'vision':
+ if not return_dict:
+ return (vision_logits,) + outputs[1:]
+
+ return FlaxCausalLMOutput(logits=vision_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
+ elif self.config.sample_mode == 'text':
+ if not return_dict:
+ return (lm_logits,) + outputs[1:]
+
+ return FlaxCausalLMOutput(logits=lm_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
+ else:
+ raise ValueError(f"Invalid sample_mode: {self.config.sample_mode}")
+
+
+
+@add_start_docstrings("", "")
+class FlaxVideoLLaMAForCausalLM(FlaxVideoLLaMAPreTrainedModel):
+ module_class = FlaxVideoLLaMAForCausalLMModule
+
+ def prepare_inputs_for_generation(
+ self, input_ids, max_length, attention_mask: Optional[jax.Array] = None, vision_masks = None
+ ):
+ # initializing the cache
+ batch_size, seq_length = input_ids.shape
+
+ past_key_values = self.init_cache(batch_size, max_length)
+ # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
+ # But since GPTJ uses a causal mask, those positions are masked anyways.
+ # Thus we can create a single static attention_mask here, which is more efficient for compilation
+ extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
+ if attention_mask is not None:
+ position_ids = attention_mask.cumsum(axis=-1) - 1
+ extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0))
+ else:
+ position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
+
+ return {
+ "past_key_values": past_key_values,
+ "attention_mask": extended_attention_mask,
+ "position_ids": position_ids,
+ "vision_masks": vision_masks
+ }
+
+ def update_inputs_for_generation(self, model_outputs, model_kwargs):
+ return {
+ "past_key_values": model_outputs.past_key_values,
+ "position_ids": model_kwargs["position_ids"][:, -1:] + 1,
+ "attention_mask": model_kwargs["attention_mask"],
+ "vision_masks": model_kwargs["vision_masks"]
+ }
+
+ def _sample_vision(
+ self,
+ input_ids: None,
+ max_length: Optional[int] = None,
+ pad_token_id: Optional[int] = None,
+ eos_token_id: Optional[int] = None,
+ prng_key: Optional[jnp.ndarray] = None,
+ logits_processor: Optional[FlaxLogitsProcessorList] = None,
+ logits_warper: Optional[FlaxLogitsProcessorList] = None,
+ cfg_scales: jnp.ndarray = 1.0,
+ trace: bool = True,
+ params: Optional[Dict[str, jnp.ndarray]] = None,
+ model_kwargs: Optional[Dict[str, jnp.ndarray]] = None,
+ ):
+ # init values
+ max_length = max_length if max_length is not None else self.generation_config.max_length
+ pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
+ eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
+ prng_key = prng_key if prng_key is not None else jax.random.PRNGKey(0)
+
+ batch_size, cur_len = input_ids.shape
+ initial_len = cur_len
+
+ eos_token_id = jnp.array(eos_token_id, dtype=jnp.int32 if eos_token_id is not None else None)
+ pad_token_id = jnp.array(pad_token_id, dtype=jnp.int32)
+ cur_len = jnp.array(cur_len)
+
+ # per batch-item holding current token in loop.
+ sequences = jnp.full((batch_size, max_length), pad_token_id, dtype=jnp.int32)
+ sequences = lax.dynamic_update_slice(sequences, input_ids, (0, 0))
+
+ # per batch-item state bit indicating if sentence has finished.
+ is_sent_finished = jnp.zeros((batch_size,), dtype=jnp.bool_)
+
+ # For Seq2Seq generation, we only need to use the decoder instead of the whole model in generation loop
+ # and pass it the `encoder_outputs`, which are part of the `model_kwargs`.
+ model = self.decode if self.config.is_encoder_decoder else self
+
+ # initialize model specific kwargs
+ model_kwargs = self.prepare_inputs_for_generation(input_ids, max_length, **model_kwargs)
+
+ # initialize state
+ state = SampleState(
+ cur_len=cur_len,
+ sequences=sequences,
+ running_token=input_ids,
+ is_sent_finished=is_sent_finished,
+ prng_key=prng_key,
+ model_kwargs=model_kwargs,
+ )
+
+ def sample_search_cond_fn(state):
+ """state termination condition fn."""
+ has_reached_max_length = state.cur_len == max_length
+ all_sequence_finished = jnp.all(state.is_sent_finished)
+ finish_generation = jnp.logical_or(has_reached_max_length, all_sequence_finished)
+ return ~finish_generation
+
+ def sample_search_body_fn(state):
+ """state update fn."""
+ prng_key, prng_key_next = jax.random.split(state.prng_key)
+ model_outputs = model(state.running_token, params=params, **state.model_kwargs)
+
+ logits = model_outputs.logits[:, -1]
+ cond_logits, uncond_logits = jnp.split(logits, 2, axis=0)
+ logits = uncond_logits + cfg_scales[:, None] * (cond_logits - uncond_logits)
+
+ # apply min_length, ...
+ logits = logits_processor(state.sequences, logits, state.cur_len)
+ # apply top_p, top_k, temperature
+ logits = logits_warper(logits, logits, state.cur_len)
+
+ next_token = jax.random.categorical(prng_key, logits, axis=-1)
+ next_token = jax.lax.cond(
+ (state.cur_len - initial_len + 1) % 257 == 0,
+ lambda: jnp.full_like(next_token, 8192),
+ lambda: next_token
+ )
+ next_token = jnp.concatenate([next_token, next_token], axis=0)
+
+ #next_token = next_token * ~state.is_sent_finished + pad_token_id * state.is_sent_finished
+ next_is_sent_finished = state.is_sent_finished | (next_token == eos_token_id)
+ next_token = next_token[:, None]
+
+ next_sequences = lax.dynamic_update_slice(state.sequences, next_token, (0, state.cur_len))
+ next_model_kwargs = self.update_inputs_for_generation(model_outputs, state.model_kwargs)
+
+ return SampleState(
+ cur_len=state.cur_len + 1,
+ sequences=next_sequences,
+ running_token=next_token,
+ is_sent_finished=next_is_sent_finished,
+ model_kwargs=next_model_kwargs,
+ prng_key=prng_key_next,
+ )
+
+ # The very first prompt often has sequence length > 1, so run outside of `lax.while_loop` to comply with TPU
+ if input_ids.shape[1] > 1:
+ state = sample_search_body_fn(state)
+
+ if not trace:
+ state = self._run_loop_in_debug(sample_search_cond_fn, sample_search_body_fn, state)
+ else:
+ state = lax.while_loop(sample_search_cond_fn, sample_search_body_fn, state)
+
+ return FlaxSampleOutput(sequences=state.sequences)
+
+ def generate_vision(
+ self,
+ input_ids: jnp.ndarray,
+ cfg_scales: jnp.ndarray,
+ generation_config: Optional[GenerationConfig] = None,
+ prng_key: Optional[jnp.ndarray] = None,
+ trace: bool = True,
+ params: Optional[Dict[str, jnp.ndarray]] = None,
+ logits_processor: Optional[FlaxLogitsProcessorList] = None,
+ **kwargs,
+ ):
+ # Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
+ self._validate_model_class()
+
+ # priority: `generation_config` argument > `model.generation_config` (the default generation config)
+ if generation_config is None:
+ # legacy: users may modify the model configuration to control generation. To trigger this legacy behavior,
+ # two conditions must be met
+ # 1) the generation config must have been created from the model config (`_from_model_config` field);
+ # 2) the generation config must have seen no modification since its creation (the hash is the same).
+ if self.generation_config._from_model_config and self.generation_config._original_object_hash == hash(
+ self.generation_config
+ ):
+ new_generation_config = GenerationConfig.from_model_config(self.config)
+ if new_generation_config != self.generation_config:
+ warnings.warn(
+ "You have modified the pretrained model configuration to control generation. This is a"
+ " deprecated strategy to control generation and will be removed soon, in a future version."
+ " Please use and modify the model generation configuration (see"
+ " https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )"
+ )
+ self.generation_config = new_generation_config
+ generation_config = self.generation_config
+
+ generation_config = copy.deepcopy(generation_config)
+ model_kwargs = generation_config.update(**kwargs) # All unused kwargs must be model kwargs
+ generation_config.validate()
+ self._validate_model_kwargs(model_kwargs.copy())
+
+ logits_processor = logits_processor if logits_processor is not None else FlaxLogitsProcessorList()
+
+ # set init values
+ prng_key = prng_key if prng_key is not None else jax.random.PRNGKey(0)
+
+ if generation_config.pad_token_id is None and generation_config.eos_token_id is not None:
+ if model_kwargs.get("attention_mask") is None:
+ logger.warning(
+ "The attention mask and the pad token id were not set. As a consequence, you may observe "
+ "unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
+ )
+ eos_token_id = generation_config.eos_token_id
+ if isinstance(eos_token_id, list):
+ eos_token_id = eos_token_id[0]
+ logger.warning(f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation.")
+ generation_config.pad_token_id = eos_token_id
+
+ if generation_config.decoder_start_token_id is None and self.config.is_encoder_decoder:
+ raise ValueError("`decoder_start_token_id` has to be defined for encoder-decoder generation.")
+
+ # decoder-only models should use left-padding for generation (can't be checked with `trace=True`)
+ if not self.config.is_encoder_decoder and not trace:
+ if (
+ generation_config.pad_token_id is not None
+ and jnp.sum(input_ids[:, -1] == generation_config.pad_token_id) > 0
+ ):
+ logger.warning(
+ "A decoder-only architecture is being used, but right-padding was detected! For correct "
+ "generation results, please set `padding_side='left'` when initializing the tokenizer."
+ )
+
+ batch_size = input_ids.shape[0]
+
+ if self.config.is_encoder_decoder:
+ # add encoder_outputs to model_kwargs
+ if model_kwargs.get("encoder_outputs") is None:
+ model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(input_ids, params, model_kwargs)
+ # prepare decoder_input_ids for generation
+ input_ids = self._prepare_decoder_input_ids_for_generation(
+ batch_size,
+ decoder_start_token_id=generation_config.decoder_start_token_id,
+ bos_token_id=generation_config.bos_token_id,
+ model_kwargs=model_kwargs,
+ )
+
+ # Prepare `max_length` depending on other stopping criteria.
+ input_ids_seq_length = input_ids.shape[-1]
+ has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
+ if has_default_max_length and generation_config.max_new_tokens is None and generation_config.max_length == 20:
+ # 20 is the default max_length of the generation config
+ warnings.warn(
+ f"Using the model-agnostic default `max_length` (={generation_config.max_length}) "
+ "to control the generation length. recommend setting `max_new_tokens` to control the maximum length of the generation.",
+ UserWarning,
+ )
+ elif generation_config.max_new_tokens is not None:
+ if not has_default_max_length and generation_config.max_length is not None:
+ logger.warning(
+ f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
+ f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
+ "Please refer to the documentation for more information. "
+ "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
+ )
+ generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
+
+ if generation_config.min_length is not None and generation_config.min_length > generation_config.max_length:
+ raise ValueError(
+ f"Unfeasable length constraints: the minimum length ({generation_config.min_length}) is larger than"
+ f" the maximum length ({generation_config.max_length})"
+ )
+ if input_ids_seq_length >= generation_config.max_length:
+ input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
+ logger.warning(
+ f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
+ f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
+ " increasing`max_new_tokens`."
