Support all weight types in a single binary.

This changes the command line flags, but the default value retains the previous behavior.

Also add a CreateGemma helper to enable extra args without interface changes.

PiperOrigin-RevId: 641266411

BUILD.bazel

@@ -116,6 +116,7 @@ cc_library(
         "gemma/cross_entropy.h",
     ],
     deps = [
+        ":common",
         ":gemma_lib",
     ],
 )

CMakeLists.txt

@@ -76,17 +76,6 @@ if(NOT CMAKE_BUILD_TYPE)
     set(CMAKE_BUILD_TYPE "Release")
 endif()
 
-# Allowable types for WEIGHT_TYPE:
-# float - slow, not recommended
-# hwy::bfloat16_t - bfloat16 as implemented by https://github.com/google/highway
-# SfpStream - 8-bit switched floating point (recommended)
-# NuqStream - experimental, work-in-progress
-option(WEIGHT_TYPE "Set weight type" "")
-
-if (WEIGHT_TYPE)
-  add_definitions(-DGEMMA_WEIGHT_T=${WEIGHT_TYPE})
-endif()
-
 FetchContent_GetProperties(sentencepiece)
 
 ## Library Target

DEVELOPERS.md

@@ -105,18 +105,12 @@ the resulting file as `--weights` and the desired .sbs name as the
 There are several compile-time flags to be aware of (note these may or may not
 be exposed to the build system):
 
-- `GEMMA_WEIGHT_T` : Sets the level of compression for weights (surfaced as
-  WEIGHT_TYPE in CMakeLists.txt). Currently this should be set to `SfpStream`
-  (default, if no flag is specified) for 8-bit SFP, or `hwy::bfloat16_t` to
-  enable for higher-fidelity (but slower) bfloat16 support. This is defined in
-  `gemma.h`.
 - `GEMMA_MAX_SEQ_LEN` : Sets maximum sequence length to preallocate for the KV
   Cache. The default is 4096 tokens but can be overridden. This is not exposed
   through `CMakeLists.txt` yet.
 
-In the medium term both of these will likely be deprecated in favor of handling
+In the medium term this will likely be deprecated in favor of handling options
-options at runtime - allowing for multiple weight compression schemes in a single
+at runtime - dynamically resizing the KV cache as needed.
-build and dynamically resizes the KV cache as needed.
 
 ## Using gemma.cpp as a Library (Advanced)
 

README.md

@@ -138,33 +138,16 @@ convenient directory location (e.g. the `build/` directory in this repo).
 The build system uses [CMake](https://cmake.org/). To build the gemma inference
 runtime, create a build directory and generate the build files using `cmake`
 from the top-level project directory. Note if you previous ran `cmake` and are
-re-running with a different setting, be sure to clean out the `build/` directory
+re-running with a different setting, be sure to delete all files in the `build/`
-with `rm -rf build/*` (warning this will delete any other files in the `build/`
+directory with `rm -rf build/*`.
-directory.
-
-For the 8-bit switched floating point weights (sfp), run cmake with no options:
 
 #### Unix-like Platforms
 ```sh
 cmake -B build
 ```
 
-**or** if you downloaded bfloat16 weights (any model *without* `-sfp` in the
+After running `cmake`, you can enter the `build/` directory and run `make` to
-name), instead of running cmake with no options as above, run cmake with
+build the `./gemma` executable:
-WEIGHT_TYPE set to [highway's](https://github.com/google/highway)
-`hwy::bfloat16_t` type. Alternatively, you can also add
-`-DGEMMA_WEIGHT_T=hwy::bfloat16_t` to the C++ compiler flags.
-
-We intend to soon support all weight types without requiring extra flags. Note
-that we recommend using `-sfp` weights instead of bfloat16 for faster inference.
-
-```sh
-cmake -B build -DWEIGHT_TYPE=hwy::bfloat16_t
-```
-
-After running whichever of the above `cmake` invocations that is appropriate for
-your weights, you can enter the `build/` directory and run `make` to build the
-`./gemma` executable:
 
 ```sh
 # Configure `build` directory
@@ -221,11 +204,12 @@ You can now run `gemma` from inside the `build/` directory.
 
 `gemma` has the following required arguments:
 
-| Argument      | Description                  | Example value              |
+Argument        | Description                  | Example value
-| ------------- | ---------------------------- | -------------------------- |
+--------------- | ---------------------------- | -----------------------
-| `--model`     | The model type.              | `2b-it`, `2b-pt`, `7b-it`, `7b-pt`, ... (see above) |
+`--model`       | The model type.              | `2b-it` ... (see below)
-| `--weights`   | The compressed weights file. | `2b-it-sfp.sbs`, ... (see above) |
+`--weights`     | The compressed weights file. | `2b-it-sfp.sbs`
-| `--tokenizer` | The tokenizer file.          | `tokenizer.spm`            |
+`--weight_type` | The compressed weight type.  | `sfp`
+`--tokenizer`   | The tokenizer file.          | `tokenizer.spm`
 
 `gemma` is invoked as:
 
@@ -233,6 +217,7 @@ You can now run `gemma` from inside the `build/` directory.
 ./gemma \
 --tokenizer [tokenizer file] \
 --weights [compressed weights file] \
+--weight_type [f32 or bf16 or sfp] \
 --model [2b-it or 2b-pt or 7b-it or 7b-pt or ...]
 ```
 
@@ -245,8 +230,7 @@ Example invocation for the following configuration:
 ```sh
 ./gemma \
 --tokenizer tokenizer.spm \
---weights 2b-it-sfp.sbs \
+--weights 2b-it-sfp.sbs --weight_type sfp --model 2b-it
---model 2b-it
 ```
 
 ### RecurrentGemma
@@ -270,14 +254,12 @@ Step 1, and run the binary as follows:
 
 **Running `./gemma` fails with "Failed to read cache gating_ein_0 (error 294) ..."**
 
-The most common problem is that `cmake` was built with the wrong weight type and
+The most common problem is that the `--weight_type` argument does not match that
-`gemma` is attempting to load `bfloat16` weights (`2b-it`, `2b-pt`, `7b-it`,
+of the model file. Revisit step #3 and check which weights you downloaded.
-`7b-pt`) using the default switched floating point (sfp) or vice versa. Revisit
-step #3 and check that the `cmake` command used to build `gemma` was correct for
-the weights that you downloaded.
 
-In the future we will handle model format handling from compile time to runtime
+Note that we have already moved weight type from a compile-time decision to a
-to simplify this.
+runtime argument. In a subsequent step, we plan to bake this information into
+the weights.
 
 **Problems building in Windows / Visual Studio**
 

backprop/backward-inl.h

@@ -21,7 +21,6 @@
 #define THIRD_PARTY_GEMMA_CPP_GEMMA_BACKWARD_INL_H_
 
 #include <stddef.h>
-#include <stdint.h>
 
 #include <array>
 #include <cmath>
@@ -44,6 +43,7 @@
 #endif
 
 #include "gemma/ops.h"
+#include "hwy/highway.h"
 
 HWY_BEFORE_NAMESPACE();
 namespace gcpp {

backprop/backward.cc

@@ -15,6 +15,11 @@
 
 #include "backprop/backward.h"
 
+#include "backprop/prompt.h"
+#include "gemma/activations.h"
+#include "gemma/common.h"
+#include "hwy/contrib/thread_pool/thread_pool.h"
+
 // Compiles this file for multiple architectures via "foreach_target.h", to
 // which we pass the filename via macro 'argument'.
 #undef HWY_TARGET_INCLUDE
@@ -29,7 +34,6 @@
 HWY_BEFORE_NAMESPACE();
 namespace gcpp {
 namespace HWY_NAMESPACE {
-namespace hn = hwy::HWY_NAMESPACE;
 
 template <typename TConfig>
 void CrossEntropyLossBackwardPass(const Prompt& prompt,
@@ -57,11 +61,11 @@ void CrossEntropyLossBackwardPassT(Model model,
   // TODO(janwas): use CallFunctorForModel
   switch (model) {
     case Model::GEMMA_2B:
-      CrossEntropyLossBackwardPass<ConfigGemma2B>(
+      CrossEntropyLossBackwardPass<ConfigGemma2B<float>>(
           prompt, weights, forward, grad, backward, pool);
       break;
     case Model::GEMMA_TINY:
-      CrossEntropyLossBackwardPass<ConfigGemmaTiny>(
+      CrossEntropyLossBackwardPass<ConfigGemmaTiny<float>>(
           prompt, weights, forward, grad, backward, pool);
       break;
     default:

backprop/forward.cc

@@ -15,6 +15,11 @@
 
 #include "backprop/forward.h"
 
+#include "backprop/prompt.h"
+#include "gemma/activations.h"
+#include "gemma/common.h"
+#include "hwy/contrib/thread_pool/thread_pool.h"
+
 // Compiles this file for multiple architectures via "foreach_target.h", to
 // which we pass the filename via macro 'argument'.
 #undef HWY_TARGET_INCLUDE
@@ -29,7 +34,6 @@
 HWY_BEFORE_NAMESPACE();
 namespace gcpp {
 namespace HWY_NAMESPACE {
-namespace hn = hwy::HWY_NAMESPACE;
 
 template <typename TConfig>
 float CrossEntropyLossForwardPass(const Prompt& prompt,
@@ -51,10 +55,10 @@ float CrossEntropyLossForwardPassT(Model model, const Prompt& prompt,
   // TODO(janwas): use CallFunctorForModel
   switch (model) {
     case Model::GEMMA_2B:
-      return CrossEntropyLossForwardPass<ConfigGemma2B>(
+      return CrossEntropyLossForwardPass<ConfigGemma2B<float>>(prompt, weights,
-          prompt, weights, forward, pool);
+                                                               forward, pool);
     case Model::GEMMA_TINY:
-      return CrossEntropyLossForwardPass<ConfigGemmaTiny>(
+      return CrossEntropyLossForwardPass<ConfigGemmaTiny<float>>(
           prompt, weights, forward, pool);
     default:
       HWY_ABORT("Model type %d unknown.", static_cast<int>(model));

backprop/optimize_test.cc

@@ -13,18 +13,23 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <iostream>
+#include <stddef.h>
-#include <string>
 
+#include <limits>
+#include <random>
+#include <vector>
+
+#include "gtest/gtest.h"
 #include "backprop/backward.h"
 #include "backprop/forward.h"
 #include "backprop/optimizer.h"
+#include "backprop/prompt.h"
 #include "backprop/sampler.h"
 #include "gemma/activations.h"
 #include "gemma/common.h"
 #include "gemma/gemma.h"
 #include "gemma/weights.h"
-#include "gtest/gtest.h"
+#include "hwy/contrib/thread_pool/thread_pool.h"
 
 namespace gcpp {
 
@@ -35,11 +40,17 @@ TEST(OptimizeTest, GradientDescent) {
   std::mt19937 gen(42);
 
   Model model_type = Model::GEMMA_TINY;
-  ByteStorageT grad = CallFunctorForModel<AllocateWeightsF>(model_type, pool);
+  Type weight_type = Type::kF32;
-  ByteStorageT grad_m = CallFunctorForModel<AllocateWeightsF>(model_type, pool);
+  ByteStorageT grad =
-  ByteStorageT grad_v = CallFunctorForModel<AllocateWeightsF>(model_type, pool);
+      CallForModelAndWeight<AllocateWeightsF>(model_type, weight_type, pool);
-  ByteStorageT forward = CallFunctorForModel<AllocateForwardPass>(model_type);
+  ByteStorageT grad_m =
-  ByteStorageT backward = CallFunctorForModel<AllocateForwardPass>(model_type);
+      CallForModelAndWeight<AllocateWeightsF>(model_type, weight_type, pool);
+  ByteStorageT grad_v =
+      CallForModelAndWeight<AllocateWeightsF>(model_type, weight_type, pool);
+  ByteStorageT forward =
+      CallForModelAndWeight<AllocateForwardPass>(model_type, weight_type);
+  ByteStorageT backward =
+      CallForModelAndWeight<AllocateForwardPass>(model_type, weight_type);
   KVCache kv_cache = KVCache::Create(model_type);
   size_t max_tokens = 32;
   size_t max_generated_tokens = 16;
@@ -47,7 +58,7 @@ TEST(OptimizeTest, GradientDescent) {
   int verbosity = 0;
   const auto accept_token = [](int) { return true; };
 
-  Gemma gemma(GemmaTokenizer(), model_type, pool);
+  Gemma gemma(GemmaTokenizer(), model_type, weight_type, pool);
 
   const auto generate = [&](const std::vector<int>& prompt) {
     std::vector<int> reply;
@@ -76,12 +87,14 @@ TEST(OptimizeTest, GradientDescent) {
     return ok;
   };
 
-  RandInitWeights(model_type, gemma.Weights(), pool, gen);
+  RandInitWeights(model_type, weight_type, gemma.Weights(), pool, gen);
-  CallFunctorForModel<ZeroInitWeightsF>(model_type, grad_m, pool);
+  CallForModelAndWeight<ZeroInitWeightsF>(model_type, weight_type, grad_m,
-  CallFunctorForModel<ZeroInitWeightsF>(model_type, grad_v, pool);
+                                          pool);
+  CallForModelAndWeight<ZeroInitWeightsF>(model_type, weight_type, grad_v,
+                                          pool);
 
   printf("Initial weights:\n");
-  LogWeightStats(model_type, gemma.Weights());
+  LogWeightStats(model_type, weight_type, gemma.Weights());
 
   constexpr size_t kBatchSize = 8;
   const float alpha = 0.001f;
@@ -96,7 +109,8 @@ TEST(OptimizeTest, GradientDescent) {
   size_t num_ok;
   for (; steps < 1000000; ++steps) {
     std::mt19937 sgen(42);
-    CallFunctorForModel<ZeroInitWeightsF>(model_type, grad, pool);
+    CallForModelAndWeight<ZeroInitWeightsF>(model_type, weight_type, grad,
+                                            pool);
     float total_loss = 0.0f;
     num_ok = 0;
     for (size_t i = 0; i < kBatchSize; ++i) {
@@ -109,13 +123,13 @@ TEST(OptimizeTest, GradientDescent) {
     }
     total_loss /= kBatchSize;
 
