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2 Commits
transforme
...
fix/cce-li
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4581d6a8de | ||
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1a85fab2ca |
@@ -68,7 +68,7 @@ def run_cmd(cmd: str, run_folder: str):
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@app.function(
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@app.function(
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image=cicd_image,
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image=cicd_image,
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gpu=GPU_CONFIG,
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gpu=GPU_CONFIG,
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timeout=90 * 60,
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timeout=60 * 60,
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cpu=8.0,
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cpu=8.0,
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memory=131072 * N_GPUS,
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memory=131072 * N_GPUS,
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volumes=VOLUME_CONFIG,
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volumes=VOLUME_CONFIG,
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@@ -12,7 +12,7 @@ liger-kernel==0.5.6
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packaging==23.2
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packaging==23.2
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peft==0.15.1
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peft==0.15.1
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transformers==4.51.1
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transformers==4.51.0
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tokenizers>=0.21.1
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tokenizers>=0.21.1
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accelerate==1.6.0
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accelerate==1.6.0
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datasets==3.5.0
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datasets==3.5.0
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@@ -26,6 +26,7 @@ from transformers.utils import (
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)
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)
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_PATCH_OPTS: PatchOptions | None = None
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_PATCH_OPTS: PatchOptions | None = None
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RESET_LM_HEAD = True
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@add_start_docstrings_to_model_forward(LLAMA4_INPUTS_DOCSTRING)
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@add_start_docstrings_to_model_forward(LLAMA4_INPUTS_DOCSTRING)
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@@ -308,7 +309,16 @@ def cce_forward_multimodal(
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if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
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if _PATCH_OPTS is not None and _PATCH_OPTS.use_lce(labels, self.training):
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assert labels is not None
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assert labels is not None
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# TODO: check if need to handle attention_mask
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# reset lm head gradient on first pass.
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# linear model has some lm_head weight issue
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# see https://github.com/axolotl-ai-cloud/axolotl/pull/2505
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global RESET_LM_HEAD # pylint: disable=global-statement
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if RESET_LM_HEAD:
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RESET_LM_HEAD = False
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self.language_model.lm_head.weight.requires_grad_(False) # Detach
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self.language_model.lm_head.weight.requires_grad_(True) # Reattach
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loss = apply_lce(
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loss = apply_lce(
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hidden_states,
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hidden_states,
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self.language_model.lm_head.weight,
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self.language_model.lm_head.weight,
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@@ -373,11 +383,7 @@ def patch_llama4_text(
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return maybe_model
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return maybe_model
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setattr(
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modeling_llama4.Llama4ForCausalLM.forward = cce_forward
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modeling_llama4.Llama4ForCausalLM,
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"forward",
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cce_forward,
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)
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return None
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return None
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@@ -403,12 +409,8 @@ def patch_llama4(
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)
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)
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return maybe_model
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return maybe_model
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setattr(
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modeling_llama4.Llama4ForConditionalGeneration.forward = cce_forward_multimodal
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modeling_llama4.Llama4ForConditionalGeneration,
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"forward",
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cce_forward_multimodal,
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)
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# patch the causal language model
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# patch the causal language model
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setattr(modeling_llama4.Llama4ForCausalLM, "forward", cce_forward)
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modeling_llama4.Llama4ForCausalLM.forward = cce_forward
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return None
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return None
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