Merge branch 'main' into fix/ic/ddp_recipe_load

README.md

@@ -128,7 +128,7 @@ More information on installation such as optional dependencies and requirements
 
 ### Recipes
 
-To enable flexibility, ease of use, and repeatability, SparseML uses a declarative interface called `recipes` for specifying the sparsity-related algorithms and hyperparamters that should be applied by SparseML.
+To enable flexibility, ease of use, and repeatability, SparseML uses a declarative interface called `recipes` for specifying the sparsity-related algorithms and hyperparameters that should be applied by SparseML.
 
 `Recipes` are YAML-files formatted as a list of `modifiers`, which encode the instructions for SparseML. Example `modifiers` can be anything from setting the learning rate to encoding the hyperparameters of the gradual magnitude pruning algorithm. The SparseML system parses the `recipes` into a native format for each framework and applies the modifications to the model and training pipeline.
 

src/sparseml/exporters/transforms/kv_cache/configs.py

@@ -138,6 +138,21 @@ class Config:
     multiply_batch_by_num_att_heads=False,
 )
 
+# Mistral has a config/model definition "MistralForCausalLM" but is based off Llama2.
+# It contains these additions to Llama2-7b:
+# * Sliding Window Attention
+# * GQA (Grouped Query Attention)
+# * Byte-fallback BPE tokenizer
+MISTRAL_CONFIG = KeyValueCacheConfig(
+    model_name="mistral",
+    additional_transforms=AdditionalTransformsLLAMA,
+    key_num_attention_heads="num_attention_heads",
+    key_num_embedding_hidden_size="hidden_size",
+    transpose_value_input=None,
+    transpose_key_input=None,
+    multiply_batch_by_num_att_heads=False,
+)
+
 # Reusing the CodeGen transforms because it happens to match what we need for GPTNeo
 additional_transforms_gpt_neo = AdditionalTransformsCodeGen
 
@@ -160,6 +175,7 @@ def get_kv_cache_config(
         BLOOM_CONFIG,
         MPT_CONFIG,
         LLAMA_CONFIG,
+        MISTRAL_CONFIG,
         GPT_NEO_CONFIG,
     ],
 ) -> KeyValueCacheConfig: