benchmark: compare backend graph computation times

Track execution time of individual tensor operations (views, copies, reshapes etc)
during LLM forward passes using CGo bindings to the native graph runtime. This
helps identify performance bottlenecks in the computation graph and optimize memory
operations that can significantly impact inference latency.

.github/workflows/release.yaml

@@ -160,10 +160,6 @@ jobs:
           echo "$hipPath\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
           echo "CC=$hipPath\bin\clang.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
           echo "CXX=$hipPath\bin\clang++.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
-      - if: matrix.preset == 'CPU'
-        run: |
-          echo "CC=clang.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
-          echo "CXX=clang++.exe" | Out-File -FilePath $env:GITHUB_ENV -Append
       - if: ${{ !cancelled() && steps.cache-install.outputs.cache-hit != 'true' }}
         uses: actions/cache/save@v4
         with:

README.md

@@ -384,8 +384,6 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [OpenDeepResearcher-via-searxng](https://github.com/benhaotang/OpenDeepResearcher-via-searxng) (A Deep Research equivent endpoint with Ollama support for running locally)
 - [AntSK](https://github.com/AIDotNet/AntSK) (Out-of-the-box & Adaptable RAG Chatbot)
 - [MaxKB](https://github.com/1Panel-dev/MaxKB/) (Ready-to-use & flexible RAG Chatbot)
-- [yla](https://github.com/danielekp/yla) (Web interface to freely interact with your customized models)
-- [LangBot](https://github.com/RockChinQ/LangBot) (LLM-based instant messaging bots platform, with Agents, RAG features, supports multiple platforms)
 
 ### Cloud
 

api/client.go

@@ -132,7 +132,7 @@ func (c *Client) do(ctx context.Context, method, path string, reqData, respData
 const maxBufferSize = 512 * format.KiloByte
 
 func (c *Client) stream(ctx context.Context, method, path string, data any, fn func([]byte) error) error {
-	var buf io.Reader
+	var buf *bytes.Buffer
 	if data != nil {
 		bts, err := json.Marshal(data)
 		if err != nil {

api/client_test.go

@@ -1,13 +1,6 @@
 package api
 
 import (
-	"context"
-	"encoding/json"
-	"fmt"
-	"net/http"
-	"net/http/httptest"
-	"net/url"
-	"strings"
 	"testing"
 )
 