+ )
+
+ logits_processor = self._get_logits_processor(
+ generation_config=generation_config,
+ input_ids_seq_length=input_ids_seq_length,
+ logits_processor=logits_processor,
+ )
+
+ if not generation_config.do_sample and generation_config.num_beams == 1:
+ raise NotImplementedError
+ elif generation_config.do_sample and generation_config.num_beams == 1:
+ logits_warper = self._get_logits_warper(generation_config=generation_config)
+ return self._sample_vision(
+ input_ids,
+ generation_config.max_length,
+ generation_config.pad_token_id,
+ generation_config.eos_token_id,
+ prng_key,
+ logits_warper=logits_warper,
+ logits_processor=logits_processor,
+ cfg_scales=cfg_scales,
+ trace=trace,
+ params=params,
+ model_kwargs=model_kwargs,
+ )
+ elif not generation_config.do_sample and generation_config.num_beams > 1:
+ raise NotImplementedError
+ else:
+ raise NotImplementedError("`Beam sampling is currently not implemented.")
diff --git a/lwm/vqgan.py b/lwm/vqgan.py
new file mode 100644
index 0000000..77715f8
--- /dev/null
+++ b/lwm/vqgan.py
@@ -0,0 +1,351 @@
+from typing import Optional
+from functools import cached_property, partial
+import pickle
+import numpy as np
+import jax
+import jax.numpy as jnp
+import flax.linen as nn
+from flax import jax_utils
+from transformers.configuration_utils import PretrainedConfig
+from ml_collections import ConfigDict
+from tux import function_args_to_config, open_file
+
+
+class VQGAN:
+ def __init__(self, vqgan_checkpoint, replicate=False):
+ assert vqgan_checkpoint != ''
+ self.replicate = replicate
+ self.config = VQGANConfig.get_default_config()
+ self.params = pickle.load(open_file(vqgan_checkpoint, 'rb'))
+ if replicate:
+ self.params = jax_utils.replicate(self.params)
+ else:
+ self.params = jax.jit(lambda x: x)(self.params)
+ self.model = VQGANModel(self.config)
+
+ def _wrap_fn(self, fn):
+ if self.replicate:
+ return jax.pmap(fn, devices=jax.local_devices())
+ else:
+ return jax.jit(fn)
+
+ @cached_property
+ def _encode(self):
+ def fn(pixel_values, params):
+ return self.model.apply(
+ {'params': params},
+ pixel_values,
+ method=self.model.encode
+ )
+ return partial(self._wrap_fn(fn), params=self.params)
+
+ @cached_property
+ def _decode(self):
+ def fn(encoding, params):
+ return self.model.apply(
+ {'params': params},
+ encoding,
+ method=self.model.decode
+ )
+ return partial(self._wrap_fn(fn), params=self.params)
+
+ def encode(self, pixel_values):
+ return self._encode(pixel_values)
+
+ def decode(self, encoding):
+ return self._decode(encoding)
+
+
+class VQGANConfig(PretrainedConfig):
+ model_type = "vqgan"
+
+ def __init__(
+ self,
+ resolution=256,
+ num_channels=3,
+ hidden_channels=128,
+ channel_mult=(1, 2, 2, 4, 6),
+ num_res_blocks=2,
+ attn_resolutions=(),
+ no_attn_mid_block=True,
+ z_channels=64,
+ num_embeddings=8192,
+ quantized_embed_dim=64,
+ dropout=0.0,
+ resample_with_conv=True,
+ commitment_cost=0.25
+ ):
+ self.resolution = resolution
+ self.num_channels = num_channels
+ self.hidden_channels = hidden_channels
+ self.channel_mult = channel_mult
+ self.num_res_blocks = num_res_blocks
+ self.attn_resolutions = attn_resolutions
+ self.no_attn_mid_block = no_attn_mid_block
+ self.z_channels = z_channels
+ self.num_embeddings = num_embeddings
+ self.quantized_embed_dim = quantized_embed_dim
+ self.dropout = dropout
+ self.resample_with_conv = resample_with_conv
+ self.commitment_cost = commitment_cost
+
+ @classmethod
+ def get_default_config(cls, updates=None):
+ config = function_args_to_config(cls.__init__)
+ if updates is not None:
+ config.update(ConfigDict(updates).copy_and_resolve_references())
+ config.num_resolutions = len(config.channel_mult)
+ return config
+
+ @classmethod
+ def load_config(cls, path):
+ return cls.get_default_config(cls)
+
+
+class VQGANModel(nn.Module):
+ config: VQGANConfig
+
+ def setup(self):
+ self.encoder = Encoder(self.config)
+ self.decoder = Decoder(self.config)
+ self.quantize = VectorQuantizer(
+ self.config.num_embeddings, self.config.quantized_embed_dim
+ )
+ self.quant_conv = nn.Conv(self.config.quantized_embed_dim, [1, 1])
+ self.post_quant_conv = nn.Conv(self.config.z_channels, [1, 1])
+
+ def encode(self, pixel_values):
+ T = None
+ if len(pixel_values.shape) == 5: # video
+ T = pixel_values.shape[1]
+ pixel_values = pixel_values.reshape(-1, *pixel_values.shape[2:])
+ hidden_states = self.encoder(pixel_values)
+ hidden_states = self.quant_conv(hidden_states)
+ quantized_states, codebook_indices = self.quantize(hidden_states)
+ if T is not None:
+ quantized_states = quantized_states.reshape(-1, T, *quantized_states.shape[1:])
+ codebook_indices = codebook_indices.reshape(-1, T, *codebook_indices.shape[1:])
+ return quantized_states, codebook_indices
+
+ def decode(self, encoding, is_codebook_indices=True):
+ if is_codebook_indices:
+ encoding = self.quantize(None, encoding)
+ T = None
+ if len(encoding.shape) == 5:
+ T = encoding.shape[1]
+ encoding = encoding.reshape(-1, *encoding.shape[2:])
+ hidden_states = self.post_quant_conv(encoding)
+ reconstructed_pixel_values = self.decoder(hidden_states)
+ if T is not None:
+ reconstructed_pixel_values = reconstructed_pixel_values.reshape(-1, T, *reconstructed_pixel_values.shape[1:])
+ return jnp.clip(reconstructed_pixel_values, -1, 1)
+
+ def __call__(self, pixel_values):
+ encoding = self.encode(pixel_values)[1]
+ recon = self.decode(encoding)
+ return recon
+
+
+class Encoder(nn.Module):
+ config: VQGANConfig
+
+ @nn.compact
+ def __call__(self, pixel_values):
+ assert pixel_values.shape[1] == pixel_values.shape[2] == self.config.resolution, pixel_values.shape
+ hidden_states = nn.Conv(self.config.hidden_channels, [3, 3])(pixel_values)
+ for i_level in range(self.config.num_resolutions):
+ hidden_states = DownsamplingBlock(self.config, i_level)(hidden_states)
+ hidden_states = MidBlock(
+ self.config, self.config.no_attn_mid_block, self.config.dropout
+ )(hidden_states)
+ hidden_states = nn.GroupNorm()(hidden_states)
+ hidden_states = nn.silu(hidden_states)
+ hidden_states = nn.Conv(self.config.z_channels, [3, 3])(hidden_states)
+ return hidden_states
+
+
+class Decoder(nn.Module):
+ config: VQGANConfig
+
+ @nn.compact
+ def __call__(self, hidden_states):
+ hidden_states = nn.Conv(
+ self.config.hidden_channels * self.config.channel_mult[self.config.num_resolutions - 1],
+ [3, 3]
+ )(hidden_states)
+ hidden_states = MidBlock(
+ self.config, self.config.no_attn_mid_block, self.config.dropout
+ )(hidden_states)
+ for i_level in reversed(range(self.config.num_resolutions)):
+ hidden_states = UpsamplingBlock(self.config, i_level)(hidden_states)
+ hidden_states = nn.GroupNorm()(hidden_states)
+ hidden_states = nn.silu(hidden_states)
+ hidden_states = nn.Conv(self.config.num_channels, [3, 3])(hidden_states)
+ return hidden_states
+
+
+class VectorQuantizer(nn.Module):
+ n_e: int
+ e_dim: int
+
+ @nn.compact
+ def __call__(self, z, encoding_indices=None):
+ def quantize(encoding_indices):
+ w = jax.device_put(embeddings)
+ return w[(encoding_indices,)]
+ embeddings = self.param(
+ 'embeddings',
+ lambda rng, shape, dtype: jax.random.uniform(
+ rng, shape, dtype, minval=-1.0 / self.n_e, maxval=1.0 / self.n_e
+ ),
+ [self.n_e, self.e_dim], jnp.float32
+ )
+
+ if encoding_indices is not None:
+ return quantize(encoding_indices)
+
+ z_flattened = z.reshape(-1, z.shape[-1])
+ d = jnp.sum(z_flattened ** 2, axis=1, keepdims=True) + \
+ jnp.sum(embeddings.T ** 2, axis=0, keepdims=True) - \
+ 2 * jnp.einsum('bd,nd->bn', z_flattened, embeddings)
+
+ min_encoding_indices = jnp.argmin(d, axis=1)
+ z_q = quantize(min_encoding_indices)
+ z_q = jnp.reshape(z_q, z.shape)
+ z_q = z + jax.lax.stop_gradient(z_q - z)
+
+ encodings_one_hot = jax.nn.one_hot(min_encoding_indices, num_classes=self.n_e)
+ assert len(encodings_one_hot.shape) == 2
+ min_encoding_indices = jnp.reshape(min_encoding_indices, z.shape[:-1])
+
+ return z_q, min_encoding_indices
+
+
+class DownsamplingBlock(nn.Module):
+ config: VQGANConfig
+ block_idx: int
+
+ @nn.compact
+ def __call__(self, hidden_states):
+ block_out = self.config.hidden_channels * self.config.channel_mult[self.block_idx]
+ for _ in range(self.config.num_res_blocks):
+ hidden_states = ResnetBlock(
+ block_out, dropout_prob=self.config.dropout
+ )(hidden_states)
+ if hidden_states.shape[1] in self.config.attn_resolutions:
+ hidden_states = AttnBlock()(hidden_states)
+ if self.block_idx != self.config.num_resolutions - 1:
+ hidden_states = Downsample(self.config.resample_with_conv)(hidden_states)
+ return hidden_states
+
+
+class ResnetBlock(nn.Module):
+ out_channels: Optional[int] = None
+ use_conv_shortcut: bool = False
+ dropout_prob: float = 0.0
+
+ @nn.compact
+ def __call__(self, hidden_states):
+ out_channels = self.out_channels or hidden_states.shape[-1]
+ residual = hidden_states
+ hidden_states = nn.GroupNorm()(hidden_states)
+ hidden_states = nn.silu(hidden_states)
+ hidden_states = nn.Conv(out_channels, [3, 3])(hidden_states)
+ hidden_states = nn.GroupNorm()(hidden_states)
+ hidden_states = nn.silu(hidden_states)
+ hidden_states = nn.Dropout(self.dropout_prob, deterministic=True)(hidden_states)
+ hidden_states = nn.Conv(out_channels, [3, 3])(hidden_states)
+ if out_channels != residual.shape[-1]:
+ if self.use_conv_shortcut:
+ residual = nn.Conv(out_channels, [3, 3])(residual)
+ else:
+ residual = nn.Conv(out_channels, [1, 1])(residual)
+ return hidden_states + residual
+
+
+class AttnBlock(nn.Module):
+ @nn.compact
+ def __call__(self, hidden_states):
+ residual = hidden_states
+ hidden_states = nn.GroupNorm()(hidden_states)
+ query = nn.Conv(hidden_states.shape[-1], [1, 1])(hidden_states)
+ key = nn.Conv(hidden_states.shape[-1], [1, 1])(hidden_states)
