Add support for jamba model with Liger Kernel

pyproject.toml

@@ -1,5 +1,5 @@
 [build-system]
-requires = ["setuptools>=42", "wheel"]
+requires = ["setuptools>=61.0", "wheel"]
 build-backend = "setuptools.build_meta"
 
 [project]
@@ -27,6 +27,11 @@ dev = [
  "seaborn",
 ]
 
+test = [
+ "causal-conv1d>=1.4.0",
+ "mamba-ssm>=2.2.2",
+]
+
 [tool.setuptools.packages.find]
 where = ["src"]
 include = ["liger_kernel", "liger_kernel.*"]

src/liger_kernel/transformers/__init__.py

@@ -10,6 +10,7 @@
 from liger_kernel.transformers.monkey_patch import ( # noqa: F401
  apply_liger_kernel_to_gemma,
  apply_liger_kernel_to_gemma2,
+ apply_liger_kernel_to_jamba,
  apply_liger_kernel_to_llama,
  apply_liger_kernel_to_mistral,
  apply_liger_kernel_to_mixtral,

src/liger_kernel/transformers/model/jamba.py

@@ -0,0 +1,169 @@
+from typing import Optional, Tuple, Union
+
+import torch
+from torch.nn import CrossEntropyLoss
+from transformers.modeling_outputs import MoeCausalLMOutputWithPast
+from transformers.models.jamba.modeling_jamba import (
+ _CONFIG_FOR_DOC,
+ JAMBA_INPUTS_DOCSTRING,
+ HybridMambaAttentionDynamicCache,
+ load_balancing_loss_func,
+)
+from transformers.utils import (
+ add_start_docstrings_to_model_forward,
+ replace_return_docstrings,
+)
+
+from liger_kernel.transformers.fused_linear_cross_entropy import (
+ LigerFusedLinearCrossEntropyLoss,
+)
+
+
+@add_start_docstrings_to_model_forward(JAMBA_INPUTS_DOCSTRING)
+@replace_return_docstrings(
+ output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+)
+def lce_forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[HybridMambaAttentionDynamicCache] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ output_router_logits: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ cache_position: Optional[torch.LongTensor] = None,
+ num_logits_to_keep: Optional[Union[int, None]] = None,
+) -> Union[Tuple, MoeCausalLMOutputWithPast]:
+ r"""
+ Args:
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ num_logits_to_keep (`int` or `None`, *optional*):
+ Calculate logits for the last `num_logits_to_keep` tokens. If `None`, calculate logits for all
+ `input_ids`. Only last token logits are needed for generation, and calculating them only for that token
+ can save memory, which becomes pretty significant for long sequences.
+
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, JambaForCausalLM
+
+ >>> model = JambaForCausalLM.from_pretrained("ai21labs/Jamba-v0.1")
+ >>> tokenizer = AutoTokenizer.from_pretrained("ai21labs/Jamba-v0.1")
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+
+ output_attentions = (
+ output_attentions
+ if output_attentions is not None
+ else self.config.output_attentions
+ )
+ output_router_logits = (
+ output_router_logits
+ if output_router_logits is not None
+ else self.config.output_router_logits
+ )
+
+ 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.use_return_dict
+ )
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ output_router_logits=output_router_logits,
+ cache_position=cache_position,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+
+ loss = None
+ logits = None
+
+ if self.training:
+ shift_hidden_states = hidden_states[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+
+ # flatten tokens
+ shift_hidden_states = shift_hidden_states.view(-1, self.config.hidden_size)
+ shift_labels = shift_labels.view(-1)
+
+ lce = LigerFusedLinearCrossEntropyLoss()
+ loss = lce(self.lm_head.weight, shift_hidden_states, shift_labels)
+ else:
+ if num_logits_to_keep is None:
+ logits = self.lm_head(hidden_states)
+ else:
+ logits = self.lm_head(hidden_states[..., -num_logits_to_keep:, :])
+ logits = logits.float()
+
+ if labels is not None:
+ # Shift so that tokens < n predict n
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ loss_fct = CrossEntropyLoss()
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
+ shift_labels = shift_labels.view(-1)
+ # Enable model parallelism
+ shift_labels = shift_labels.to(shift_logits.device)
+ loss = loss_fct(shift_logits, shift_labels)
+
+ aux_loss = None
+ if output_router_logits:
+ aux_loss = load_balancing_loss_func(
+ outputs.router_logits if return_dict else outputs[-1],
+ self.num_experts,
+ self.num_experts_per_tok,
+ attention_mask,
+ )
+ if labels is not None:
+ loss += self.router_aux_loss_coef * aux_loss.to(
+ loss.device
+ ) # make sure to reside in the same device
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ if output_router_logits:
+ output = (aux_loss,) + output
+ return (loss,) + output if loss is not None else output
+
+ return MoeCausalLMOutputWithPast(
+ loss=loss,
+ aux_loss=aux_loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ router_logits=outputs.router_logits,
+ )