@@ -50,206 +43,3 @@ func TestClientFromEnvironment(t *testing.T) {
 		})
 	}
 }
-
-// testError represents an internal error type with status code and message
-// this is used since the error response from the server is not a standard error struct
-type testError struct {
-	message    string
-	statusCode int
-}
-
-func (e testError) Error() string {
-	return e.message
-}
-
-func TestClientStream(t *testing.T) {
-	testCases := []struct {
-		name      string
-		responses []any
-		wantErr   string
-	}{
-		{
-			name: "immediate error response",
-			responses: []any{
-				testError{
-					message:    "test error message",
-					statusCode: http.StatusBadRequest,
-				},
-			},
-			wantErr: "test error message",
-		},
-		{
-			name: "error after successful chunks, ok response",
-			responses: []any{
-				ChatResponse{Message: Message{Content: "partial response 1"}},
-				ChatResponse{Message: Message{Content: "partial response 2"}},
-				testError{
-					message:    "mid-stream error",
-					statusCode: http.StatusOK,
-				},
-			},
-			wantErr: "mid-stream error",
-		},
-		{
-			name: "successful stream completion",
-			responses: []any{
-				ChatResponse{Message: Message{Content: "chunk 1"}},
-				ChatResponse{Message: Message{Content: "chunk 2"}},
-				ChatResponse{
-					Message:    Message{Content: "final chunk"},
-					Done:       true,
-					DoneReason: "stop",
-				},
-			},
-		},
-	}
-
-	for _, tc := range testCases {
-		t.Run(tc.name, func(t *testing.T) {
-			ts := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
-				flusher, ok := w.(http.Flusher)
-				if !ok {
-					t.Fatal("expected http.Flusher")
-				}
-
-				w.Header().Set("Content-Type", "application/x-ndjson")
-
-				for _, resp := range tc.responses {
-					if errResp, ok := resp.(testError); ok {
-						w.WriteHeader(errResp.statusCode)
-						err := json.NewEncoder(w).Encode(map[string]string{
-							"error": errResp.message,
-						})
-						if err != nil {
-							t.Fatal("failed to encode error response:", err)
-						}
-						return
-					}
-
-					if err := json.NewEncoder(w).Encode(resp); err != nil {
-						t.Fatalf("failed to encode response: %v", err)
-					}
-					flusher.Flush()
-				}
-			}))
-			defer ts.Close()
-
-			client := NewClient(&url.URL{Scheme: "http", Host: ts.Listener.Addr().String()}, http.DefaultClient)
-
-			var receivedChunks []ChatResponse
-			err := client.stream(context.Background(), http.MethodPost, "/v1/chat", nil, func(chunk []byte) error {
-				var resp ChatResponse
-				if err := json.Unmarshal(chunk, &resp); err != nil {
-					return fmt.Errorf("failed to unmarshal chunk: %w", err)
-				}
-				receivedChunks = append(receivedChunks, resp)
-				return nil
-			})
-
-			if tc.wantErr != "" {
-				if err == nil {
-					t.Fatal("expected error but got nil")
-				}
-				if !strings.Contains(err.Error(), tc.wantErr) {
-					t.Errorf("expected error containing %q, got %v", tc.wantErr, err)
-				}
-				return
-			}
-			if err != nil {
-				t.Errorf("unexpected error: %v", err)
-			}
-		})
-	}
-}
-
-func TestClientDo(t *testing.T) {
-	testCases := []struct {
-		name     string
-		response any
-		wantErr  string
-	}{
-		{
-			name: "immediate error response",
-			response: testError{
-				message:    "test error message",
-				statusCode: http.StatusBadRequest,
-			},
-			wantErr: "test error message",
-		},
-		{
-			name: "server error response",
-			response: testError{
-				message:    "internal error",
-				statusCode: http.StatusInternalServerError,
-			},
-			wantErr: "internal error",
-		},
-		{
-			name: "successful response",
-			response: struct {
-				ID      string `json:"id"`
-				Success bool   `json:"success"`
-			}{
-				ID:      "msg_123",
-				Success: true,
-			},
-		},
-	}
-
-	for _, tc := range testCases {
-		t.Run(tc.name, func(t *testing.T) {
-			ts := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
-				if errResp, ok := tc.response.(testError); ok {
-					w.WriteHeader(errResp.statusCode)
-					err := json.NewEncoder(w).Encode(map[string]string{
-						"error": errResp.message,
-					})
-					if err != nil {
-						t.Fatal("failed to encode error response:", err)
-					}
-					return
-				}
-
-				w.Header().Set("Content-Type", "application/json")
-				if err := json.NewEncoder(w).Encode(tc.response); err != nil {
-					t.Fatalf("failed to encode response: %v", err)
-				}
-			}))
-			defer ts.Close()
-
-			client := NewClient(&url.URL{Scheme: "http", Host: ts.Listener.Addr().String()}, http.DefaultClient)
-
-			var resp struct {
-				ID      string `json:"id"`
-				Success bool   `json:"success"`
-			}
-			err := client.do(context.Background(), http.MethodPost, "/v1/messages", nil, &resp)
-
-			if tc.wantErr != "" {
-				if err == nil {
-					t.Fatalf("got nil, want error %q", tc.wantErr)
-				}
-				if err.Error() != tc.wantErr {
-					t.Errorf("error message mismatch: got %q, want %q", err.Error(), tc.wantErr)
-				}
-				return
-			}
-
-			if err != nil {
-				t.Fatalf("got error %q, want nil", err)
-			}
-
-			if expectedResp, ok := tc.response.(struct {
-				ID      string `json:"id"`
-				Success bool   `json:"success"`
-			}); ok {
-				if resp.ID != expectedResp.ID {
-					t.Errorf("response ID mismatch: got %q, want %q", resp.ID, expectedResp.ID)
-				}
-				if resp.Success != expectedResp.Success {
-					t.Errorf("response Success mismatch: got %v, want %v", resp.Success, expectedResp.Success)
-				}
-			}
-		})
-	}
-}