+ value = nn.Conv(hidden_states.shape[-1], [1, 1])(hidden_states)
+ query, key, value = map(
+ lambda x: x.reshape(x.shape[0], -1, x.shape[-1]),
+ [query, key, value]
+ )
+ attn_weights = jnp.einsum("bqd,bkd->bqk", query, key)
+ attn_weights *= hidden_states.shape[-1] ** -0.5
+ attn_weights = jax.nn.softmax(attn_weights, axis=-1)
+ hidden_states = jnp.einsum("bqk,bkd->bqd", attn_weights, value)
+ hidden_states = nn.Conv(hidden_states.shape[-1], [1, 1])(hidden_states)
+ return hidden_states + residual
+
+
+class Downsample(nn.Module):
+ with_conv: bool
+
+ @nn.compact
+ def __call__(self, hidden_states):
+ if self.with_conv:
+ hidden_states = jnp.pad(
+ hidden_states,
+ [(0, 0), (0, 1), (0, 1), (0, 0)]
+ )
+ hidden_states = nn.Conv(
+ hidden_states.shape[-1], [3, 3],
+ strides=[2, 2],
+ padding="VALID"
+ )(hidden_states)
+ else:
+ hidden_states = nn.avg_pool(hidden_states, [2, 2], [2, 2])
+ return hidden_states
+
+
+class Upsample(nn.Module):
+ with_conv: bool
+
+ @nn.compact
+ def __call__(self, hidden_states):
+ B, H, W, C = hidden_states.shape
+ hidden_states = jax.image.resize(
+ hidden_states,
+ (B, H * 2, W * 2, C),
+ method="nearest"
+ )
+ if self.with_conv:
+ hidden_states = nn.Conv(hidden_states.shape[-1], [3, 3])(hidden_states)
+ return hidden_states
+
+
+class UpsamplingBlock(nn.Module):
+ config: VQGANConfig
+ block_idx: int
+
+ @nn.compact
+ def __call__(self, hidden_states):
+ block_out = self.config.hidden_channels * self.config.channel_mult[self.block_idx]
+ for _ in range(self.config.num_res_blocks + 1):
+ hidden_states = ResnetBlock(
+ block_out, dropout_prob=self.config.dropout
+ )(hidden_states)
+ if hidden_states.shape[1] in self.config.attn_resolutions:
+ hidden_states = AttnBlock()(hidden_states)
+ if self.block_idx != 0:
+ hidden_states = Upsample(self.config.resample_with_conv)(hidden_states)
+ return hidden_states
+
+
+class MidBlock(nn.Module):
+ config: VQGANConfig
+ no_attn: bool
+ dropout: float
+
+ @nn.compact
+ def __call__(self, hidden_states):
+ hidden_states = ResnetBlock(dropout_prob=self.dropout)(hidden_states)
+ if not self.no_attn:
+ hidden_states = AttnBlock()(hidden_states)
+ hidden_states = ResnetBlock(dropout_prob=self.dropout)(hidden_states)
+ return hidden_states
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..f6648b0
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,24 @@
+tensorflow==2.11.0
+tensorboard-plugin-profile
+flax==0.7.0
+optax==0.1.7
+chex==0.1.82
+einops
+--extra-index-url https://download.pytorch.org/whl/cpu
+torch==2.0.0
+torchvision==0.15.0
+transformers==4.29.2
+datasets==2.13.0
+tqdm
+ml_collections
+wandb
+gcsfs
+requests
+typing-extensions
+sentencepiece
+tux @ git+https://github.com/lhao499/tux.git
+Pillow
+ipdb
+imageio[ffmpeg]
+decord
+tiktoken
\ No newline at end of file
diff --git a/scripts/eval_needle.py b/scripts/eval_needle.py
new file mode 100644
index 0000000..798c7eb
--- /dev/null
+++ b/scripts/eval_needle.py
@@ -0,0 +1,447 @@
+from absl.app import run
+import time
+import json
+import math
+import os
+from tqdm import tqdm
+import random
+from functools import cached_property
+import numpy as np
+import jax
+from jax.experimental.pjit import pjit
+from jax.sharding import PartitionSpec as PS
+import gcsfs
+import tiktoken
+from transformers import GenerationConfig
+from tux import (
+ define_flags_with_default, StreamingCheckpointer, JaxDistributedConfig,
+ set_random_seed, get_float_dtype_by_name, JaxRNG, next_rng,
+ match_partition_rules, make_shard_and_gather_fns,
+ with_sharding_constraint, tree_apply, open_file
+)
+from lwm.llama import LLaMAConfig, FlaxLLaMAForCausalLM
+
+
+FLAGS, FLAGS_DEF = define_flags_with_default(
+ haystack_file="",
+ max_tokens_per_batch=2000000,
+ output_file="results.json",
+ context_lengths_min=1000,
+ context_lengths_max=32000,
+ n_context_length_intervals=3,
+ n_document_depth_intervals=3,
+ n_rounds=2,
+ seed=1234,
+ mesh_dim='1,-1,1,1',
+ dtype='fp32',
+ load_llama_config='',
+ update_llama_config='',
+ load_checkpoint='',
+ tokenizer=LLaMAConfig.get_tokenizer_config(),
+ checkpointer=StreamingCheckpointer.get_default_config(),
+ llama=LLaMAConfig.get_default_config(),
+ jax_distributed=JaxDistributedConfig.get_default_config(),
+)
+
+
+class LLMNeedleHaystackTester:
+ OURS_TEMPLATE = "You are a helpful assistant. USER: {context} {question} Don't give information outside the document or repeat your findings. Keep your response short and direct. ASSISTANT: "
+ RANDOM_NEEDLE_CITIES = [
+ 'Chicago', 'Yangon', 'Antananarivo', 'Colombo', 'Almaty', 'Sydney', 'Chicago', 'Mexico City',
+ 'Seattle', 'Lagos', 'Amsterdam', 'Belgrade', 'Cairo', 'Baghdad', 'Damascus', 'Kigali', 'Dakar',
+ 'Dakar', 'Sofia', 'Kigali', 'Victoria', 'Tashkent', 'Mumbai', 'Barcelona', 'Almaty', 'Amman',
+ 'Toronto', 'Bratislava', 'Johannesburg', 'Thimphu', 'Bangkok', 'Santiago', 'Cairo', 'San Francisco',
+ 'Lagos', 'Amsterdam', 'Paris', 'Rabat', 'Santiago', 'Copenhagen', 'Madrid', 'Kigali',
+ 'Ho Chi Minh City', 'Sarajevo', 'Delhi', 'Istanbul', 'Ho Chi Minh City', 'Khartoum', 'Helsinki',
+ 'Doha', 'Istanbul', 'Kuala Lumpur', 'Budapest', 'Shanghai', 'Moscow', 'Los Angeles', 'Oslo',
+ 'Johannesburg', 'Berlin', 'Bangalore', 'Tokyo', 'Melbourne', 'Barcelona', 'Chicago', 'Port Louis',
+ 'Lisbon', 'Nairobi', 'Kampala', 'Lima', 'Maputo', 'Vancouver', 'Dubai', 'Khartoum', 'Jakarta',
+ 'Madrid', 'Yerevan', 'Beirut', 'Athens', 'Chicago', 'Paris', 'Bucharest', 'Copenhagen', 'Brussels',
+ 'Damascus', 'Seattle', 'Los Angeles', 'Yerevan', 'Victoria', 'Tunis', 'Astana', 'Seoul',
+ 'Buenos Aires', 'Bangkok', 'Colombo', 'Brussels', 'Khartoum', 'Doha', 'San Francisco', 'Vienna', 'Jakarta'
+ ]
+
+ def __init__(self,
+ needle="",
+ haystack_file="",
+ retrieval_question="What is the special magic {} number?",
+ results_version = 1,
+ rnd_number_digits = 7,
+ context_lengths_min = 1000,
+ context_lengths_max = 126000,
+ context_lengths_num_intervals = 10,
+ document_depth_percent_min = 0,
+ document_depth_percent_max = 100,
+ document_depth_percent_intervals = 10,
+ document_depth_percent_interval_type = "linear",
+ save_results = False,
+ final_context_length_buffer = 200,
+ print_ongoing_status = True):
+ needle="\nThe special magic {city} number is: {rnd_number}\n"
+ self.needle = needle
+ if not needle or not haystack_file or not retrieval_question:
+ raise ValueError("Needle, haystack, and retrieval_question must be provided.")
+
+ self.rnd_number_digits = rnd_number_digits
+ self.context_lengths_num_intervals = context_lengths_num_intervals
+ self.document_depth_percent_intervals = document_depth_percent_intervals
+ self.haystack_file = haystack_file
+ self.retrieval_question = retrieval_question
+ self.results_version = results_version
+ self.save_results = save_results
+ self.final_context_length_buffer = final_context_length_buffer
+ self.print_ongoing_status = print_ongoing_status
+ self.testing_results = []
+
+ self.context_lengths = np.round(np.linspace(context_lengths_min, context_lengths_max, num=context_lengths_num_intervals, endpoint=True)).astype(int)
+ if document_depth_percent_interval_type == 'linear':
+ self.document_depth_percents = np.round(np.linspace(document_depth_percent_min, document_depth_percent_max, num=document_depth_percent_intervals, endpoint=True)).astype(int)
+ elif document_depth_percent_interval_type == 'sigmoid':
+ self.document_depth_percents = [self.logistic(x) for x in np.linspace(document_depth_percent_min, document_depth_percent_max, document_depth_percent_intervals)]
+ else:
+ raise ValueError(f"Unsupported document_depth_percent_interval_type: {document_depth_percent_interval_type}")
+
+ self.model = Sampler()
+
+ self.enc = LLaMAConfig.get_tokenizer(FLAGS.tokenizer)
+ self.enc_tiktoken = tiktoken.encoding_for_model("gpt-4-1106-preview")
+
+ def generate_random_number(self, num_digits):
+ lower_bound = 10**(num_digits - 1)
+ upper_bound = 10**num_digits - 1
+ return random.randint(lower_bound, upper_bound)
+
+ def logistic(self, x, L=100, x0=50, k=.1):
+ if x == 0:
+ return 0
+ if x == 100:
+ return 100
+ return np.round(L / (1 + np.exp(-k * (x - x0))), 3)
+
+ def read_context_files(self, n):
+ max_context_length = max(self.context_lengths)
+ contexts = []
+ f = open_file(self.haystack_file, 'r')
+ for _ in range(n):
+ context = ""
+ toks = 0
+ while toks < max_context_length:
+ text = json.loads(f.readline())['text']
+ context += text
+ toks += len(self.enc.encode(text))
+ contexts.append(context)
+ return contexts
+
+ def encode_and_trim(self, context, context_length):
+ tokens = self.enc.encode(context)
+ if len(tokens) > context_length:
+ context = self.enc.decode(tokens[:context_length])
+ return context
+
+ def create_contexts(self, needle_rnd_number, insert_needle, random_city, trim_context, context_length, depth_percent, seed):
+ if self.save_results:
+ if self.result_exists(context_length, depth_percent):
+ return
+ needle = self.needle.format(city=random_city, rnd_number=needle_rnd_number)
+ question = self.retrieval_question.format(random_city)
+ if not insert_needle:
+ needle = " " #replace needle with a space
+ context = self.generate_context(needle, trim_context, context_length, depth_percent)
+ results = {
+ 'context' : context,
+ 'context_length' : int(context_length),
+ 'depth_percent' : float(depth_percent),
+ 'needle' : needle,
+ 'question' : question,
+ 'insert_needle' : insert_needle,
+ 'needle_rnd_number' : needle_rnd_number,
+ 'seed': seed,
+ }
+ return results
+
+ def insert_needle(self, needle, context, depth_percent, context_length):
+ tokens_needle = self.enc_tiktoken.encode(needle)
+ tokens_context = self.enc_tiktoken.encode(context)
+
+ # Reducing the context length by 150 buffer. This is to account for system message, the user question, and response.