-    AdamUpdate(model_type, grad, alpha, beta1, beta2, epsilon, steps + 1,
+    AdamUpdate(model_type, weight_type, grad, alpha, beta1, beta2, epsilon,
-               gemma.Weights(), grad_m, grad_v, pool);
+               steps + 1, gemma.Weights(), grad_m, grad_v, pool);
     printf("step: %zu  total_loss: %.15f   num_ok: %zu/%zu\n",
            steps, total_loss, num_ok, kBatchSize);
     if (steps % 100 == 0) {
       printf("Batch gradient:\n");
-      LogWeightStats(model_type, grad);
+      LogWeightStats(model_type, weight_type, grad);
     }
     if (total_loss < 0.5f) {
       break;
@@ -124,7 +138,7 @@ TEST(OptimizeTest, GradientDescent) {
   }
   printf("Num steps: %zu\n", steps);
   printf("Final weights:\n");
-  LogWeightStats(model_type, gemma.Weights());
+  LogWeightStats(model_type, weight_type, gemma.Weights());
   EXPECT_LT(steps, 200);
   EXPECT_EQ(num_ok, kBatchSize);
 }

backprop/optimizer.cc

@@ -107,18 +107,20 @@ struct AdamUpdateT {
 
 }  // namespace
 
-void RandInitWeights(Model model, const ByteStorageT& weights,
+void RandInitWeights(Model model_type, Type weight_type,
-                     hwy::ThreadPool& pool,
+                     const ByteStorageT& weights, hwy::ThreadPool& pool,
                      std::mt19937& gen) {
-  CallFunctorForModel<RandInitWeightsT>(model, weights, pool, gen);
+  CallForModelAndWeight<RandInitWeightsT>(model_type, weight_type, weights,
+                                          pool, gen);
 }
 
-void AdamUpdate(Model model, const ByteStorageT& grad, float alpha, float beta1,
+void AdamUpdate(Model model_type, Type weight_type, const ByteStorageT& grad,
-                float beta2, float epsilon, size_t t,
+                float alpha, float beta1, float beta2, float epsilon, size_t t,
                 const ByteStorageT& weights, const ByteStorageT& grad_m,
                 const ByteStorageT& grad_v, hwy::ThreadPool& pool) {
-  CallFunctorForModel<AdamUpdateT>(model, grad, alpha, beta1, beta2, epsilon, t,
+  CallForModelAndWeight<AdamUpdateT>(model_type, weight_type, grad, alpha,
-                                   weights, grad_m, grad_v, pool);
+                                     beta1, beta2, epsilon, t, weights, grad_m,
+                                     grad_v, pool);
 }
 
 }  // namespace gcpp

backprop/optimizer.h

@@ -19,16 +19,16 @@
 #include <random>
 
 #include "gemma/common.h"
-#include "gemma/weights.h"
 #include "hwy/contrib/thread_pool/thread_pool.h"
 
 namespace gcpp {
 
-void RandInitWeights(Model model, const ByteStorageT& weights,
+void RandInitWeights(Model model_type, Type weight_type,
-                     hwy::ThreadPool& pool, std::mt19937& gen);
+                     const ByteStorageT& weights, hwy::ThreadPool& pool,
+                     std::mt19937& gen);
 
-void AdamUpdate(Model model, const ByteStorageT& grad, float alpha, float beta1,
+void AdamUpdate(Model model_type, Type weight_type, const ByteStorageT& grad,
-                float beta2, float epsilon, size_t t,
+                float alpha, float beta1, float beta2, float epsilon, size_t t,
                 const ByteStorageT& weights, const ByteStorageT& grad_m,
                 const ByteStorageT& grad_v, hwy::ThreadPool& pool);
 

debug_prompt.cc

@@ -113,7 +113,7 @@ int main(int argc, char** argv) {
     gcpp::PinWorkersToCores(pool);
   }
 
-  gcpp::Gemma model(loader.tokenizer, loader.weights, loader.ModelType(), pool);
+  gcpp::Gemma model = gcpp::CreateGemma(loader, pool);
   gcpp::KVCache kv_cache = gcpp::KVCache::Create(loader.ModelType());
 
   const std::string& prompt = prompt_args.prompt;

examples/hello_world/run.cc

@@ -39,7 +39,7 @@ int main(int argc, char** argv) {
   hwy::ThreadPool pool(num_threads);
 
   // Instantiate model and KV Cache
-  gcpp::Gemma model(loader.tokenizer, loader.weights, loader.ModelType(), pool);
+  gcpp::Gemma model = gcpp::CreateGemma(loader, pool);
   gcpp::KVCache kv_cache = gcpp::KVCache::Create(loader.ModelType());
   size_t pos = 0;  // KV Cache position
 

gemma/benchmark.cc

@@ -280,7 +280,7 @@ int main(int argc, char** argv) {
     gcpp::PinWorkersToCores(pool);
   }
 
-  gcpp::Gemma model(loader.tokenizer, loader.weights, loader.ModelType(), pool);
+  gcpp::Gemma model = gcpp::CreateGemma(loader, pool);
   gcpp::KVCache kv_cache = gcpp::KVCache::Create(loader.ModelType());
 
   if (!benchmark_args.goldens.path.empty()) {

gemma/common.cc

@@ -64,4 +64,28 @@ const char* ParseModelTypeAndTraining(const std::string& model_flag,
   return kErrorMessageBuffer;
 }
 
+const char* ParseType(const std::string& type_string, Type& type) {
+  constexpr Type kTypes[] = {Type::kF32, Type::kBF16, Type::kSFP};
+  constexpr const char* kStrings[] = {"f32", "bf16", "sfp"};
+  constexpr size_t kNum = std::end(kStrings) - std::begin(kStrings);
+  static char kErrorMessageBuffer[kNum * 8 + 100] =
+      "Invalid or missing type, need to specify one of ";
+  for (size_t i = 0; i + 1 < kNum; i++) {
+    strcat(kErrorMessageBuffer, kStrings[i]);  // NOLINT
+    strcat(kErrorMessageBuffer, ", ");         // NOLINT
+  }
+  strcat(kErrorMessageBuffer, kStrings[kNum - 1]);  // NOLINT
+  strcat(kErrorMessageBuffer, ".");                 // NOLINT
+  std::string type_lc = type_string;
+  std::transform(begin(type_lc), end(type_lc), begin(type_lc),
+                 [](unsigned char c) { return std::tolower(c); });
+  for (size_t i = 0; i < kNum; i++) {
+    if (kStrings[i] == type_lc) {
+      type = kTypes[i];
+      return nullptr;
+    }
+  }
+  return kErrorMessageBuffer;
+}
+
 }  // namespace gcpp

gemma/common.h

@@ -21,6 +21,7 @@
 
 #include <string>
 
+#include "compression/compress.h"
 #include "gemma/configs.h"  // IWYU pragma: export
 #include "hwy/aligned_allocator.h"
 #include "hwy/base.h"  // ConvertScalarTo
@@ -37,56 +38,86 @@ ByteStorageT AllocateSizeof() {
 // Model variants: see configs.h for details.
 enum class Model { GEMMA_2B, GEMMA_7B, GRIFFIN_2B, GEMMA_TINY };
 
+// Instruction-tuned models require extra 'turn structure' tokens in prompts.
 enum class ModelTraining { GEMMA_IT, GEMMA_PT };
 