benchmark/ggml_backend_benchmark_test.go

@@ -0,0 +1,86 @@
+package backend
+
+import (
+	"flag"
+	"fmt"
+	"io"
+	"log"
+	"os"
+	"testing"
+
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/server"
+
+	_ "github.com/ollama/ollama/model/models/llama"
+)
+
+var modelName = flag.String("m", "", "Name of the model to benchmark")
+
+func suppressOutput() (cleanup func()) {
+	oldStdout, oldStderr := os.Stdout, os.Stderr
+	os.Stdout, os.Stderr = nil, nil
+	log.SetOutput(io.Discard)
+
+	return func() {
+		os.Stdout, os.Stderr = oldStdout, oldStderr
+		log.SetOutput(os.Stderr)
+	}
+}
+
+func setupModel(b *testing.B) model.Model {
+	if *modelName == "" {
+		b.Fatal("Error: -m flag is required for benchmark tests")
+	}
+
+	sm, err := server.GetModel(*modelName)
+	if err != nil {
+		b.Fatal(err)
+	}
+
+	m, err := model.New(sm.ModelPath)
+	if err != nil {
+		b.Fatal(err)
+	}
+
+	m.Config().Cache.Init(m.Backend(), ml.DTypeF32, 2048)
+	return m
+}
+
+func BenchmarkGGMLOperations(b *testing.B) {
+	// loading the GGML back-end logs to standard out and makes the bench output messy
+	cleanup := suppressOutput()
+	defer cleanup()
+
+	b.Setenv("OLLAMA_BENCHMARK", "1")
+	b.Setenv("OLLAMA_BACKEND", "ggml")
+
+	m := setupModel(b)
+
+	// Sample input data
+	inputIDs := []int32{1, 2, 3, 4, 5}
+	options := model.Options{
+		Inputs:    inputIDs,
+		Positions: []int32{1, 2, 3, 4, 5},
+		Sequences: []int{1, 1, 1, 1, 1},
+		Outputs:   []int32{int32(len(inputIDs) - 1)},
+	}
+
+	b.ResetTimer()
+
+	for range b.N {
+		ctx := m.Backend().NewContext()
+		defer ctx.Close()
+
+		modelOutput, err := model.Forward(ctx, m, options)
+		if err != nil {
+			b.Fatal(fmt.Errorf("forward pass failed: %v", err))
+		}
+
+		ctx.Compute(modelOutput)
+
+		for _, op := range ctx.Timing() {
+			b.ReportMetric(op.Duration, fmt.Sprintf("%s_ms", op.Type))
+		}
+	}
+}

docs/development.md

@@ -46,6 +46,15 @@ Install prerequisites:
 - (Optional) NVIDIA GPU support
     - [CUDA SDK](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=11&target_type=exe_network)
 
+> [!IMPORTANT]
+> Ensure prerequisites are in `PATH` before running CMake.
+
+> [!IMPORTANT]
+> ROCm is not compatible with Visual Studio CMake generators. Use `-GNinja` when configuring the project.
+
+> [!IMPORTANT]
+> CUDA is only compatible with Visual Studio CMake generators.
+
 Then, configure and build the project:
 
 ```shell
@@ -53,14 +62,6 @@ cmake -B build
 cmake --build build --config Release
 ```
 