+ context_length -= self.final_context_length_buffer
+
+ # If your context + needle are longer than the context length (which it will be), then reduce tokens from the context by the needle length
+ if len(tokens_context) + len(tokens_needle) > context_length:
+ tokens_context = tokens_context[:context_length - len(tokens_needle)]
+
+ if depth_percent == 100:
+ # If your depth percent is 100 (which means your needle is the last thing in the doc), throw it at the end
+ tokens_new_context = tokens_context + tokens_needle
+ else:
+ # Go get the position (in terms of tokens) to insert your needle
+ insertion_point = int(len(tokens_context) * (depth_percent / 100))
+
+ # tokens_new_context represents the tokens before the needle
+ tokens_new_context = tokens_context[:insertion_point]
+
+ # We want to make sure that we place our needle at a sentence break so we first see what token a '.' is
+ period_tokens = self.enc_tiktoken.encode('.')
+
+ # Then we iteration backwards until we find the first period
+ while tokens_new_context and tokens_new_context[-1] not in period_tokens:
+ insertion_point -= 1
+ tokens_new_context = tokens_context[:insertion_point]
+
+ # Once we get there, then add in your needle, and stick the rest of your context in on the other end.
+ # Now we have a needle in a haystack
+ tokens_new_context += tokens_needle + tokens_context[insertion_point:]
+
+ # Convert back to a string and return it
+ new_context = self.enc_tiktoken.decode(tokens_new_context)
+ return new_context
+
+ def generate_context(self, needle, trim_context, context_length, depth_percent):
+ context = self.insert_needle(needle, trim_context, depth_percent, context_length)
+ return context
+
+ def compute_max_input_length(self, context_length, buffer=1024):
+ block_size = self.model.block_size
+ context_length += buffer
+ context_length = math.ceil(context_length / block_size) * block_size
+ return int(context_length)
+
+ def run_test(self):
+ fs = gcsfs.GCSFileSystem()
+ contexts = []
+ template = self.OURS_TEMPLATE
+
+ def _key_from_result(result):
+ return (result['context_length'], result['depth_percent'], result['seed'])
+
+ results = []
+ completed = set()
+ def exists(fname):
+ if fname.startswith('gs://'):
+ return fs.exists(fname)
+ else:
+ return os.path.exists(fname)
+ if exists(FLAGS.output_file):
+ with open_file(FLAGS.output_file, 'r') as f:
+ results = json.load(f)
+ completed = set([_key_from_result(result) for result in results])
+ print('completed', len(completed))
+
+ full_contexts = self.read_context_files(FLAGS.n_rounds)
+ full_tokens = [self.enc.encode(full_context) for full_context in tqdm(full_contexts)]
+
+ start = time.time()
+ for context_length in self.context_lengths:
+ trim_contexts = [self.enc.decode(full_token[:context_length]) for full_token in tqdm(full_tokens)]
+ max_input_length = self.compute_max_input_length(context_length)
+ contexts = []
+ for depth_percent in self.document_depth_percents:
+ for i in range(FLAGS.n_rounds):
+ if (int(context_length), float(depth_percent), i) in completed:
+ continue
+ random_city = random.choice(LLMNeedleHaystackTester.RANDOM_NEEDLE_CITIES)
+ insert_needle = True
+ needle_rnd_number = str(self.generate_random_number(self.rnd_number_digits))
+ print("context length: " + str(context_length))
+ print("depth_percent : " + str(depth_percent))
+ context = self.create_contexts(needle_rnd_number, insert_needle, random_city, trim_contexts[i], context_length, depth_percent, i)
+ contexts.append(context)
+
+ if len(contexts) == 0:
+ continue
+
+ B = FLAGS.max_tokens_per_batch / (max_input_length + self.model.block_size)
+ B = int(B / self.model.data_dim) * self.model.data_dim
+ if B < self.model.data_dim:
+ B = self.model.data_dim
+ elif B > len(contexts):
+ B = int(math.ceil(len(contexts) / self.model.data_dim) * self.model.data_dim)
+ if len(contexts) % B == 0:
+ n_pad = 0
+ else:
+ n_pad = B - len(contexts) % B
+ for _ in range(n_pad):
+ contexts.insert(0, contexts[0])
+
+ pbar = tqdm(total=len(contexts))
+ for i in range(0, len(contexts), B):
+ contexts_i = contexts[i:i + B]
+ prompts = [
+ template.format(context=context['context'], question=context['question'])
+ for context in contexts_i
+ ]
+ outs = self.model(prompts, max_input_length)
+ for j, (context, out) in enumerate(zip(contexts_i, outs)):
+ if i + j < n_pad:
+ continue
+ results.append({
+ 'context_length': context['context_length'],
+ 'depth_percent': context['depth_percent'],
+ 'response': out,
+ 'answer': context['needle_rnd_number'],
+ 'correct': context['needle_rnd_number'] in out,
+ 'seed': context['seed'],
+ })
+ print(results[-1])
+ if jax.process_index() == 0:
+ with open_file(FLAGS.output_file, 'w') as f:
+ json.dump(results, f)
+ pbar.update(len(contexts_i))
+ pbar.close()
+ print('elapsed', time.time() - start)
+ print('done')
+
+
+ def print_start_test_summary(self):
+ print ("\n")
+ print ("Starting Needle In A Haystack Testing...")
+ print (f"- Context Lengths: {len(self.context_lengths)}, Min: {min(self.context_lengths)}, Max: {max(self.context_lengths)}")
+ print (f"- Document Depths: {len(self.document_depth_percents)}, Min: {min(self.document_depth_percents)}%, Max: {max(self.document_depth_percents)}%")
+ print (f"- Needle: {self.needle.strip()}")
+ print ("\n\n")
+
+ def start_test(self):
+ if self.print_ongoing_status:
+ self.print_start_test_summary()
+ self.run_test()
+
+
+
+class Sampler:
+ def __init__(self):
+ self.mesh = LLaMAConfig.get_jax_mesh(FLAGS.mesh_dim)
+ self.prefix_tokenizer = LLaMAConfig.get_tokenizer(
+ FLAGS.tokenizer, truncation_side='left', padding_side='left'
+ )
+ self.tokenizer = LLaMAConfig.get_tokenizer(FLAGS.tokenizer)
+ self.sharded_rng = next_rng()
+ self._load_model()
+
+ @property
+ def block_size(self):
+ # return 2 * max(self.config.scan_query_chunk_size, self.config.scan_key_chunk_size)
+ return max(self.config.scan_query_chunk_size, self.config.scan_key_chunk_size) * self.mesh.shape['sp']
+
+ @property
+ def data_dim(self):
+ return self.mesh.shape['dp'] * self.mesh.shape['fsdp']
+
+ def _load_model(self):
+ if FLAGS.load_llama_config != '':
+ llama_config = LLaMAConfig.load_config(FLAGS.load_llama_config)
+ updates = LLaMAConfig(**FLAGS.llama)
+ llama_config.update(dict(
+ remat_block=updates.remat_block,
+ remat_attention=updates.remat_attention,
+ remat_mlp=updates.remat_mlp,
+ scan_attention=updates.scan_attention,
+ scan_mlp=updates.scan_mlp,
+ scan_query_chunk_size=updates.scan_query_chunk_size,
+ scan_key_chunk_size=updates.scan_key_chunk_size,
+ scan_mlp_chunk_size=updates.scan_mlp_chunk_size,
+ scan_layers=updates.scan_layers,
+ param_scan_axis=updates.param_scan_axis,
+ ))
+ else:
+ llama_config = LLaMAConfig(**FLAGS.llama)
+
+ if FLAGS.update_llama_config != '':
+ llama_config.update(dict(eval(FLAGS.update_llama_config)))
+
+ llama_config.update(dict(
+ bos_token_id=self.tokenizer.bos_token_id,
+ eos_token_id=self.tokenizer.eos_token_id,
+ ))
+ llama_config.update(dict(mesh_dim=FLAGS.mesh_dim))
+ self.config = llama_config
+ assert not self.config.use_flash_attention, f"Flash attention is not supported for inference"
+
+ with jax.default_device(jax.devices("cpu")[0]):
+ _, self.params = StreamingCheckpointer.load_trainstate_checkpoint(
+ FLAGS.load_checkpoint, disallow_trainstate=True, max_buffer_size=32 * 2 ** 30
+ )
+ self.model = FlaxLLaMAForCausalLM(
+ llama_config,
+ input_shape=(512, self.block_size),
+ seed=FLAGS.seed,
+ _do_init=False,
+ dtype=get_float_dtype_by_name(FLAGS.dtype),
+ )
+ self.model_ps = match_partition_rules(
+ LLaMAConfig.get_partition_rules(llama_config.scan_layers, llama_config.param_scan_axis), self.params
+ )
+ shard_fns, _ = make_shard_and_gather_fns(
+ self.model_ps, get_float_dtype_by_name(FLAGS.dtype)
+ )
+
+ with self.mesh:
+ self.params = tree_apply(shard_fns, self.params)
+
+ @cached_property
+ def _forward_generate(self):
+ def fn(params, rng, batch):
+ batch = with_sharding_constraint(batch, PS(('dp', 'fsdp'), 'sp'))
+ rng_generator = JaxRNG(rng)
+ output = self.model.generate(
+ batch['input_ids'],
+ attention_mask=batch['attention_mask'],
+ params=params['params'],
+ prng_key=rng_generator(),
+ generation_config=GenerationConfig(
+ max_new_tokens=self.block_size,
+ pad_token_id=self.tokenizer.pad_token_id,
+ eos_token_id=self.tokenizer.eos_token_id,
+ temperature=0.,
+ do_sample=False,
+ num_beams=1,
+ top_k=50,
+ top_p=1.0,
+ )
+ ).sequences[:, batch['input_ids'].shape[1]:]
+ return output, rng_generator()
+ return pjit(
+ fn,
+ in_shardings=(self.model_ps, PS(), PS()),
+ out_shardings=(PS(), PS())
+ )
+
+ def __call__(self, prompts, max_input_length):
+ inputs = self.prefix_tokenizer(
+ prompts,
+ padding='max_length',
+ truncation=True,
+ max_length=max_input_length,
+ return_tensors='np'
+ )
+ batch = dict(
+ input_ids=inputs.input_ids,
+ attention_mask=inputs.attention_mask
+ )
+ with self.mesh:
+ output, self.sharded_rng = self._forward_generate(
+ self.params, self.sharded_rng, batch
+ )
+ output = jax.device_get(output)
+ output_text = []
+ for text in list(self.tokenizer.batch_decode(output, skip_special_tokens=True)):
