[Minor] Support MistralModel and MistralForCausalLM

pyvene/models/intervenable_modelcard.py

@@ -1,12 +1,12 @@
 from .constants import *
 from .llama.modelings_intervenable_llama import *
+from .mistral.modellings_intervenable_mistral import *
 from .gpt2.modelings_intervenable_gpt2 import *
 from .gpt_neo.modelings_intervenable_gpt_neo import *
 from .gpt_neox.modelings_intervenable_gpt_neox import *
 from .mlp.modelings_intervenable_mlp import *
 from .gru.modelings_intervenable_gru import *
 from .blip.modelings_intervenable_blip import *
-from .blip.modelings_intervenable_blip_itm import *
 from .backpack_gpt2.modelings_intervenable_backpack_gpt2 import *
 
 
@@ -21,7 +21,6 @@
 
 import transformers.models as hf_models
 from .blip.modelings_blip import BlipWrapper
-from .blip.modelings_blip_itm import BlipITMWrapper
 from .mlp.modelings_mlp import MLPModel, MLPForClassification
 from .gru.modelings_gru import GRUModel, GRULMHeadModel, GRUForClassification
 from .backpack_gpt2.modelings_backpack_gpt2 import BackpackGPT2LMHeadModel
@@ -35,17 +34,16 @@
 type_to_module_mapping = {
     hf_models.gpt2.modeling_gpt2.GPT2Model: gpt2_type_to_module_mapping,
     hf_models.gpt2.modeling_gpt2.GPT2LMHeadModel: gpt2_lm_type_to_module_mapping,
-    hf_models.gpt2.modeling_gpt2.GPT2ForSequenceClassification: gpt2_classifier_type_to_module_mapping,
     hf_models.llama.modeling_llama.LlamaModel: llama_type_to_module_mapping,
     hf_models.llama.modeling_llama.LlamaForCausalLM: llama_lm_type_to_module_mapping,
     hf_models.gpt_neo.modeling_gpt_neo.GPTNeoModel: gpt_neo_type_to_module_mapping,
     hf_models.gpt_neo.modeling_gpt_neo.GPTNeoForCausalLM: gpt_neo_lm_type_to_module_mapping,
     hf_models.gpt_neox.modeling_gpt_neox.GPTNeoXModel: gpt_neox_type_to_module_mapping,
     hf_models.gpt_neox.modeling_gpt_neox.GPTNeoXForCausalLM: gpt_neox_lm_type_to_module_mapping,
+    hf_models.mistral.modeling_mistral.MistralModel: mistral_type_to_module_mapping,
+    hf_models.mistral.modeling_mistral.MistralForCausalLM: mistral_lm_type_to_module_mapping,
     hf_models.blip.modeling_blip.BlipForQuestionAnswering: blip_type_to_module_mapping,
-    hf_models.blip.modeling_blip.BlipForImageTextRetrieval: blip_itm_type_to_module_mapping,
     BlipWrapper: blip_wrapper_type_to_module_mapping,
-    BlipITMWrapper: blip_itm_wrapper_type_to_module_mapping,
     MLPModel: mlp_type_to_module_mapping,
     MLPForClassification: mlp_classifier_type_to_module_mapping,
     GRUModel: gru_type_to_module_mapping,
@@ -59,17 +57,16 @@
 type_to_dimension_mapping = {
     hf_models.gpt2.modeling_gpt2.GPT2Model: gpt2_type_to_dimension_mapping,
     hf_models.gpt2.modeling_gpt2.GPT2LMHeadModel: gpt2_lm_type_to_dimension_mapping,
-    hf_models.gpt2.modeling_gpt2.GPT2ForSequenceClassification: gpt2_classifier_type_to_dimension_mapping,
     hf_models.llama.modeling_llama.LlamaModel: llama_type_to_dimension_mapping,
     hf_models.llama.modeling_llama.LlamaForCausalLM: llama_lm_type_to_dimension_mapping,
     hf_models.gpt_neo.modeling_gpt_neo.GPTNeoModel: gpt_neo_type_to_dimension_mapping,
     hf_models.gpt_neo.modeling_gpt_neo.GPTNeoForCausalLM: gpt_neo_lm_type_to_dimension_mapping,
     hf_models.gpt_neox.modeling_gpt_neox.GPTNeoXModel: gpt_neox_type_to_dimension_mapping,
     hf_models.gpt_neox.modeling_gpt_neox.GPTNeoXForCausalLM: gpt_neox_lm_type_to_dimension_mapping,
+    hf_models.mistral.modeling_mistral.MistralModel: mistral_type_to_dimension_mapping,
+    hf_models.mistral.modeling_mistral.MistralForCausalLM: mistral_lm_type_to_dimension_mapping,
     hf_models.blip.modeling_blip.BlipForQuestionAnswering: blip_type_to_dimension_mapping,
-    hf_models.blip.modeling_blip.BlipForImageTextRetrieval: blip_itm_type_to_dimension_mapping,
     BlipWrapper: blip_wrapper_type_to_dimension_mapping,
-    BlipITMWrapper: blip_itm_wrapper_type_to_dimension_mapping,
     MLPModel: mlp_type_to_dimension_mapping,
     MLPForClassification: mlp_classifier_type_to_dimension_mapping,
     GRUModel: gru_type_to_dimension_mapping,