-> [!IMPORTANT]
-> Building for ROCm requires additional flags:
-> ```
-> cmake -B build -G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++
-> cmake --build build --config Release
-> ```
-
-
 Lastly, run Ollama:
 
 ```shell

envconfig/config.go

@@ -53,8 +53,8 @@ func Host() *url.URL {
 	}
 }
 
-// AllowedOrigins returns a list of allowed origins. AllowedOrigins can be configured via the OLLAMA_ORIGINS environment variable.
-func AllowedOrigins() (origins []string) {
+// Origins returns a list of allowed origins. Origins can be configured via the OLLAMA_ORIGINS environment variable.
+func Origins() (origins []string) {
 	if s := Var("OLLAMA_ORIGINS"); s != "" {
 		origins = strings.Split(s, ",")
 	}
@@ -167,6 +167,8 @@ var (
 	MultiUserCache = Bool("OLLAMA_MULTIUSER_CACHE")
 	// Enable the new Ollama engine
 	NewEngine = Bool("OLLAMA_NEW_ENGINE")
+	// Ollama is running in a benchmark context, additional timing data will be collected.
+	Benchmark = Bool("OLLAMA_BENCHMARK")
 )
 
 func String(s string) func() string {
@@ -249,7 +251,7 @@ func AsMap() map[string]EnvVar {
 		"OLLAMA_NOHISTORY":         {"OLLAMA_NOHISTORY", NoHistory(), "Do not preserve readline history"},
 		"OLLAMA_NOPRUNE":           {"OLLAMA_NOPRUNE", NoPrune(), "Do not prune model blobs on startup"},
 		"OLLAMA_NUM_PARALLEL":      {"OLLAMA_NUM_PARALLEL", NumParallel(), "Maximum number of parallel requests"},
-		"OLLAMA_ORIGINS":           {"OLLAMA_ORIGINS", AllowedOrigins(), "A comma separated list of allowed origins"},
+		"OLLAMA_ORIGINS":           {"OLLAMA_ORIGINS", Origins(), "A comma separated list of allowed origins"},
 		"OLLAMA_SCHED_SPREAD":      {"OLLAMA_SCHED_SPREAD", SchedSpread(), "Always schedule model across all GPUs"},
 		"OLLAMA_MULTIUSER_CACHE":   {"OLLAMA_MULTIUSER_CACHE", MultiUserCache(), "Optimize prompt caching for multi-user scenarios"},
 		"OLLAMA_NEW_ENGINE":        {"OLLAMA_NEW_ENGINE", NewEngine(), "Enable the new Ollama engine"},

envconfig/config_test.go

@@ -134,7 +134,7 @@ func TestOrigins(t *testing.T) {
 		t.Run(tt.value, func(t *testing.T) {
 			t.Setenv("OLLAMA_ORIGINS", tt.value)
 
-			if diff := cmp.Diff(AllowedOrigins(), tt.expect); diff != "" {
+			if diff := cmp.Diff(Origins(), tt.expect); diff != "" {
 				t.Errorf("%s: mismatch (-want +got):\n%s", tt.value, diff)
 			}
 		})

kvcache/causal_test.go

@@ -352,6 +352,10 @@ func (c *testContext) MaxTensors() int {
 	return 10
 }
 
+func (c *testContext) Timing() []ml.OpTiming {
+	return []ml.OpTiming{}
+}
+
 func (c *testContext) Close() {}
 
 type testTensor struct {

llama/patches/0018-use-std-filesystem-path-instead-of-wstring.patch

@@ -4,23 +4,17 @@ Date: Sun, 16 Feb 2025 20:00:22 -0500
 Subject: [PATCH] use std::filesystem::path instead of wstring
 
 ---
- ggml/src/ggml-backend-reg.cpp | 144 ++++++++++++++--------------------
- 1 file changed, 58 insertions(+), 86 deletions(-)
+ ggml/src/ggml-backend-reg.cpp | 116 ++++++++++++----------------------
+ 1 file changed, 40 insertions(+), 76 deletions(-)
 
 diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp
-index 84b21dd8..e35a6936 100644
+index 84b21dd8..de78feae 100644
 --- a/ggml/src/ggml-backend-reg.cpp
 +++ b/ggml/src/ggml-backend-reg.cpp
-@@ -66,26 +66,6 @@
- #include "ggml-kompute.h"
+@@ -72,16 +72,6 @@
+ #    pragma clang diagnostic ignored "-Wdeprecated-declarations"
  #endif
  
--// disable C++17 deprecation warning for std::codecvt_utf8
--#if defined(__clang__)
--#    pragma clang diagnostic push
--#    pragma clang diagnostic ignored "-Wdeprecated-declarations"
--#endif
--
 -static std::wstring utf8_to_utf16(const std::string & str) {
 -    std::wstring_convert<std::codecvt_utf8_utf16<wchar_t>> converter;
 -    return converter.from_bytes(str);
@@ -31,14 +25,10 @@ index 84b21dd8..e35a6936 100644
 -    return converter.to_bytes(str);
 -}
 -
--#if defined(__clang__)
--#    pragma clang diagnostic pop
--#endif
--
- #ifdef _WIN32
- 
- using dl_handle = std::remove_pointer_t<HMODULE>;
-@@ -96,7 +76,7 @@ struct dl_handle_deleter {
+ #if defined(__clang__)
+ #    pragma clang diagnostic pop
+ #endif
+@@ -96,12 +86,12 @@ struct dl_handle_deleter {
      }
  };
  
@@ -47,44 +37,24 @@ index 84b21dd8..e35a6936 100644
      // suppress error dialogs for missing DLLs
      DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS);
      SetErrorMode(old_mode | SEM_FAILCRITICALERRORS);
-@@ -129,8 +109,8 @@ struct dl_handle_deleter {
+ 
+-    HMODULE handle = LoadLibraryW(path.c_str());