+ if self.tokenizer.eos_token in text:
+ text = text.split(self.tokenizer.eos_token, maxsplit=1)[0]
+ output_text.append(text)
+ return output_text
+
+
+def main(argv):
+ JaxDistributedConfig.initialize(FLAGS.jax_distributed)
+ set_random_seed(FLAGS.seed)
+
+ ht = LLMNeedleHaystackTester(
+ haystack_file=FLAGS.haystack_file,
+ context_lengths_min=FLAGS.context_lengths_min,
+ context_lengths_max=FLAGS.context_lengths_max,
+ context_lengths_num_intervals=FLAGS.n_context_length_intervals,
+ document_depth_percent_intervals=FLAGS.n_document_depth_intervals,
+ )
+ ht.start_test()
+
+if __name__ == "__main__":
+ run(main)
diff --git a/scripts/eval_needle_multi.py b/scripts/eval_needle_multi.py
new file mode 100644
index 0000000..7145def
--- /dev/null
+++ b/scripts/eval_needle_multi.py
@@ -0,0 +1,455 @@
+from absl.app import run
+import glob
+import time
+import json
+import math
+import os
+from tqdm import tqdm
+import random
+from functools import cached_property
+import numpy as np
+import jax
+from jax.experimental.pjit import pjit
+from jax.sharding import PartitionSpec as PS
+import gcsfs
+import tiktoken
+from transformers import GenerationConfig, LlamaTokenizer
+from tux import (
+ define_flags_with_default, StreamingCheckpointer, JaxDistributedConfig,
+ set_random_seed, get_float_dtype_by_name, JaxRNG, next_rng,
+ match_partition_rules, make_shard_and_gather_fns,
+ with_sharding_constraint, tree_apply, open_file
+)
+from lwm.llama import LLaMAConfig, FlaxLLaMAForCausalLM
+
+
+FLAGS, FLAGS_DEF = define_flags_with_default(
+ haystack_file="",
+ max_tokens_per_batch=2000000,
+ output_file="results.json",
+ context_lengths_min=1000,
+ context_lengths_max=32000,
+ n_context_length_intervals=3,
+ n_document_depth_intervals=3,
+ n_rounds=2,
+ n_needles_total=4,
+ n_needles_retrieve=4,
+ seed=1234,
+ mesh_dim='1,-1,1,1',
+ dtype='fp32',
+ load_llama_config='',
+ update_llama_config='',
+ load_checkpoint='',
+ tokenizer=LLaMAConfig.get_tokenizer_config(),
+ checkpointer=StreamingCheckpointer.get_default_config(),
+ llama=LLaMAConfig.get_default_config(),
+ jax_distributed=JaxDistributedConfig.get_default_config(),
+)
+
+
+class LLMNeedleHaystackTester:
+ OURS_TEMPLATE = "You are a helpful assistant. USER: {context} {question} Don't give information outside the document. ASSISTANT: "
+ RANDOM_NEEDLE_CITIES = [
+ 'Chicago', 'Yangon', 'Antananarivo', 'Colombo', 'Almaty', 'Sydney', 'Chicago', 'Mexico City',
+ 'Seattle', 'Lagos', 'Amsterdam', 'Belgrade', 'Cairo', 'Baghdad', 'Damascus', 'Kigali', 'Dakar',
+ 'Dakar', 'Sofia', 'Kigali', 'Victoria', 'Tashkent', 'Mumbai', 'Barcelona', 'Almaty', 'Amman',
+ 'Toronto', 'Bratislava', 'Johannesburg', 'Thimphu', 'Bangkok', 'Santiago', 'Cairo', 'San Francisco',
+ 'Lagos', 'Amsterdam', 'Paris', 'Rabat', 'Santiago', 'Copenhagen', 'Madrid', 'Kigali',
+ 'Ho Chi Minh City', 'Sarajevo', 'Delhi', 'Istanbul', 'Ho Chi Minh City', 'Khartoum', 'Helsinki',
+ 'Doha', 'Istanbul', 'Kuala Lumpur', 'Budapest', 'Shanghai', 'Moscow', 'Los Angeles', 'Oslo',
+ 'Johannesburg', 'Berlin', 'Bangalore', 'Tokyo', 'Melbourne', 'Barcelona', 'Chicago', 'Port Louis',
+ 'Lisbon', 'Nairobi', 'Kampala', 'Lima', 'Maputo', 'Vancouver', 'Dubai', 'Khartoum', 'Jakarta',
+ 'Madrid', 'Yerevan', 'Beirut', 'Athens', 'Chicago', 'Paris', 'Bucharest', 'Copenhagen', 'Brussels',
+ 'Damascus', 'Seattle', 'Los Angeles', 'Yerevan', 'Victoria', 'Tunis', 'Astana', 'Seoul',
+ 'Buenos Aires', 'Bangkok', 'Colombo', 'Brussels', 'Khartoum', 'Doha', 'San Francisco', 'Vienna', 'Jakarta'
+ ]
+
+ def __init__(self,
+ needle="",
+ haystack_file="",
+ retrieval_question="What are the special magic numbers for {}?",
+ results_version = 1,
+ rnd_number_digits = 7,
+ context_lengths_min = 1000,
+ context_lengths_max = 126000,
+ context_lengths_num_intervals = 10,
+ document_depth_percent_min = 0,
+ document_depth_percent_max = 100,
+ document_depth_percent_intervals = 10,
+ document_depth_percent_interval_type = "linear",
+ save_results = False,
+ final_context_length_buffer = 200,
+ print_ongoing_status = True):
+ needle="\nThe special magic {city} number is: {rnd_number}\n"
+ self.needle = needle
+ if not needle or not haystack_file or not retrieval_question:
+ raise ValueError("Needle, haystack, and retrieval_question must be provided.")
+
+ self.rnd_number_digits = rnd_number_digits
+ self.context_lengths_num_intervals = context_lengths_num_intervals
+ self.document_depth_percent_intervals = document_depth_percent_intervals
+ self.haystack_file = haystack_file
+ self.retrieval_question = retrieval_question
+ self.results_version = results_version
+ self.save_results = save_results
+ self.final_context_length_buffer = final_context_length_buffer
+ self.print_ongoing_status = print_ongoing_status
+ self.testing_results = []
+
+ self.context_lengths = np.round(np.linspace(context_lengths_min, context_lengths_max, num=context_lengths_num_intervals, endpoint=True)).astype(int)
+ self.context_lengths = self.context_lengths.tolist()
+ if document_depth_percent_interval_type == 'linear':
+ self.document_depth_percents = np.round(np.linspace(document_depth_percent_min, document_depth_percent_max, num=document_depth_percent_intervals, endpoint=True)).astype(int)
+ elif document_depth_percent_interval_type == 'sigmoid':
+ self.document_depth_percents = [self.logistic(x) for x in np.linspace(document_depth_percent_min, document_depth_percent_max, document_depth_percent_intervals)]
+ else:
+ raise ValueError(f"Unsupported document_depth_percent_interval_type: {document_depth_percent_interval_type}")
+ self.document_depth_percents = self.document_depth_percents.tolist()
+
+ self.model = Sampler()
+
+ self.enc = LLaMAConfig.get_tokenizer(FLAGS.tokenizer)
+ self.enc_tiktoken = tiktoken.encoding_for_model("gpt-4-1106-preview")
+
+ def generate_random_number(self, num_digits):
+ lower_bound = 10**(num_digits - 1)
+ upper_bound = 10**num_digits - 1
+ return random.randint(lower_bound, upper_bound)
+
+ def logistic(self, x, L=100, x0=50, k=.1):
+ if x == 0:
+ return 0
+ if x == 100:
+ return 100
+ return np.round(L / (1 + np.exp(-k * (x - x0))), 3)
+
+ def read_context_files(self, n):
+ max_context_length = max(self.context_lengths)
+ contexts = []
+ f = open_file(self.haystack_file, 'r')
+ for i in range(n):
+ context = ""
+ while len(self.enc.encode(context)) < max_context_length:
+ context += json.loads(f.readline())['text']
+ contexts.append(context)
+ return contexts
+
+ def encode_and_trim(self, context, context_length):
+ tokens = self.enc.encode(context)
+ if len(tokens) > context_length:
+ context = self.enc.decode(tokens[:context_length])
+ return context
+
+ def create_contexts(self, needles_info, random_cities_retrieve, context, context_length, seed):
+ assert all([random_city in needles_info for random_city in random_cities_retrieve])
+ for random_city, (needle_rnd_number, depth_percent) in needles_info.items():
+ context = self.generate_context(
+ self.needle.format(city=random_city, rnd_number=needle_rnd_number),
+ context, context_length, depth_percent
+ )
+
+ if len(random_cities_retrieve) == 1:
+ question = f"What is the special magic number for {random_cities_retrieve[0]}?"
+ else:
+ q = ', '.join(random_cities_retrieve[:-1]) + ', and ' + random_cities_retrieve[-1]
+ question = self.retrieval_question.format(q)
+ results = {
+ 'context' : context,
+ 'context_length' : int(context_length),
+ 'needles_info': needles_info,
+ 'question' : question,
+ 'cities_to_retrieve' : random_cities_retrieve,
+ 'seed': seed,
+ }
+ return results
+
+ def insert_needle(self, needle, context, depth_percent, context_length):
+ tokens_needle = self.enc_tiktoken.encode(needle)
+ tokens_context = self.enc_tiktoken.encode(context)
+
+ # Reducing the context length by 150 buffer. This is to account for system message, the user question, and response.
+ context_length -= self.final_context_length_buffer
+
+ # If your context + needle are longer than the context length (which it will be), then reduce tokens from the context by the needle length
+ if len(tokens_context) + len(tokens_needle) > context_length:
+ tokens_context = tokens_context[:context_length - len(tokens_needle)]
+
+ if depth_percent == 100:
+ # If your depth percent is 100 (which means your needle is the last thing in the doc), throw it at the end
+ tokens_new_context = tokens_context + tokens_needle
+ else:
+ # Go get the position (in terms of tokens) to insert your needle
+ insertion_point = int(len(tokens_context) * (depth_percent / 100))
+
+ # tokens_new_context represents the tokens before the needle
+ tokens_new_context = tokens_context[:insertion_point]
+
+ # We want to make sure that we place our needle at a sentence break so we first see what token a '.' is
+ period_tokens = self.enc_tiktoken.encode('.')
+
+ # Then we iteration backwards until we find the first period
+ while tokens_new_context and tokens_new_context[-1] not in period_tokens:
+ insertion_point -= 1
+ tokens_new_context = tokens_context[:insertion_point]
+
+ # Once we get there, then add in your needle, and stick the rest of your context in on the other end.