-// Returns the return value of Func<T>().operator() called with `args`, where
+// Tensor types for loading weights.
-// `T` is selected based on `model`.
+enum class Type { kF32, kBF16, kSFP };
+
+// Returns the return value of FuncT<Config*<TWeight>>().operator()(args), where
+// Config* is selected via `model`. Typically called by CallForModelAndWeight,
+// but can also be called directly when FuncT does not actually use TWeight.
 //
-// This is used to implement type-erased functions such as
+// Note that a T prefix indicates a concrete type template argument, whereas a
-// LoadCompressedWeights, which can be called from other .cc files, by calling a
+// T suffix indicates the argument is itself a template.
-// functor LoadCompressedWeightsT, which has a template argument. `Func` must
-// be a functor because function templates cannot be passed as a template
-// template argument, and we prefer to avoid the overhead of std::function.
 //
-// This function avoids having to update all call sites when we extend `Model`.
+// `FuncT` must be a functor because function templates cannot be passed as a
-template <template <typename Config> class Func, typename... Args>
+// template template argument, and we prefer to avoid the overhead of
-decltype(auto) CallFunctorForModel(Model model, Args&&... args) {
+// std::function.
+template <typename TWeight, template <typename TConfig> class FuncT,
+          typename... TArgs>
+decltype(auto) CallForModel(Model model, TArgs&&... args) {
   switch (model) {
     case Model::GEMMA_TINY:
-      return Func<ConfigGemmaTiny>()(std::forward<Args>(args)...);
+      return FuncT<ConfigGemmaTiny<TWeight>>()(std::forward<TArgs>(args)...);
     case Model::GEMMA_2B:
-      return Func<ConfigGemma2B>()(std::forward<Args>(args)...);
+      return FuncT<ConfigGemma2B<TWeight>>()(std::forward<TArgs>(args)...);
     case Model::GEMMA_7B:
-      return Func<ConfigGemma7B>()(std::forward<Args>(args)...);
+      return FuncT<ConfigGemma7B<TWeight>>()(std::forward<TArgs>(args)...);
     case Model::GRIFFIN_2B:
-      return Func<ConfigGriffin2B>()(std::forward<Args>(args)...);
+      return FuncT<ConfigGriffin2B<TWeight>>()(std::forward<TArgs>(args)...);
     default:
       HWY_ABORT("Model type %d unknown.", static_cast<int>(model));
   }
 }
 
-// Like CallFunctorForModel, but for SIMD function templates. This is a macro
+// Returns the return value of FuncT<TConfig>().operator()(args),
-// because it boils down to N_SSE4::FUNC, which would not work if FUNC was a
+// where `TConfig` is selected based on `model` and `weight`.
-// normal function argument.
+
-#define GEMMA_EXPORT_AND_DISPATCH_MODEL(MODEL, FUNC, ARGS)          \
+// This makes it easy to extend `Model` or `Type` without updating callers.
+//
+// Usage example: LoadWeights is type-erased so that it can be called from other
+// .cc files. It uses this function to call the appropriate instantiation of a
+// template functor LoadCompressedWeightsT<TConfig>.
+template <template <typename TConfig> class FuncT, typename... TArgs>
+decltype(auto) CallForModelAndWeight(Model model, Type weight,
+                                     TArgs&&... args) {
+  switch (weight) {
+    case Type::kF32:
+      return CallForModel<float, FuncT, TArgs...>(  //
+          model, std::forward<TArgs>(args)...);
+    case Type::kBF16:
+      return CallForModel<hwy::bfloat16_t, FuncT, TArgs...>(
+          model, std::forward<TArgs>(args)...);
+    case Type::kSFP:
+      return CallForModel<SfpStream, FuncT, TArgs...>(
+          model, std::forward<TArgs>(args)...);
+    default:
+      HWY_ABORT("Weight type %d unknown.", static_cast<int>(weight));
+  }
+}
+
+// Used by GEMMA_EXPORT_AND_DISPATCH. For a given TWEIGHT (e.g. float),
+// calls FUNC<ConfigT<TWEIGHT>> where ConfigT is chosen via MODEL enum.
+#define GEMMA_DISPATCH_MODEL(MODEL, TWEIGHT, FUNC, ARGS)                \
   switch (MODEL) {                                                      \
     case Model::GEMMA_TINY: {                                           \
-      HWY_EXPORT_AND_DYNAMIC_DISPATCH_T(FUNC<ConfigGemmaTiny>)      \
+      HWY_EXPORT_AND_DYNAMIC_DISPATCH_T(FUNC<ConfigGemmaTiny<TWEIGHT>>) \
       ARGS;                                                             \
       break;                                                            \
     }                                                                   \
     case Model::GEMMA_2B: {                                             \
-      HWY_EXPORT_AND_DYNAMIC_DISPATCH_T(FUNC<ConfigGemma2B>)        \
+      HWY_EXPORT_AND_DYNAMIC_DISPATCH_T(FUNC<ConfigGemma2B<TWEIGHT>>)   \
       ARGS;                                                             \
       break;                                                            \
     }                                                                   \
     case Model::GEMMA_7B: {                                             \
-      HWY_EXPORT_AND_DYNAMIC_DISPATCH_T(FUNC<ConfigGemma7B>)        \
+      HWY_EXPORT_AND_DYNAMIC_DISPATCH_T(FUNC<ConfigGemma7B<TWEIGHT>>)   \
       ARGS;                                                             \
       break;                                                            \
     }                                                                   \
     case Model::GRIFFIN_2B: {                                           \
-      HWY_EXPORT_AND_DYNAMIC_DISPATCH_T(FUNC<ConfigGriffin2B>)      \
+      HWY_EXPORT_AND_DYNAMIC_DISPATCH_T(FUNC<ConfigGriffin2B<TWEIGHT>>) \
       ARGS;                                                             \
       break;                                                            \
     }                                                                   \
@@ -94,10 +125,42 @@ decltype(auto) CallFunctorForModel(Model model, Args&&... args) {
       HWY_ABORT("Model type %d unknown.", static_cast<int>(MODEL));     \
   }
 
+// Like CallForModelAndWeight, but for SIMD function templates. This is a macro
+// because it boils down to N_SSE4::FUNC, which would not work if FUNC was a
+// normal function argument. MODEL and WEIGHT are enums.
+#define GEMMA_EXPORT_AND_DISPATCH(MODEL, WEIGHT, FUNC, ARGS)          \
+  switch (WEIGHT) {                                                   \
+    case Type::kF32:                                                  \
+      GEMMA_DISPATCH_MODEL(MODEL, float, FUNC, ARGS);                 \
+      break;                                                          \
+    case Type::kBF16:                                                 \
+      GEMMA_DISPATCH_MODEL(MODEL, hwy::bfloat16_t, FUNC, ARGS);       \
+      break;                                                          \
+    case Type::kSFP:                                                  \
+      GEMMA_DISPATCH_MODEL(MODEL, SfpStream, FUNC, ARGS);             \
+      break;                                                          \
+    default:                                                          \
+      HWY_ABORT("Weight type %d unknown.", static_cast<int>(WEIGHT)); \
+  }
+
 // Returns error string or nullptr if OK.
 // Thread-hostile.
 const char* ParseModelTypeAndTraining(const std::string& model_flag,
                                       Model& model, ModelTraining& training);
+const char* ParseType(const std::string& type_string, Type& type);
+
+static inline const char* StringFromType(Type type) {
+  switch (type) {
+    case Type::kF32:
+      return "f32";
+    case Type::kBF16:
+      return "bf16";
+    case Type::kSFP:
+      return "sfp";
+    default:
+      return "?";
+  }
+}
 