src/liger_kernel/transformers/monkey_patch.py

@@ -6,6 +6,7 @@
 from liger_kernel.transformers.geglu import LigerGEGLUMLP
 from liger_kernel.transformers.layer_norm import LigerLayerNorm
 from liger_kernel.transformers.model.gemma import lce_forward as gemma_lce_forward
+from liger_kernel.transformers.model.jamba import lce_forward as jamba_lce_forward
 from liger_kernel.transformers.model.llama import lce_forward as llama_lce_forward
 from liger_kernel.transformers.model.mistral import lce_forward as mistral_lce_forward
 from liger_kernel.transformers.model.mixtral import lce_forward as mixtral_lce_forward
@@ -332,6 +333,43 @@ def apply_liger_kernel_to_phi3(
  modeling_phi3.Phi3ForCausalLM.forward = phi3_lce_forward
 
 
+def apply_liger_kernel_to_jamba(
+ cross_entropy: bool = False,
+ fused_linear_cross_entropy: bool = True,
+ rms_norm: bool = True,
+ swiglu: bool = True,
+) -> None:
+ """
+ Apply Liger kernels to replace original implementation in HuggingFace Jamba models
+ to make GPU go burrr.
+
+ # Note: Jamba model does not use rotary position embedding(RoPE).
+
+ Args:
+ cross_entropy (bool): Whether to apply Liger's cross entropy loss. Default is False.
+ fused_linear_cross_entropy (bool):
+ Whether to apply Liger's fused lienar cross entropy loss. Default is True.
+ `cross_entropy` and `fused_linear_cross_entropy` cannot both be True.
+ If `fused_linear_cross_entropy` is True, the logits will not be materialized but more memory efficient.
+ rms_norm (bool): Whether to apply Liger's RMSNorm. Default is True.
+ geglu (bool): Whether to apply Liger's GeGLU MLP. Default is True.
+ """
+ assert not (
+ cross_entropy and fused_linear_cross_entropy
+ ), "cross_entropy and fused_linear_cross_entropy cannot both be True."
+
+ from transformers.models.jamba import modeling_jamba
+
+ if rms_norm:
+ modeling_jamba.JambaRMSNorm = LigerRMSNorm
+ if cross_entropy:
+ modeling_jamba.CrossEntropyLoss = LigerCrossEntropyLoss
+ if swiglu:
+ modeling_jamba.JambaMLP = LigerSwiGLUMLP
+ if fused_linear_cross_entropy:
+ modeling_jamba.JambaForCausalLM.forward = jamba_lce_forward
+
+
 # Model type corresponds to the keys defined in transformers/models/auto/modeling_auto.py
 MODEL_TYPE_TO_APPLY_LIGER_FN = {
  "gemma": apply_liger_kernel_to_gemma,
@@ -342,6 +380,7 @@ def apply_liger_kernel_to_phi3(
  "qwen2": apply_liger_kernel_to_qwen2,
  "qwen2_vl": apply_liger_kernel_to_qwen2_vl,
  "phi3": apply_liger_kernel_to_phi3,
+ "jamba": apply_liger_kernel_to_jamba,
 }
 
 

test/convergence/test_mini_models.py

@@ -15,6 +15,7 @@
 from torch.utils.data import DataLoader
 from transformers.models.gemma import GemmaConfig, GemmaForCausalLM
 from transformers.models.gemma2 import Gemma2Config, Gemma2ForCausalLM
+from transformers.models.jamba import JambaConfig, JambaForCausalLM
 from transformers.models.llama import LlamaConfig, LlamaForCausalLM
 from transformers.models.mistral import MistralConfig, MistralForCausalLM
 from transformers.models.mixtral import MixtralConfig, MixtralForCausalLM
@@ -24,6 +25,7 @@
 from liger_kernel.transformers import (
  apply_liger_kernel_to_gemma,
  apply_liger_kernel_to_gemma2,
+ apply_liger_kernel_to_jamba,
  apply_liger_kernel_to_llama,
  apply_liger_kernel_to_mistral,
  apply_liger_kernel_to_mixtral,
@@ -279,6 +281,31 @@
  attn_implementation="eager",
  ),
  ),
+ "mini_jamba": MiniModelConfig(
+ liger_kernel_patch_func=functools.partial(
+ apply_liger_kernel_to_jamba, fused_linear_cross_entropy=False
+ ),
+ model_class=JambaForCausalLM,
+ mini_model_config=JambaConfig(
+ attention_dropout=0.0,
+ num_experts_per_tok=1,
+ num_experts=2,
+ bos_token_id=1,
+ eos_token_id=2, # 32000
+ hidden_act="silu",
+ hidden_size=1024, # 3072
+ initializer_range=0.02,
+ intermediate_size=2048, # 8192
+ max_position_embeddings=32768,
+ num_attention_heads=8, # 32
+ num_hidden_layers=4, # 32
+ rms_norm_eps=1e-5,
+ sliding_window=None,
+ tie_word_embeddings=False,
+ use_cache=True,
+ vocab_size=32064,
+ ),
+ ),
 }
 