+ # Now we have a needle in a haystack
+ tokens_new_context += tokens_needle + tokens_context[insertion_point:]
+
+ # Convert back to a string and return it
+ new_context = self.enc_tiktoken.decode(tokens_new_context)
+ return new_context
+
+ def generate_context(self, needle, trim_context, context_length, depth_percent):
+ context = self.insert_needle(needle, trim_context, depth_percent, context_length)
+ return context
+
+ def compute_max_input_length(self, context_length, buffer=1024):
+ block_size = self.model.block_size
+ context_length += buffer
+ # context_length = 2 ** math.ceil(math.log2(context_length))
+ context_length = math.ceil(context_length / block_size) * block_size
+ return int(context_length)
+
+ def run_test(self):
+ fs = gcsfs.GCSFileSystem()
+ contexts = []
+ template = self.OURS_TEMPLATE
+
+ def _key_from_result(result):
+ return (result['context_length'], result['depth_percent'], result['seed'])
+
+ results = []
+ completed = set()
+ def exists(fname):
+ if fname.startswith('gs://'):
+ return fs.exists(fname)
+ else:
+ return os.path.exists(fname)
+ if exists(FLAGS.output_file):
+ with open_file(FLAGS.output_file, 'r') as f:
+ results = json.load(f)
+ completed = set([_key_from_result(result) for result in results])
+ print('completed', len(completed))
+
+ full_contexts = self.read_context_files(FLAGS.n_rounds)
+ full_tokens = [self.enc.encode(full_context) for full_context in full_contexts]
+
+ start = time.time()
+ for context_length in self.context_lengths:
+ trim_contexts = [self.enc.decode(full_token[:context_length]) for full_token in full_tokens]
+ max_input_length = self.compute_max_input_length(context_length)
+ contexts = []
+ for i in range(FLAGS.n_rounds):
+ if (int(context_length), i) in completed:
+ continue
+ random_cities = random.sample(LLMNeedleHaystackTester.RANDOM_NEEDLE_CITIES, FLAGS.n_needles_total)
+ document_depths = random.sample(self.document_depth_percents, FLAGS.n_needles_total)
+ random_cities_retrieve = random.sample(random_cities, FLAGS.n_needles_retrieve)
+ needles_info = {}
+ for random_city, depth_percent in zip(random_cities, document_depths):
+ needles_info[random_city] = (
+ str(self.generate_random_number(self.rnd_number_digits)),
+ depth_percent
+ )
+ context = self.create_contexts(needles_info, random_cities_retrieve, trim_contexts[i], context_length, i)
+ contexts.append(context)
+
+ if len(contexts) == 0:
+ continue
+
+ B = FLAGS.max_tokens_per_batch / (max_input_length + self.model.block_size)
+ B = int(B / self.model.data_dim) * self.model.data_dim
+ if B < self.model.data_dim:
+ B = self.model.data_dim
+ elif B > len(contexts):
+ B = int(math.ceil(len(contexts) / self.model.data_dim) * self.model.data_dim)
+ n_pad = B - len(contexts) % B
+ for _ in range(n_pad):
+ contexts.insert(0, contexts[0])
+
+ pbar = tqdm(total=len(contexts))
+ for i in range(0, len(contexts), B):
+ contexts_i = contexts[i:i + B]
+ prompts = [
+ template.format(context=context['context'], question=context['question'])
+ for context in contexts_i
+ ]
+ outs = self.model(prompts, max_input_length)
+ for j, (context, out) in enumerate(zip(contexts_i, outs)):
+ if i + j < n_pad:
+ continue
+ rnd_nums_to_retrieve = [
+ context['needles_info'][city][0] for city in context['cities_to_retrieve']
+ ]
+ results.append({
+ 'context_length': context['context_length'],
+ 'needles_info': context['needles_info'],
+ 'question': context['question'],
+ 'answer': rnd_nums_to_retrieve,
+ 'response': out,
+ 'correct': [rnd_num in out for rnd_num in rnd_nums_to_retrieve],
+ 'seed': context['seed'],
+ })
+ print(results[-1]['correct'], out, rnd_nums_to_retrieve)
+ if jax.process_index() == 0:
+ with open_file(FLAGS.output_file, 'w') as f:
+ json.dump(results, f)
+ pbar.update(len(contexts_i))
+ pbar.close()
+ print('elapsed', time.time() - start)
+ print('done')
+
+
+ def print_start_test_summary(self):
+ print ("\n")
+ print ("Starting Needle In A Haystack Testing...")
+ print (f"- Context Lengths: {len(self.context_lengths)}, Min: {min(self.context_lengths)}, Max: {max(self.context_lengths)}")
+ print (f"- Document Depths: {len(self.document_depth_percents)}, Min: {min(self.document_depth_percents)}%, Max: {max(self.document_depth_percents)}%")
+ print (f"- Needle: {self.needle.strip()}")
+ print ("\n\n")
+
+ def start_test(self):
+ if self.print_ongoing_status:
+ self.print_start_test_summary()
+ self.run_test()
+
+
+
+class Sampler:
+ def __init__(self):
+ self.mesh = LLaMAConfig.get_jax_mesh(FLAGS.mesh_dim)
+ self.prefix_tokenizer = LLaMAConfig.get_tokenizer(
+ FLAGS.tokenizer, truncation_side='left', padding_side='left'
+ )
+ self.tokenizer = LLaMAConfig.get_tokenizer(FLAGS.tokenizer)
+ self.sharded_rng = next_rng()
+ self._load_model()
+
+ @property
+ def block_size(self):
+ # return 2 * max(self.config.scan_query_chunk_size, self.config.scan_key_chunk_size)
+ return max(self.config.scan_query_chunk_size, self.config.scan_key_chunk_size) * self.mesh.shape['sp']
+
+ @property
+ def data_dim(self):
+ return self.mesh.shape['dp'] * self.mesh.shape['fsdp']
+
+ def _load_model(self):
+ if FLAGS.load_llama_config != '':
+ llama_config = LLaMAConfig.load_config(FLAGS.load_llama_config)
+ updates = LLaMAConfig(**FLAGS.llama)
+ llama_config.update(dict(
+ remat_block=updates.remat_block,
+ remat_attention=updates.remat_attention,
+ remat_mlp=updates.remat_mlp,
+ scan_attention=updates.scan_attention,
+ scan_mlp=updates.scan_mlp,
+ scan_query_chunk_size=updates.scan_query_chunk_size,
+ scan_key_chunk_size=updates.scan_key_chunk_size,
+ scan_mlp_chunk_size=updates.scan_mlp_chunk_size,
+ scan_layers=updates.scan_layers,
+ param_scan_axis=updates.param_scan_axis,
+ ))
+ else:
+ llama_config = LLaMAConfig(**FLAGS.llama)
+
+ if FLAGS.update_llama_config != '':
+ llama_config.update(dict(eval(FLAGS.update_llama_config)))
+
+ llama_config.update(dict(
+ bos_token_id=self.tokenizer.bos_token_id,
+ eos_token_id=self.tokenizer.eos_token_id,
+ ))
+ llama_config.update(dict(mesh_dim=FLAGS.mesh_dim))
+ self.config = llama_config
+
+ with jax.default_device(jax.devices("cpu")[0]):
+ _, self.params = StreamingCheckpointer.load_trainstate_checkpoint(
+ FLAGS.load_checkpoint, disallow_trainstate=True, max_buffer_size=32 * 2 ** 30
+ )
+ self.model = FlaxLLaMAForCausalLM(
+ llama_config,
+ input_shape=(512, self.block_size),
+ seed=FLAGS.seed,
+ _do_init=False,
+ dtype=get_float_dtype_by_name(FLAGS.dtype),
+ )
+ self.model_ps = match_partition_rules(
+ LLaMAConfig.get_partition_rules(llama_config.scan_layers, llama_config.param_scan_axis), self.params
+ )
+ shard_fns, _ = make_shard_and_gather_fns(
+ self.model_ps, get_float_dtype_by_name(FLAGS.dtype)
+ )
+
+ with self.mesh:
+ self.params = tree_apply(shard_fns, self.params)
+
+ @cached_property
+ def _forward_generate(self):
+ def fn(params, rng, batch):
+ batch = with_sharding_constraint(batch, PS(('dp', 'fsdp'), 'sp'))
+ rng_generator = JaxRNG(rng)
+ output = self.model.generate(
+ batch['input_ids'],
+ attention_mask=batch['attention_mask'],
+ params=params['params'],
+ prng_key=rng_generator(),
+ generation_config=GenerationConfig(
+ max_new_tokens=self.block_size,
+ pad_token_id=self.tokenizer.pad_token_id,
+ eos_token_id=self.tokenizer.eos_token_id,
+ temperature=0.,
+ do_sample=False,
+ num_beams=1,
+ top_k=50,
+ top_p=1.0,
+ )
+ ).sequences[:, batch['input_ids'].shape[1]:]
+ return output, rng_generator()
+ return pjit(
+ fn,
+ in_shardings=(self.model_ps, PS(), PS()),
+ out_shardings=(PS(), PS())
+ )
+
+ def __call__(self, prompts, max_input_length):
+ inputs = self.prefix_tokenizer(
+ prompts,
+ padding='max_length',
+ truncation=True,
+ max_length=max_input_length,
+ return_tensors='np'
+ )
+ batch = dict(
+ input_ids=inputs.input_ids,
+ attention_mask=inputs.attention_mask
+ )
+ with self.mesh:
+ output, self.sharded_rng = self._forward_generate(
+ self.params, self.sharded_rng, batch
+ )
+ output = jax.device_get(output)
+ output_text = []
+ for text in list(self.tokenizer.batch_decode(output, skip_special_tokens=True)):
+ if self.tokenizer.eos_token in text:
+ text = text.split(self.tokenizer.eos_token, maxsplit=1)[0]
+ output_text.append(text)
+ return output_text
+
+
+def main(argv):
+ JaxDistributedConfig.initialize(FLAGS.jax_distributed)
+ set_random_seed(FLAGS.seed)
+
+ ht = LLMNeedleHaystackTester(
+ haystack_file=FLAGS.haystack_file,
+ context_lengths_min=FLAGS.context_lengths_min,
+ context_lengths_max=FLAGS.context_lengths_max,
+ context_lengths_num_intervals=FLAGS.n_context_length_intervals,
+ document_depth_percent_intervals=FLAGS.n_document_depth_intervals,
+ )
+ ht.start_test()
+
+if __name__ == "__main__":
+ run(main)
diff --git a/scripts/run_eval_needle.sh b/scripts/run_eval_needle.sh
new file mode 100755
index 0000000..24eae3b
--- /dev/null
+++ b/scripts/run_eval_needle.sh
@@ -0,0 +1,31 @@
+#! /bin/bash
+
+export SCRIPT_DIR="$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
+export PROJECT_DIR="$( cd -- "$( dirname -- "$SCRIPT_DIR" )" &> /dev/null && pwd )"
+cd $PROJECT_DIR
+export PYTHONPATH="$PYTHONPATH:$PROJECT_DIR"
+export LIBTPU_INIT_ARGS="--xla_tpu_megacore_fusion_allow_ags=false --xla_enable_async_collective_permute=true --xla_tpu_enable_ag_backward_pipelining=true --xla_tpu_enable_data_parallel_all_reduce_opt=true --xla_tpu_data_parallel_opt_different_sized_ops=true --xla_tpu_enable_async_collective_fusion=true --xla_tpu_enable_async_collective_fusion_multiple_steps=true --xla_tpu_overlap_compute_collective_tc=true --xla_enable_async_all_gather=true"
+
+export llama_tokenizer_path=""
+export lwm_text_checkpoint=""
+# jsonl file containing text for haystack. Each line should be a json
+# with a single key "text" containing the text.