 // __builtin_sqrt is not constexpr as of Clang 17.
 #if HWY_COMPILER_GCC_ACTUAL

gemma/compress_weights.cc

@@ -62,14 +62,16 @@ struct Args : public ArgsBase<Args> {
     ChooseNumThreads();
   }
 
-  gcpp::Model ModelType() const { return model_type; }
-
   // Returns error string or nullptr if OK.
   const char* Validate() {
     ModelTraining model_training;
-    const char* parse_result =
+    if (const char* err = ParseModelTypeAndTraining(model_type_str, model_type_,
-        ParseModelTypeAndTraining(model_type_str, model_type, model_training);
+                                                    model_training)) {
-    if (parse_result) return parse_result;
+      return err;
+    }
+    if (const char* err = ParseType(weight_type_str, weight_type_)) {
+      return err;
+    }
     if (weights.path.empty()) {
       return "Missing --weights flag, a file for the uncompressed model.";
     }
@@ -86,7 +88,7 @@ struct Args : public ArgsBase<Args> {
   Path weights;             // uncompressed weights file location
   Path compressed_weights;  // compressed weights file location
   std::string model_type_str;
-  Model model_type;
+  std::string weight_type_str;
   size_t num_threads;
 
   template <class Visitor>
@@ -101,6 +103,9 @@ struct Args : public ArgsBase<Args> {
             "gr2b-it = griffin 2B parameters, instruction-tuned\n    "
             "gr2b-pt = griffin 2B parameters, pretrained\n    "
             "    Required argument.");
+    visitor(weight_type_str, "weight_type", std::string("sfp"),
+            "Weight type\n    f32 = float, bf16 = bfloat16, SFP = 8-bit FP\n"
+            "    Required argument.");
     visitor(compressed_weights, "compressed_weights", Path(),
             "Path name where compressed weights (.sbs) file will be written.\n"
             "    Required argument.");
@@ -110,6 +115,14 @@ struct Args : public ArgsBase<Args> {
             "number of suupported concurrent threads.",
             2);
   }
+
+  // Uninitialized before Validate, must call after that.
+  gcpp::Model ModelType() const { return model_type_; }
+  gcpp::Type WeightType() const { return weight_type_; }
+
+ private:
+  Model model_type_;
+  Type weight_type_;
 };
 
 void ShowHelp(gcpp::Args& args) {
@@ -132,7 +145,7 @@ namespace HWY_NAMESPACE {
 template <class TConfig>
 void CompressWeights(const Path& weights_path,
                      const Path& compressed_weights_path, Model model_type,
-                     hwy::ThreadPool& pool) {
+                     Type weight_type, hwy::ThreadPool& pool) {
   if (!weights_path.Exists()) {
     HWY_ABORT("The model weights file '%s' does not exist.",
               weights_path.path.c_str());
@@ -147,7 +160,7 @@ void CompressWeights(const Path& weights_path,
   // Get weights, compress, and store.
   const bool scale_for_compression = TConfig::kNumTensorScales > 0;
   const ByteStorageT weights_u8 = gcpp::LoadRawWeights(
-      weights_path, model_type, pool, scale_for_compression);
+      weights_path, model_type, weight_type, pool, scale_for_compression);
   WeightsF<TConfig>* weights =
       reinterpret_cast<WeightsF<TConfig>*>(weights_u8.get());
   Compressor compressor(pool);
@@ -169,9 +182,10 @@ namespace gcpp {
 void Run(Args& args) {
   hwy::ThreadPool pool(args.num_threads);
   const Model model_type = args.ModelType();
-  GEMMA_EXPORT_AND_DISPATCH_MODEL(
+  const Type weight_type = args.WeightType();
-      model_type, CompressWeights,
+  GEMMA_EXPORT_AND_DISPATCH(
-      (args.weights, args.compressed_weights, model_type, pool));
+      model_type, weight_type, CompressWeights,
+      (args.weights, args.compressed_weights, model_type, weight_type, pool));
 }
 
 }  // namespace gcpp

gemma/configs.h

@@ -22,7 +22,6 @@
 
 #include <array>
 
-#include "compression/compress.h"  // SfpStream
 #include "hwy/base.h"                // hwy::bfloat16_t
 
 namespace gcpp {
@@ -42,18 +41,10 @@ namespace gcpp {
 #define GEMMA_MAX_THREADS 128
 #endif  // !GEMMA_MAX_THREADS
 
-// Allowable types for GEMMA_WEIGHT_T (can be specified at compilation time):
-// float, hwy::bfloat16_t, SfpStream, NuqStream
-#ifndef GEMMA_WEIGHT_T
-#define GEMMA_WEIGHT_T SfpStream
-#endif  // !GEMMA_WEIGHT_T
-
 static constexpr size_t kSeqLen = GEMMA_MAX_SEQLEN;
 static constexpr size_t kTopK = GEMMA_TOPK;
 static constexpr size_t kMaxThreads = GEMMA_MAX_THREADS;
 
-using GemmaWeightT = GEMMA_WEIGHT_T;
-
 using EmbedderInputT = hwy::bfloat16_t;
 
 enum class LayerAttentionType {
@@ -82,7 +73,10 @@ constexpr size_t NumLayersOfTypeBefore(
   return count;
 }
 
+template <typename TWeight>
 struct ConfigGemma7B {
+  using Weight = TWeight;  // make accessible where we only have a TConfig
+
   static constexpr int kSeqLen = gcpp::kSeqLen;
   static constexpr int kVocabSize = 256000;
   static constexpr std::array<LayerAttentionType, 28> kLayerConfig =
@@ -111,10 +105,12 @@ struct ConfigGemma7B {
   static constexpr bool kUseLocalAttention = false;
   static constexpr bool kInterleaveQKV = true;
   static constexpr int kNumTensorScales = 0;
-  using WeightT = GEMMA_WEIGHT_T;
 };
 
+template <typename TWeight>
 struct ConfigGemma2B {
+  using Weight = TWeight;  // make accessible where we only have a TConfig
+
   static constexpr int kSeqLen = gcpp::kSeqLen;
   static constexpr int kVocabSize = 256000;
   static constexpr std::array<LayerAttentionType, 18> kLayerConfig =
@@ -143,10 +139,12 @@ struct ConfigGemma2B {
   static constexpr bool kUseLocalAttention = false;
   static constexpr bool kInterleaveQKV = true;
   static constexpr int kNumTensorScales = 0;
-  using WeightT = GEMMA_WEIGHT_T;
 };
 
+template <typename TWeight>
 struct ConfigGemmaTiny {
+  using Weight = TWeight;  // make accessible where we only have a TConfig
+
   static constexpr int kSeqLen = 32;
   static constexpr int kVocabSize = 16;
   static constexpr std::array<LayerAttentionType, 3> kLayerConfig =
@@ -175,10 +173,12 @@ struct ConfigGemmaTiny {
   static constexpr bool kUseLocalAttention = false;
   static constexpr bool kInterleaveQKV = true;
   static constexpr int kNumTensorScales = 0;
-  using WeightT = GEMMA_WEIGHT_T;
 };
 