pyvene/models/mistral/modellings_intervenable_mistral.py

@@ -0,0 +1,78 @@
+"""
+Each modeling file in this library is a mapping between
+abstract naming of intervention anchor points and actual
+model module defined in the huggingface library.
+
+We also want to let the intervention library know how to
+config the dimensions of intervention based on model config
+defined in the huggingface library.
+"""
+
+
+import torch
+from ..constants import *
+
+
+mistral_type_to_module_mapping = {
+    "block_input": ("layers[%s]", CONST_INPUT_HOOK),
+    "block_output": ("layers[%s]", CONST_OUTPUT_HOOK),
+    "mlp_activation": ("layers[%s].mlp.act_fn", CONST_OUTPUT_HOOK),
+    "mlp_output": ("layers[%s].mlp", CONST_OUTPUT_HOOK),
+    "mlp_input": ("layers[%s].mlp", CONST_INPUT_HOOK),
+    "attention_value_output": ("layers[%s].self_attn.o_proj", CONST_INPUT_HOOK),
+    "head_attention_value_output": ("layers[%s].self_attn.o_proj", CONST_INPUT_HOOK),
+    "attention_output": ("layers[%s].self_attn", CONST_OUTPUT_HOOK),
+    "attention_input": ("layers[%s].self_attn", CONST_INPUT_HOOK),
+    "query_output": ("layers[%s].self_attn.q_proj", CONST_OUTPUT_HOOK),
+    "key_output": ("layers[%s].self_attn.k_proj", CONST_OUTPUT_HOOK),
+    "value_output": ("layers[%s].self_attn.v_proj", CONST_OUTPUT_HOOK),
+    "head_query_output": ("layers[%s].self_attn.q_proj", CONST_OUTPUT_HOOK),
+    "head_key_output": ("layers[%s].self_attn.k_proj", CONST_OUTPUT_HOOK),
+    "head_value_output": ("layers[%s].self_attn.v_proj", CONST_OUTPUT_HOOK),
+}
+
+
+mistral_type_to_dimension_mapping = {
+    "block_input": ("hidden_size",),
+    "block_output": ("hidden_size",),
+    "mlp_activation": ("intermediate_size",),
+    "mlp_output": ("hidden_size",),
+    "mlp_input": ("hidden_size",),
+    "attention_value_output": ("hidden_size",),
+    "head_attention_value_output": ("hidden_size/num_attention_heads",),
+    "attention_output": ("hidden_size",),
+    "attention_input": ("hidden_size",),
+    "query_output": ("hidden_size",),
+    "key_output": ("hidden_size",),
+    "value_output": ("hidden_size",),
+    "head_query_output": ("hidden_size/num_attention_heads",),
+    "head_key_output": ("hidden_size/num_attention_heads",),
+    "head_value_output": ("hidden_size/num_attention_heads",),
+}
+
+
+"""llama model with LM head"""
+mistral_lm_type_to_module_mapping = {}
+for k, v in mistral_type_to_module_mapping.items():
+    mistral_lm_type_to_module_mapping[k] = (f"model.{v[0]}", v[1])
+
+
+mistral_lm_type_to_dimension_mapping = mistral_type_to_dimension_mapping
+
+
+def create_mistral(
+    name="mistralai/Mistral-7B-v0.1", cache_dir=None
+):
+    """Creates a Mistral Causal LM model, config, and tokenizer from the given name and revision"""
+    from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
+
+    config = AutoConfig.from_pretrained(name, cache_dir=cache_dir)
+    tokenizer = AutoTokenizer.from_pretrained(name, cache_dir=cache_dir)
+    llama = AutoModelForCausalLM.from_pretrained(
+        name,
+        config=config,
+        cache_dir=cache_dir,
+        torch_dtype=torch.bfloat16,  # save memory
+    )
+    print("loaded model")
+    return config, tokenizer, llama