+export haystack_file=""
+export output_file=""
+
+python3 -u scripts/eval_needle.py \
+ --mesh_dim='!1,-1,4,1' \
+ --dtype='fp32' \
+ --load_llama_config='7b' \
+ --update_llama_config="dict(theta=10000000,max_sequence_length=131072,use_flash_attention=False,scan_attention=True,scan_query_chunk_size=1024,scan_key_chunk_size=1024,scan_mlp=True,scan_mlp_chunk_size=1024,scan_layers=True)" \
+ --load_checkpoint="params::$lwm_text_checkpoint" \
+ --tokenizer.vocab_file="$llama_tokenizer_path" \
+ --max_tokens_per_batch=5000 \
+ --output_file="$output_file" \
+ --haystack_file="$haystack_file" \
+ --context_lengths_min=1000 \
+ --context_lengths_max=10000 \
+ --n_context_length_intervals=20 \
+ --n_document_depth_intervals=20 \
+ --n_rounds=3
+read
diff --git a/scripts/run_eval_needle_multi.sh b/scripts/run_eval_needle_multi.sh
new file mode 100755
index 0000000..c87e2a6
--- /dev/null
+++ b/scripts/run_eval_needle_multi.sh
@@ -0,0 +1,33 @@
+#! /bin/bash
+
+export SCRIPT_DIR="$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
+export PROJECT_DIR="$( cd -- "$( dirname -- "$SCRIPT_DIR" )" &> /dev/null && pwd )"
+cd $PROJECT_DIR
+export PYTHONPATH="$PYTHONPATH:$PROJECT_DIR"
+export LIBTPU_INIT_ARGS="--xla_tpu_megacore_fusion_allow_ags=false --xla_enable_async_collective_permute=true --xla_tpu_enable_ag_backward_pipelining=true --xla_tpu_enable_data_parallel_all_reduce_opt=true --xla_tpu_data_parallel_opt_different_sized_ops=true --xla_tpu_enable_async_collective_fusion=true --xla_tpu_enable_async_collective_fusion_multiple_steps=true --xla_tpu_overlap_compute_collective_tc=true --xla_enable_async_all_gather=true"
+
+export llama_tokenizer_path=""
+export lwm_text_checkpoint=""
+# jsonl file containing text for haystack. Each line should be a json
+# with a single key "text" containing the text.
+export haystack_file=""
+export output_file=""
+
+python3 -u scripts/eval_needle_multi.py \
+ --mesh_dim='!1,-1,4,1' \
+ --dtype='fp32' \
+ --load_llama_config='7b' \
+ --update_llama_config="dict(theta=10000000,max_sequence_length=131072,use_flash_attention=False,scan_attention=True,scan_query_chunk_size=1024,scan_key_chunk_size=1024,scan_mlp=True,scan_mlp_chunk_size=1024,scan_layers=True)" \
+ --load_checkpoint="params::$lwm_text_checkpoint" \
+ --tokenizer.vocab_file="$llama_tokenizer_path" \
+ --max_tokens_per_batch=5000 \
+ --output_file="$output_file" \
+ --haystack_file="$haystack_file" \
+ --context_lengths_min=1000 \
+ --context_lengths_max=10000 \
+ --n_context_length_intervals=10 \
+ --n_document_depth_intervals=10 \
+ --n_needles_total=4 \
+ --n_needles_retrieve=2 \
+ --n_rounds=10
+read
diff --git a/scripts/run_sample_image.sh b/scripts/run_sample_image.sh
new file mode 100755
index 0000000..2627ad1
--- /dev/null
+++ b/scripts/run_sample_image.sh
@@ -0,0 +1,27 @@
+#! /bin/bash
+
+export SCRIPT_DIR="$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
+export PROJECT_DIR="$( cd -- "$( dirname -- "$SCRIPT_DIR" )" &> /dev/null && pwd )"
+cd $PROJECT_DIR
+export PYTHONPATH="$PYTHONPATH:$PROJECT_DIR"
+export LIBTPU_INIT_ARGS="--xla_tpu_megacore_fusion_allow_ags=false --xla_enable_async_collective_permute=true --xla_tpu_enable_ag_backward_pipelining=true --xla_tpu_enable_data_parallel_all_reduce_opt=true --xla_tpu_data_parallel_opt_different_sized_ops=true --xla_tpu_enable_async_collective_fusion=true --xla_tpu_enable_async_collective_fusion_multiple_steps=true --xla_tpu_overlap_compute_collective_tc=true --xla_enable_async_all_gather=true"
+
+export llama_tokenizer_path=""
+export vqgan_checkpoint=""
+export lwm_checkpoint=""
+
+python3 -u -m lwm.vision_generation \
+ --prompt='Fireworks over the city' \
+ --output_file='fireworks.png' \
+ --temperature_image=1.0 \
+ --top_k_image=8192 \
+ --cfg_scale_image=5.0 \
+ --vqgan_checkpoint="$vqgan_checkpoint" \
+ --n_frames=1 \
+ --mesh_dim='!-1,1,8,1' \
+ --dtype='fp32' \
+ --load_llama_config='7b' \
+ --update_llama_config="dict(sample_mode='vision',theta=50000000,max_sequence_length=32768,use_flash_attention=True,scan_attention=False,scan_query_chunk_size=128,scan_key_chunk_size=128,scan_mlp=False,scan_mlp_chunk_size=8192,scan_layers=True)" \
+ --load_checkpoint="params::$lwm_checkpoint" \
+ --tokenizer.vocab_file="$llama_tokenizer_path"
+read
diff --git a/scripts/run_sample_video.sh b/scripts/run_sample_video.sh
new file mode 100755
index 0000000..f4cbd12
--- /dev/null
+++ b/scripts/run_sample_video.sh
@@ -0,0 +1,30 @@
+#! /bin/bash
+
+export SCRIPT_DIR="$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
+export PROJECT_DIR="$( cd -- "$( dirname -- "$SCRIPT_DIR" )" &> /dev/null && pwd )"
+cd $PROJECT_DIR
+export PYTHONPATH="$PYTHONPATH:$PROJECT_DIR"
+export LIBTPU_INIT_ARGS="--xla_tpu_megacore_fusion_allow_ags=false --xla_enable_async_collective_permute=true --xla_tpu_enable_ag_backward_pipelining=true --xla_tpu_enable_data_parallel_all_reduce_opt=true --xla_tpu_data_parallel_opt_different_sized_ops=true --xla_tpu_enable_async_collective_fusion=true --xla_tpu_enable_async_collective_fusion_multiple_steps=true --xla_tpu_overlap_compute_collective_tc=true --xla_enable_async_all_gather=true"
+
+export llama_tokenizer_path=""
+export vqgan_checkpoint=""
+export lwm_checkpoint=""
+
+python3 -u -m lwm.vision_generation \
+ --prompt='Fireworks over the city' \
+ --output_file='fireworks.mp4' \
+ --temperature_image=1.0 \
+ --temperature_video=1.0 \
+ --top_k_image=8192 \
+ --top_k_video=1000 \
+ --cfg_scale_image=5.0 \
+ --cfg_scale_video=1.0 \
+ --vqgan_checkpoint="$vqgan_checkpoint" \
+ --n_frames=8 \
+ --mesh_dim='!-1,1,8,1' \
+ --dtype='fp32' \
+ --load_llama_config='7b' \
+ --update_llama_config="dict(sample_mode='vision',theta=50000000,max_sequence_length=32768,use_flash_attention=True,scan_attention=False,scan_query_chunk_size=128,scan_key_chunk_size=128,scan_mlp=False,scan_mlp_chunk_size=8192,scan_layers=True)" \
+ --load_checkpoint="params::$lwm_checkpoint" \
+ --tokenizer.vocab_file="$llama_tokenizer_path"
+read
diff --git a/scripts/run_train_text.sh b/scripts/run_train_text.sh
new file mode 100755
index 0000000..c1b83e3
--- /dev/null
+++ b/scripts/run_train_text.sh
@@ -0,0 +1,55 @@
+#! /bin/bash
+
+export SCRIPT_DIR="$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
+export PROJECT_DIR="$( cd -- "$( dirname -- "$SCRIPT_DIR" )" &> /dev/null && pwd )"
+cd $PROJECT_DIR
+export PYTHONPATH="$PYTHONPATH:$PROJECT_DIR"
+export LIBTPU_INIT_ARGS="--xla_tpu_megacore_fusion_allow_ags=false --xla_enable_async_collective_permute=true --xla_tpu_enable_ag_backward_pipelining=true --xla_tpu_enable_data_parallel_all_reduce_opt=true --xla_tpu_data_parallel_opt_different_sized_ops=true --xla_tpu_enable_async_collective_fusion=true --xla_tpu_enable_async_collective_fusion_multiple_steps=true --xla_tpu_overlap_compute_collective_tc=true --xla_enable_async_all_gather=true"
+
+export llama_tokenizer_path=""
+export dataset_path=""
+export output_dir=""
+
+export project_id='lwm'
+export experiment_note=''
+export experiment_id='example-text-train'
+
+# mesh_dim: dp, fsdp, tp, sp
+python3 -u -m lwm.train \
+ --modality='text' \
+ --mesh_dim='!1,-1,2,2' \
+ --dtype='fp32' \
+ --total_steps=200\
+ --log_freq=1 \
+ --save_model_freq=0 \
+ --save_milestone_freq=10 \
+ --load_llama_config='debug' \
+ --update_llama_config="dict(theta=10000,max_sequence_length=4096,use_flash_attention=False,scan_attention=True,scan_query_chunk_size=512,scan_key_chunk_size=1024,remat_attention='nothing_saveable',scan_mlp=True,scan_mlp_chunk_size=8192,remat_mlp='nothing_saveable',remat_block='nothing_saveable',scan_layers=True)" \
+ --tokenizer.vocab_file="$llama_tokenizer_path" \
+ --optimizer.type='adamw' \
+ --optimizer.accumulate_gradient_steps=1 \
+ --optimizer.adamw_optimizer.weight_decay=0.1 \
+ --optimizer.adamw_optimizer.lr=8e-5 \
+ --optimizer.adamw_optimizer.end_lr=8e-5 \
+ --optimizer.adamw_optimizer.lr_warmup_steps=5 \
+ --optimizer.adamw_optimizer.lr_decay_steps=200 \
+ --use_data_sharded_loader=True \
+ --train_dataset.type='json' \
+ --train_dataset.text_processor.fields='text' \
+ --train_dataset.json_dataset.path="$dataset_path" \
+ --train_dataset.json_dataset.seq_length=1024 \
+ --train_dataset.json_dataset.batch_size=8 \
+ --train_dataset.json_dataset.tokenizer_processes=4 \
+ --train_dataset.json_dataset.tokenizer_parallel_chunk_size=2 \
+ --train_dataset.json_dataset.tokenizer_parallel_batch_size=8 \
+ --train_dataset.json_dataset.use_data_sharded_loader=True \
+ --checkpointer.save_optimizer_state=True \
+ --autoresume=False \
+ --logger.append_uuid=False \
+ --logger.online=False \
+ --logger.project_id="$project_id" \
+ --logger.experiment_id="$experiment_id" \
+ --logger.experiment_note="$experiment_note" \
+ --logger.output_dir="$output_dir" \