+template <typename TWeight>
 struct ConfigGriffin2B {
+  using Weight = TWeight;  // make accessible where we only have a TConfig
+
   // Griffin uses local attention, so kSeqLen is actually the local attention
   // window.
   static constexpr int kSeqLen = 2048;
@@ -235,7 +235,6 @@ struct ConfigGriffin2B {
   static constexpr bool kUseLocalAttention = true;
   static constexpr bool kInterleaveQKV = false;
   static constexpr int kNumTensorScales = 140;
-  using WeightT = GEMMA_WEIGHT_T;
 };
 
 }  // namespace gcpp

gemma/cross_entropy.cc

@@ -15,13 +15,20 @@
 
 #include "gemma/cross_entropy.h"
 
+#include <stddef.h>
+#include <stdio.h>
+
 #include <algorithm>
 #include <cmath>
+#include <functional>
 #include <regex>  // NOLINT
 #include <string>
 #include <utility>
 #include <vector>
 
+#include "gemma/common.h"
+#include "gemma/gemma.h"
+
 namespace gcpp {
 
 namespace {
@@ -63,7 +70,9 @@ float ComputeCrossEntropy(Gemma& gemma, size_t max_tokens,
   auto stream_token = [](int, float) { return true; };
   auto accept_token = [](int) { return true; };
 
-  const int vocab_size = CallFunctorForModel<GetVocabSize>(gemma.ModelType());
+  // TWeight is unused, but we have to pass it to Config*.
+  const int vocab_size =
+      CallForModel</*TWeight=*/float, GetVocabSize>(gemma.ModelType());
   float cross_entropy = std::log(vocab_size);  // first token
   size_t pos = 1;
   std::function<int(const float*, size_t)> sample_token =

gemma/cross_entropy.h

@@ -16,6 +16,8 @@
 #ifndef THIRD_PARTY_GEMMA_CPP_GEMMA_CROSS_ENTROPY_H_
 #define THIRD_PARTY_GEMMA_CPP_GEMMA_CROSS_ENTROPY_H_
 
+#include <stddef.h>
+
 #include <vector>
 
 #include "gemma/gemma.h"

gemma/gemma.cc

@@ -147,8 +147,10 @@ struct CreateKVCache {
 
 }  // namespace
 
-KVCache KVCache::Create(Model type) {
+KVCache KVCache::Create(Model model_type) {
-  return CallFunctorForModel<CreateKVCache>(type);
+  // TWeight=float is a placeholder and unused because CreateKVCache does not
+  // use TConfig::Weight.
+  return CallForModel</*TWeight=*/float, CreateKVCache>(model_type);
 }
 
 class GemmaTokenizer::Impl {
@@ -727,7 +729,7 @@ Activations<TConfig, kBatchSize>& GetActivations(const ByteStorageT& state_u8) {
 }  // namespace
 
 template <class TConfig>
-void Generate(const ByteStorageT& weights_u8, const ByteStorageT& prefill_u8,
+void GenerateT(const ByteStorageT& weights_u8, const ByteStorageT& prefill_u8,
                const ByteStorageT& decode_u8,
                const RuntimeConfig& runtime_config,
                const std::vector<int>& prompt, size_t pos, KVCache& kv_cache,
@@ -871,23 +873,31 @@ struct AllocateDecode {
 }  // namespace
 
 Gemma::Gemma(const Path& tokenizer_path, const Path& weights, Model model_type,
-             hwy::ThreadPool& pool)
+             Type weight_type, hwy::ThreadPool& pool)
-    : pool_(pool), tokenizer_(tokenizer_path), model_type_(model_type) {
+    : pool_(pool),
-  weights_u8_ = LoadWeights(weights, model_type, pool);
+      tokenizer_(tokenizer_path),
-  prefill_u8_ = CallFunctorForModel<AllocatePrefill>(model_type);
+      model_type_(model_type),
-  decode_u8_ = CallFunctorForModel<AllocateDecode>(model_type);
+      weight_type_(weight_type) {
+  weights_u8_ = LoadWeights(weights, model_type, weight_type, pool);
+  prefill_u8_ = CallForModelAndWeight<AllocatePrefill>(model_type, weight_type);
+  decode_u8_ = CallForModelAndWeight<AllocateDecode>(model_type, weight_type);
 }
 
-Gemma::Gemma(GemmaTokenizer&& tokenizer, Model model_type,
+Gemma::Gemma(GemmaTokenizer&& tokenizer, Model model_type, Type weight_type,
              hwy::ThreadPool& pool)
-    : pool_(pool), tokenizer_(std::move(tokenizer)), model_type_(model_type) {
+    : pool_(pool),
-  weights_u8_ = CallFunctorForModel<AllocateWeightsF>(model_type, pool);
+      tokenizer_(std::move(tokenizer)),
-  prefill_u8_ = CallFunctorForModel<AllocatePrefill>(model_type);
+      model_type_(model_type),
-  decode_u8_ = CallFunctorForModel<AllocateDecode>(model_type);
+      weight_type_(weight_type) {
+  weights_u8_ =
+      CallForModelAndWeight<AllocateWeightsF>(model_type, weight_type, pool);
+  prefill_u8_ = CallForModelAndWeight<AllocatePrefill>(model_type, weight_type);
+  decode_u8_ = CallForModelAndWeight<AllocateDecode>(model_type, weight_type);
 }
 
 Gemma::~Gemma() {
-  CallFunctorForModel<DeleteLayersPtrs>(model_type_, weights_u8_);
+  CallForModelAndWeight<DeleteLayersPtrs>(model_type_, weight_type_,
+                                          weights_u8_);
 }
 
 void Gemma::Generate(const RuntimeConfig& runtime_config,
@@ -896,8 +906,8 @@ void Gemma::Generate(const RuntimeConfig& runtime_config,
                      LayersOutputT* layers_output) {
   pool_.SetWaitMode(hwy::PoolWaitMode::kSpin);
 
-  GEMMA_EXPORT_AND_DISPATCH_MODEL(
+  GEMMA_EXPORT_AND_DISPATCH(
-      model_type_, Generate,
+      model_type_, weight_type_, GenerateT,
       (weights_u8_, prefill_u8_, decode_u8_, runtime_config, prompt, start_pos,
        kv_cache, pool_, timing_info, layers_output));
 

gemma/gemma.h

@@ -107,10 +107,11 @@ using LayersOutputT =
 class Gemma {
  public:
   Gemma(const Path& tokenizer_path, const Path& weights, Model model_type,
-        hwy::ThreadPool& pool);
+        Type weight_type, hwy::ThreadPool& pool);
 
   // Allocates weights, caller is responsible for filling them.
-  Gemma(GemmaTokenizer&& tokenizer, Model model_type, hwy::ThreadPool& pool);
+  Gemma(GemmaTokenizer&& tokenizer, Model model_type, Type weight_type,
+        hwy::ThreadPool& pool);
   ~Gemma();
 
   Model ModelType() const { return model_type_; }
@@ -136,6 +137,7 @@ class Gemma {
   ByteStorageT prefill_u8_;
   ByteStorageT decode_u8_;
   Model model_type_;
+  Type weight_type_;
 };
 
 // DEPRECATED, call Gemma::Generate directly.