 
@@ -316,6 +343,8 @@ def run_mini_model(
  kwargs["geglu"] = True
  else:
  kwargs["swiglu"] = True
+ if model_name == "mini_jamba":
+ del kwargs["rope"]
  MINI_MODEL_SETUPS[model_name].liger_kernel_patch_func(**kwargs)
 
  model = create_model(model_name).to(dtype).to("cuda")
@@ -328,7 +357,6 @@ def run_mini_model(
  optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
 
  loss_list = []
-
  for i in range(num_steps):
  batch = next(loader_iter).to(model.device)
  optimizer.zero_grad()
@@ -461,6 +489,8 @@ def run_mini_model(
  not supports_bfloat16(), reason="bfloat16 not supported on this GPU"
  ),
  ),
+ # To run this test, you need to first run `pip install . '[test]'`
+ # ("mini_jamba", 32, 1e-4, torch.float32, 1e-8, 1e-5, 5e-3, 1e-5, 5e-3, 1e-5),
  ],
 )
 def test_mini_model(

test/convergence/test_mini_models_no_logits.py

@@ -11,6 +11,7 @@
 import torch
 from datasets import load_from_disk
 from torch.utils.data import DataLoader
+from transformers import JambaConfig, JambaForCausalLM
 from transformers.models.gemma import GemmaConfig, GemmaForCausalLM
 from transformers.models.gemma2 import Gemma2Config, Gemma2ForCausalLM
 from transformers.models.llama import LlamaConfig, LlamaForCausalLM
@@ -22,6 +23,7 @@
 from liger_kernel.transformers import (
  apply_liger_kernel_to_gemma,
  apply_liger_kernel_to_gemma2,
+ apply_liger_kernel_to_jamba,
  apply_liger_kernel_to_llama,
  apply_liger_kernel_to_mistral,
  apply_liger_kernel_to_mixtral,
@@ -259,6 +261,29 @@
  attention_dropout=0.0,
  ),
  ),
+ "mini_jamba": MiniModelConfig(
+ liger_kernel_patch_func=apply_liger_kernel_to_jamba,
+ model_class=JambaForCausalLM,
+ mini_model_config=JambaConfig(
+ attention_dropout=0.0,
+ num_experts_per_tok=1,
+ num_experts=2,
+ bos_token_id=1,
+ eos_token_id=2, # 32000
+ hidden_act="silu",
+ hidden_size=1024, # 3072
+ initializer_range=0.02,
+ intermediate_size=2048, # 8192
+ max_position_embeddings=32768,
+ num_attention_heads=8, # 32
+ num_hidden_layers=4, # 32
+ rms_norm_eps=1e-5,
+ sliding_window=None,
+ tie_word_embeddings=False,
+ use_cache=True,
+ vocab_size=32064,
+ ),
+ ),
 }
 
 if QWEN2_VL_AVAILABLE:
@@ -346,7 +371,8 @@ def run_mini_model(
  kwargs["geglu"] = True
  else:
  kwargs["swiglu"] = True
-
+ if model_name == "mini_jamba":
+ del kwargs["rope"]
  model_support_flce = "gemma2" not in model_name
  if model_support_flce:
  kwargs["fused_linear_cross_entropy"] = True
@@ -547,6 +573,8 @@ def run_mini_model(
  not supports_bfloat16(), reason="bfloat16 not supported on this GPU"
  ),
  ),
+ # To run this test, you need to first run `pip install . '[test]'`
+ # ("mini_jamba", 32, 1e-4, torch.float32, 5e-4, 1e-4, 5e-3, 1e-5, 1e-2, 1e-5),
  ],
 )
 def test_mini_model(