+ --logger.wandb_dir="$HOME/experiment_output/$project_id"
+read
diff --git a/scripts/run_train_vision_text.sh b/scripts/run_train_vision_text.sh
new file mode 100755
index 0000000..6f5af6b
--- /dev/null
+++ b/scripts/run_train_vision_text.sh
@@ -0,0 +1,57 @@
+#! /bin/bash
+
+export SCRIPT_DIR="$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
+export PROJECT_DIR="$( cd -- "$( dirname -- "$SCRIPT_DIR" )" &> /dev/null && pwd )"
+cd $PROJECT_DIR
+export PYTHONPATH="$PYTHONPATH:$PROJECT_DIR"
+export LIBTPU_INIT_ARGS="--xla_tpu_megacore_fusion_allow_ags=false --xla_enable_async_collective_permute=true --xla_tpu_enable_ag_backward_pipelining=true --xla_tpu_enable_data_parallel_all_reduce_opt=true --xla_tpu_data_parallel_opt_different_sized_ops=true --xla_tpu_enable_async_collective_fusion=true --xla_tpu_enable_async_collective_fusion_multiple_steps=true --xla_tpu_overlap_compute_collective_tc=true --xla_enable_async_all_gather=true"
+
+export llama_tokenizer_path=""
+export dataset_path=""
+export output_dir=""
+
+export project_id='lwm'
+export experiment_note=''
+export experiment_id='example-vision-text-train'
+
+# mesh_dim: dp, fsdp, tp, sp
+python3 -u -m lwm.train \
+ --modality='vision,text' \
+ --mesh_dim='!1,-1,2,2' \
+ --dtype='fp32' \
+ --total_steps=200 \
+ --log_freq=1 \
+ --save_model_freq=0 \
+ --save_milestone_freq=10 \
+ --load_llama_config='debug' \
+ --update_llama_config="dict(theta=50000000,max_sequence_length=2048,use_flash_attention=True,scan_attention=True,scan_query_chunk_size=512,scan_key_chunk_size=1024,remat_attention='nothing_saveable',scan_mlp=True,scan_mlp_chunk_size=8192,remat_mlp='nothing_saveable',remat_block='nothing_saveable',scan_layers=True)" \
+ --tokenizer.vocab_file="$llama_tokenizer_path" \
+ --optimizer.type='adamw' \
+ --optimizer.accumulate_gradient_steps=1 \
+ --optimizer.adamw_optimizer.weight_decay=0.1 \
+ --optimizer.adamw_optimizer.lr=8e-5 \
+ --optimizer.adamw_optimizer.end_lr=8e-5 \
+ --optimizer.adamw_optimizer.lr_warmup_steps=5 \
+ --optimizer.adamw_optimizer.lr_decay_steps=200 \
+ --use_data_sharded_loader=True \
+ --train_dataset.type='json_vision' \
+ --train_dataset.vision_text_processor.fields_from_example='fields' \
+ --train_dataset.vision_text_processor.max_n_frames=4 \
+ --train_dataset.json_vision_dataset.mode="no_pad" \
+ --train_dataset.json_vision_dataset.path="$dataset_path" \
+ --train_dataset.json_vision_dataset.seq_length=2048 \
+ --train_dataset.json_vision_dataset.batch_size=8 \
+ --train_dataset.json_vision_dataset.tokenizer_processes=4 \
+ --train_dataset.json_vision_dataset.tokenizer_parallel_chunk_size=2 \
+ --train_dataset.json_vision_dataset.tokenizer_parallel_batch_size=8 \
+ --train_dataset.json_vision_dataset.use_data_sharded_loader=True \
+ --checkpointer.save_optimizer_state=True \
+ --autoresume=False \
+ --logger.append_uuid=False \
+ --logger.online=False \
+ --logger.project_id="$project_id" \
+ --logger.experiment_id="$experiment_id" \
+ --logger.experiment_note="$experiment_note" \
+ --logger.output_dir="$output_dir" \
+ --logger.wandb_dir="$HOME/experiment_output/$project_id"
+read
diff --git a/scripts/run_vision_chat.sh b/scripts/run_vision_chat.sh
new file mode 100755
index 0000000..869ceb4
--- /dev/null
+++ b/scripts/run_vision_chat.sh
@@ -0,0 +1,25 @@
+#! /bin/bash
+
+export SCRIPT_DIR="$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
+export PROJECT_DIR="$( cd -- "$( dirname -- "$SCRIPT_DIR" )" &> /dev/null && pwd )"
+cd $PROJECT_DIR
+export PYTHONPATH="$PYTHONPATH:$PROJECT_DIR"
+
+export llama_tokenizer_path=""
+export vqgan_checkpoint=""
+export lwm_checkpoint=""
+export input_file=""
+
+python3 -u -m lwm.vision_chat \
+ --prompt="What is the video about?" \
+ --input_file="$input_file" \
+ --vqgan_checkpoint="$vqgan_checkpoint" \
+ --mesh_dim='!1,-1,32,1' \
+ --dtype='fp32' \
+ --load_llama_config='7b' \
+ --max_n_frames=8 \
+ --update_llama_config="dict(sample_mode='text',theta=50000000,max_sequence_length=131072,use_flash_attention=False,scan_attention=False,scan_query_chunk_size=128,scan_key_chunk_size=128,remat_attention='',scan_mlp=False,scan_mlp_chunk_size=2048,remat_mlp='',remat_block='',scan_layers=True)" \
+ --load_checkpoint="params::$lwm_checkpoint" \
+ --tokenizer.vocab_file="$llama_tokenizer_path" \
+2>&1 | tee ~/output.log
+read
diff --git a/tpu_vm_setup.sh b/tpu_vm_setup.sh
new file mode 100755
index 0000000..6c4d1fb
--- /dev/null
+++ b/tpu_vm_setup.sh
@@ -0,0 +1,170 @@
+#! /bin/bash
+
+sudo umount /mnt/ramdisk
+sudo rm -rf /mnt/ramdisk
+sudo mkdir /mnt/ramdisk
+sudo mount -t tmpfs -o size=10G tmpfs /mnt/ramdisk
+
+sudo apt-get update && sudo apt-get install -y \
+ build-essential \
+ python-is-python3 \
+ tmux \
+ htop \
+ git \
+ nodejs \
+ bmon \
+ p7zip-full \
+ nfs-common \
+ ffmpeg
+
+# Update pip
+pip install --upgrade pip
+
+pip uninstall -y tux
+
+# Python dependencies
+cat > $HOME/tpu_requirements.txt <<- EndOfFile
+-f https://storage.googleapis.com/jax-releases/libtpu_releases.html
+jax[tpu]==0.4.23
+tensorflow-cpu==2.11.0
+tensorboard-plugin-profile
+flax==0.7.0
+optax==0.1.7
+chex==0.1.82
+einops
+--extra-index-url https://download.pytorch.org/whl/cpu
+torch==2.0.0
+torchvision==0.15.0
+transformers==4.29.2
+datasets==2.13.0
+tqdm
+ml_collections
+wandb
+requests
+gcsfs
+typing-extensions
+sentencepiece
+tux @ git+https://github.com/lhao499/tux.git
+Pillow
+ipdb
+imageio[ffmpeg]
+tiktoken
+EndOfFile
+
+pip install --upgrade -r $HOME/tpu_requirements.txt
+
+# vim configurations
+cat > $HOME/.vimrc <<- EndOfFile
+set tabstop=4
+set shiftwidth=4
+set softtabstop=4
+set expandtab
+set backspace=indent,eol,start
+syntax on
+EndOfFile
+
+# tmux configurations
+cat > $HOME/.tmux.conf <<- EndOfFile
+bind r source-file ~/.tmux.conf \; display-message "█▓░ ~/.tmux.conf reloaded."
+
+# Enable colors, https://github.com/tmux/tmux/wiki/FAQ
+set -g default-terminal "tmux-256color"
+
+# start with window 1 (instead of 0)
+set -g base-index 1
+setw -g pane-base-index 1
+
+set -g prefix C-a
+
+set -g set-titles on
+set -g set-titles-string '#(whoami)::#h::#(curl ipecho.net/plain;echo)'
+
+# Status bar customization
+set -g status-interval 5
+set -g status-left-length 90
+set -g status-right-length 60
+set -g status-justify left
+
+# send the prefix to client inside window (ala nested sessions)
+bind-key a send-prefix
+
+bind-key x kill-pane
+
+# auto reorder
+set-option -g renumber-windows on
+
+# default window name
+set -g status-left "#[fg=green,bg=colour236] #S "
+
+# default statusbar colors
+set-option -g status-style fg=yellow,dim,bg=colour235
+
+# default window title colors
+set-window-option -g window-status-style fg=yellow,bg=colour236,dim
+
+# active window title colors
+set-window-option -g window-status-current-style fg=brightred,bg=colour236
+
+# basename as window title https://stackoverflow.com/a/37136828
+set-window-option -g window-status-current-format '#{window_index} #{pane_current_command} #(echo "#{pane_current_path}" | rev | cut -d'/' -f-3 | rev)'
+set-window-option -g window-status-format '#{window_index} #{pane_current_command} #(echo "#{pane_current_path}" | rev | cut -d'/' -f-3 | rev)'
+
+# pane border
+set-option -g pane-border-style fg=white #base2
+set-option -g pane-active-border-style fg=brightcyan #base1
+
+# enable mouse click
+set -g mouse on
+
+# keep window on
+set -g remain-on-exit on
+
+# Longer scrollback history
+set -g history-limit 50000
+
+# Scroll position indicator
+set -g mode-style bg=colour235,fg=colour245
+
+# SSH agent forwarding
+# set-environment -g SSH_AUTH_SOCK $SSH_AUTH_SOCK
+if-shell '[ -n $SSH_AUTH_SOCK ]' " \
+ set-option -sg update-environment \"DISPLAY WINDOWID XAUTHORITY\"; \
+ setenv -g SSH_AUTH_SOCK /tmp/ssh_auth_sock_tmux; \
+ run-shell \"ln -sf $(find /tmp/ssh-* -type s -readable | head -n 1) /tmp/ssh_auth_sock_tmux\" \
+"
+
+# Drag windows on the status bar
+bind-key -n MouseDrag1Status swap-window -t=
+EndOfFile
+
+
+# htop Configurations
+mkdir -p $HOME/.config/htop
+cat > $HOME/.config/htop/htoprc <<- EndOfFile
+# Beware! This file is rewritten by htop when settings are changed in the interface.
+# The parser is also very primitive, and not human-friendly.
+fields=0 48 17 18 38 39 40 2 46 47 49 1
+sort_key=46
+sort_direction=1
+hide_threads=0
+hide_kernel_threads=1
+hide_userland_threads=1
+shadow_other_users=0
+show_thread_names=0
+show_program_path=1
+highlight_base_name=0
+highlight_megabytes=1
+highlight_threads=1
+tree_view=0
+header_margin=1
+detailed_cpu_time=0
+cpu_count_from_zero=0
+update_process_names=0
+account_guest_in_cpu_meter=0
+color_scheme=0
+delay=15
+left_meters=CPU Memory Swap
+left_meter_modes=1 1 1
+right_meters=Tasks LoadAverage Uptime
+right_meter_modes=2 2 2
+EndOfFile