gemma/gemma_test.cc

@@ -38,8 +38,7 @@ class GemmaTest : public ::testing::Test {
       : weights("./2b-it-mqa.sbs"),
         tokenizer("./tokenizer.spm"),
         pool(std::min<int>(20, (std::thread::hardware_concurrency() - 1) / 2)),
-        model_type(gcpp::Model::GEMMA_2B),
+        model(tokenizer, weights, model_type, weight_type, pool) {
-        model(tokenizer, weights, model_type, pool) {
     KVCache kv_cache = KVCache::Create(model_type);
   }
 
@@ -96,7 +95,8 @@ class GemmaTest : public ::testing::Test {
   gcpp::Path tokenizer;
   gcpp::KVCache kv_cache;
   hwy::ThreadPool pool;
-  gcpp::Model model_type = {};
+  gcpp::Model model_type = gcpp::Model::GEMMA_2B;
+  gcpp::Type weight_type = gcpp::Type::kSFP;
   gcpp::Gemma model;
 };
 

gemma/run.cc

@@ -71,7 +71,7 @@ void ShowConfig(LoaderArgs& loader, InferenceArgs& inference, AppArgs& app) {
               << hwy::VectorBytes() * 8 << " bits)" << "\n"
               << "Compiled config               : " << CompiledConfig() << "\n"
               << "Weight Type                   : "
-              << gcpp::TypeName(gcpp::GemmaWeightT()) << "\n"
+              << gcpp::StringFromType(loader.WeightType()) << "\n"
               << "EmbedderInput Type            : "
               << gcpp::TypeName(gcpp::EmbedderInputT()) << "\n";
   }
@@ -251,8 +251,7 @@ void Run(LoaderArgs& loader, InferenceArgs& inference, AppArgs& app) {
     PinWorkersToCores(pool);
   }
 
-  gcpp::Gemma model(loader.tokenizer, loader.weights, loader.ModelType(), pool);
+  gcpp::Gemma model = gcpp::CreateGemma(loader, pool);
-
   KVCache kv_cache = KVCache::Create(loader.ModelType());
 
   if (app.verbosity >= 1) {
@@ -278,7 +277,8 @@ void Run(LoaderArgs& loader, InferenceArgs& inference, AppArgs& app) {
   }
 
   ReplGemma(
-      model, loader.ModelTraining(), kv_cache, pool, inference, app.verbosity,
+      model, loader.ModelTrainingType(), kv_cache, pool, inference,
+      app.verbosity,
       /*accept_token=*/[](int) { return true; }, app.eot_line);
 }
 

gemma/run_mmlu.cc

@@ -157,8 +157,7 @@ void Run(LoaderArgs& loader, InferenceArgs& inference, AppArgs& app) {
     PinWorkersToCores(pool);
   }
 
-  gcpp::Gemma model(loader.tokenizer, loader.weights, loader.ModelType(), pool);
+  gcpp::Gemma model = gcpp::CreateGemma(loader, pool);
-
   gcpp::KVCache kv_cache = gcpp::KVCache::Create(loader.ModelType());
 
   JsonGemma(model, kv_cache, pool, inference, app.verbosity, app.eot_line);

gemma/weights.cc

@@ -173,10 +173,11 @@ struct LoadRawWeightsT {
 #undef SCALE_WEIGHTS
 }  // namespace
 
-ByteStorageT LoadRawWeights(const Path& weights, Model model,
+ByteStorageT LoadRawWeights(const Path& weights, Model model_type,
-                            hwy::ThreadPool& pool, bool scale_for_compression) {
+                            Type weight_type, hwy::ThreadPool& pool,
-  return CallFunctorForModel<LoadRawWeightsT>(model, weights, pool,
+                            bool scale_for_compression) {
-                                              scale_for_compression);
+  return CallForModelAndWeight<LoadRawWeightsT>(
+      model_type, weight_type, weights, pool, scale_for_compression);
 }
 
 namespace {
@@ -227,17 +228,18 @@ struct LoadCompressedWeightsT {
 };
 }  // namespace
 
-ByteStorageT LoadCompressedWeights(const Path& weights, Model model,
+ByteStorageT LoadCompressedWeights(const Path& weights, Model model_type,
-                                   hwy::ThreadPool& pool) {
+                                   Type weight_type, hwy::ThreadPool& pool) {
-  return CallFunctorForModel<LoadCompressedWeightsT>(model, weights, pool);
+  return CallForModelAndWeight<LoadCompressedWeightsT>(model_type, weight_type,
+                                                       weights, pool);
 }
 
-ByteStorageT LoadWeights(const Path& weights, Model model,
+ByteStorageT LoadWeights(const Path& weights, Model model_type,
-                         hwy::ThreadPool& pool) {
+                         Type weight_type, hwy::ThreadPool& pool) {
   if constexpr (kWeightsAreCompressed) {
-    return LoadCompressedWeights(weights, model, pool);
+    return LoadCompressedWeights(weights, model_type, weight_type, pool);
   } else {
-    return LoadRawWeights(weights, model, pool,
+    return LoadRawWeights(weights, model_type, weight_type, pool,
                           /*scale_for_compression=*/false);
   }
 }
@@ -274,8 +276,9 @@ struct LogWeightStatsT {
 };
 }  // namespace
 
-void LogWeightStats(gcpp::Model model, const ByteStorageT& weights) {
+void LogWeightStats(gcpp::Model model_type, Type weight_type,
-  CallFunctorForModel<LogWeightStatsT>(model, weights);
+                    const ByteStorageT& weights) {
+  CallForModelAndWeight<LogWeightStatsT>(model_type, weight_type, weights);
 }
 
 }  // namespace gcpp

gemma/weights.h

@@ -129,21 +129,17 @@ using WeightsF = Weights<float, TConfig>;
 // ----------------------------------------------------------------------------
 // Compressed
 
-// If weights are f32, also f32; otherwise at least bf16. Useful for ops that do
-// not yet support smaller compressed types, or require at least bf16. When
-// weights are f32, we also want such tensors to be f32.
-template <class TConfig>
-using WeightF32OrBF16T =
-    hwy::If<hwy::IsSame<typename TConfig::WeightT, float>(), float,
-            hwy::bfloat16_t>;
-
 template <class TConfig>
 struct CompressedLayer {
   // No ctor/dtor, allocated via AllocateAligned.
 
   using TLayer = gcpp::LayerF<TConfig>;
-  using WeightT = typename TConfig::WeightT;
+  using Weight = typename TConfig::Weight;
-  using WeightF32OrBF16 = WeightF32OrBF16T<TConfig>;
+  // If weights are f32, also f32; otherwise at least bf16. Useful for ops that
+  // do not yet support smaller compressed types, or require at least bf16. When
+  // weights are f32, we also want such tensors to be f32.
+  using WeightF32OrBF16 =
+      hwy::If<hwy::IsSame<Weight, float>(), float, hwy::bfloat16_t>;
 
   static constexpr size_t kHeads = TLayer::kHeads;
   static constexpr size_t kKVHeads = TLayer::kKVHeads;
@@ -166,29 +162,29 @@ struct CompressedLayer {
 
   union {
     struct {
-      ArrayT<WeightT, kAttVecEinsumWSize> attn_vec_einsum_w;
+      ArrayT<Weight, kAttVecEinsumWSize> attn_vec_einsum_w;
-      ArrayT<WeightT, kQKVEinsumWSize> qkv_einsum_w;
+      ArrayT<Weight, kQKVEinsumWSize> qkv_einsum_w;
       ArrayT<float, kAOBiasDim> attention_output_biases;
     };
 
     struct {
-      ArrayT<WeightT, kGriffinDim * kGriffinDim> linear_x_w;
+      ArrayT<Weight, kGriffinDim * kGriffinDim> linear_x_w;
       ArrayT<float, kGriffinDim> linear_x_biases;
-      ArrayT<WeightT, kGriffinDim * kGriffinDim> linear_y_w;
+      ArrayT<Weight, kGriffinDim * kGriffinDim> linear_y_w;
       ArrayT<float, kGriffinDim> linear_y_biases;
-      ArrayT<WeightT, kGriffinDim * kGriffinDim> linear_out_w;
+      ArrayT<Weight, kGriffinDim * kGriffinDim> linear_out_w;
       ArrayT<float, kGriffinDim> linear_out_biases;
       ArrayT<float, TConfig::kConv1dWidth * kGriffinDim> conv_w;
       ArrayT<float, kGriffinDim> conv_biases;
-      ArrayT<WeightT, kGriffinDim * kGriffinDim / kHeads * 2> gate_w;
+      ArrayT<Weight, kGriffinDim * kGriffinDim / kHeads * 2> gate_w;
       ArrayT<float, kGriffinDim * 2> gate_biases;
       ArrayT<float, kGriffinDim> a;
     } griffin;
   };
 
-  ArrayT<WeightT, TLayer::kGatingEinsumWSize> gating_einsum_w;
+  ArrayT<Weight, TLayer::kGatingEinsumWSize> gating_einsum_w;
-  ArrayT<WeightT, kModelDim * kFFHiddenDim> linear_w;
+  ArrayT<Weight, kModelDim * kFFHiddenDim> linear_w;
-  // We don't yet have an RMSNorm that accepts all WeightT.
+  // We don't yet have an RMSNorm that accepts all Weight.
   ArrayT<WeightF32OrBF16, kModelDim> pre_attention_norm_scale;
   ArrayT<WeightF32OrBF16, kModelDim> pre_ffw_norm_scale;
   ArrayT<WeightF32OrBF16, kPostNormScale ? kModelDim : 0>
@@ -241,7 +237,7 @@ template <class TConfig>
 using WeightsT = hwy::If<kWeightsAreCompressed, CompressedWeights<TConfig>,
                          WeightsF<TConfig>>;
 
-// Call via CallFunctorForModel.
+// TODO: can we use TConfig::Weight instead of T?
 template <typename T, typename TConfig>
 struct AllocateWeights {
   ByteStorageT operator()(hwy::ThreadPool& pool) const {
@@ -335,14 +331,15 @@ class WeightsWrapper {
 };
 
 // For use by compress_weights.cc.
-ByteStorageT LoadRawWeights(const Path& weights, Model model,
+ByteStorageT LoadRawWeights(const Path& weights, Model model_type,
-                            hwy::ThreadPool& pool, bool scale_for_compression);
+                            Type weight_type, hwy::ThreadPool& pool,
+                            bool scale_for_compression);
 
 // For gemma.cc; calls LoadRawWeights if !kWeightsAreCompressed.
-ByteStorageT LoadWeights(const Path& weights, Model model,
+ByteStorageT LoadWeights(const Path& weights, Model model_type,
-                         hwy::ThreadPool& pool);
+                         Type weight_type, hwy::ThreadPool& pool);
 
-void LogWeightStats(Model model, const ByteStorageT& weights);
+void LogWeightStats(Model model, Type weight_type, const ByteStorageT& weights);
 
 // ----------------------------------------------------------------------------
 // Iterators

util/app.h

@@ -21,19 +21,17 @@
 #include "hwy/contrib/thread_pool/thread_pool.h"
 #if HWY_OS_LINUX
 #include <sched.h>
-
-#include <cctype>
-#include <cerrno>  // IDE does not recognize errno.h as providing errno.
-#include <string>
 #endif  // HWY_OS_LINUX
 #include <stddef.h>
 #include <stdio.h>
 
 #include <algorithm>  // std::clamp
+#include <string>
 #include <thread>  // NOLINT>
 #include <vector>
 
 #include "compression/io.h"  // Path
+#include "gemma/common.h"
 #include "gemma/configs.h"
 #include "gemma/gemma.h"
 #include "util/args.h"
@@ -49,14 +47,10 @@ static inline const char* CompiledConfig() {
     return "msan";
   } else if (HWY_IS_TSAN) {
     return "tsan";
-#if defined(HWY_IS_HWASAN)
   } else if (HWY_IS_HWASAN) {
     return "hwasan";
-#endif
-#if defined(HWY_IS_UBSAN)
   } else if (HWY_IS_UBSAN) {
     return "ubsan";
-#endif
   } else if (HWY_IS_DEBUG_BUILD) {
     return "dbg";
   } else {
@@ -172,15 +166,15 @@ class AppArgs : public ArgsBase<AppArgs> {
 struct LoaderArgs : public ArgsBase<LoaderArgs> {
   LoaderArgs(int argc, char* argv[]) { InitAndParse(argc, argv); }
 
-  gcpp::Model ModelType() const { return model_type; }
-
-  gcpp::ModelTraining ModelTraining() const { return model_training; }
-
   // Returns error string or nullptr if OK.
   const char* Validate() {
-    const char* parse_result =
+    if (const char* err = ParseModelTypeAndTraining(model_type_str, model_type_,
-        ParseModelTypeAndTraining(model_type_str, model_type, model_training);
+                                                    model_training_)) {
-    if (parse_result) return parse_result;
+      return err;
+    }
+    if (const char* err = ParseType(weight_type_str, weight_type_)) {
+      return err;
+    }
     if (tokenizer.path.empty()) {
       return "Missing --tokenizer flag, a file for the tokenizer is required.";
     }
@@ -209,8 +203,7 @@ struct LoaderArgs : public ArgsBase<LoaderArgs> {
   Path weights;  // weights file location
   Path compressed_weights;
   std::string model_type_str;
-  Model model_type;
+  std::string weight_type_str;
-  enum ModelTraining model_training;
 
   template <class Visitor>
   void ForEach(const Visitor& visitor) {
@@ -227,9 +220,28 @@ struct LoaderArgs : public ArgsBase<LoaderArgs> {
             "gr2b-it = griffin 2B parameters, instruction-tuned\n    "
             "gr2b-pt = griffin 2B parameters, pretrained\n    "
             "    Required argument.");
+    visitor(weight_type_str, "weight_type", std::string("sfp"),
+            "Weight type\n    f32 = float, bf16 = bfloat16, SFP = 8-bit FP\n"
+            "    Required argument.");
   }
+
+  // Uninitialized before Validate, must call after that.
+  gcpp::Model ModelType() const { return model_type_; }
+  gcpp::ModelTraining ModelTrainingType() const { return model_training_; }
+  gcpp::Type WeightType() const { return weight_type_; }
+
+ private:
+  Model model_type_;
+  ModelTraining model_training_;
+  Type weight_type_;
 };
 
+static inline Gemma CreateGemma(const LoaderArgs& loader,
+                                hwy::ThreadPool& pool) {
+  return Gemma(loader.tokenizer, loader.weights, loader.ModelType(),
+               loader.WeightType(), pool);
+}
+
 struct InferenceArgs : public ArgsBase<InferenceArgs> {
   InferenceArgs(int argc, char* argv[]) { InitAndParse(argc, argv); }