++    HMODULE handle = LoadLibraryW(path.wstring().c_str());
+ 
+     SetErrorMode(old_mode);
+ 
+@@ -129,8 +119,8 @@ struct dl_handle_deleter {
      }
  };
  
 -static void * dl_load_library(const std::wstring & path) {
 -    dl_handle * handle = dlopen(utf16_to_utf8(path).c_str(), RTLD_NOW | RTLD_LOCAL);
 +static void * dl_load_library(const std::filesystem::path & path) {
-+    dl_handle * handle = dlopen(path.c_str(), RTLD_NOW | RTLD_LOCAL);
++    dl_handle * handle = dlopen(path.string().c_str(), RTLD_NOW | RTLD_LOCAL);
  
      return handle;
  }
-@@ -141,6 +121,25 @@ static void * dl_get_sym(dl_handle * handle, const char * name) {
- 
- #endif
- 
-+static std::string path_to_string(const std::filesystem::path & path)
-+{
-+#ifdef _WIN32
-+    const std::wstring wstr = path.wstring();
-+    const int size_needed = WideCharToMultiByte(CP_UTF8, 0, wstr.c_str(), -1, nullptr, 0, nullptr, nullptr);
-+    if (size_needed <= 0) {
-+        return std::string();
-+    }
-+
-+    // size_needed includes the null terminator
-+    std::string str(size_needed - 1, '\0');
-+    WideCharToMultiByte(CP_UTF8, 0, wstr.c_str(), -1, str.data(), size_needed, nullptr, nullptr);
-+    return str;
-+#else
-+    return path.string();
-+#endif
-+}
-+
-+
- using dl_handle_ptr = std::unique_ptr<dl_handle, dl_handle_deleter>;
- 
- struct ggml_backend_reg_entry {
-@@ -222,11 +221,11 @@ struct ggml_backend_registry {
+@@ -222,11 +212,11 @@ struct ggml_backend_registry {
          );
      }
  
@@ -94,49 +64,49 @@ index 84b21dd8..e35a6936 100644
          if (!handle) {
              if (!silent) {
 -                GGML_LOG_ERROR("%s: failed to load %s\n", __func__, utf16_to_utf8(path).c_str());
-+                GGML_LOG_ERROR("%s: failed to load %s\n", __func__, path_to_string(path).c_str());
++                GGML_LOG_ERROR("%s: failed to load %s\n", __func__, path.string().c_str());
              }
              return nullptr;
          }
-@@ -234,7 +233,7 @@ struct ggml_backend_registry {
+@@ -234,7 +224,7 @@ struct ggml_backend_registry {
          auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
          if (score_fn && score_fn() == 0) {
              if (!silent) {
 -                GGML_LOG_INFO("%s: backend %s is not supported on this system\n", __func__, utf16_to_utf8(path).c_str());
-+                GGML_LOG_INFO("%s: backend %s is not supported on this system\n", __func__, path_to_string(path).c_str());
++                GGML_LOG_INFO("%s: backend %s is not supported on this system\n", __func__, path.string().c_str());
              }
              return nullptr;
          }
-@@ -242,7 +241,7 @@ struct ggml_backend_registry {
+@@ -242,7 +232,7 @@ struct ggml_backend_registry {
          auto backend_init_fn = (ggml_backend_init_t) dl_get_sym(handle.get(), "ggml_backend_init");
          if (!backend_init_fn) {
              if (!silent) {
 -                GGML_LOG_ERROR("%s: failed to find ggml_backend_init in %s\n", __func__, utf16_to_utf8(path).c_str());
-+                GGML_LOG_ERROR("%s: failed to find ggml_backend_init in %s\n", __func__, path_to_string(path).c_str());
++                GGML_LOG_ERROR("%s: failed to find ggml_backend_init in %s\n", __func__, path.string().c_str());
              }
              return nullptr;
          }
-@@ -251,16 +250,16 @@ struct ggml_backend_registry {
+@@ -251,16 +241,16 @@ struct ggml_backend_registry {
          if (!reg || reg->api_version != GGML_BACKEND_API_VERSION) {
              if (!silent) {
                  if (!reg) {
 -                    GGML_LOG_ERROR("%s: failed to initialize backend from %s: ggml_backend_init returned NULL\n", __func__, utf16_to_utf8(path).c_str());
-+                    GGML_LOG_ERROR("%s: failed to initialize backend from %s: ggml_backend_init returned NULL\n", __func__, path_to_string(path).c_str());
++                    GGML_LOG_ERROR("%s: failed to initialize backend from %s: ggml_backend_init returned NULL\n", __func__, path.string().c_str());
                  } else {
                      GGML_LOG_ERROR("%s: failed to initialize backend from %s: incompatible API version (backend: %d, current: %d)\n",
 -                        __func__, utf16_to_utf8(path).c_str(), reg->api_version, GGML_BACKEND_API_VERSION);
-+                        __func__, path_to_string(path).c_str(), reg->api_version, GGML_BACKEND_API_VERSION);
++                        __func__, path.string().c_str(), reg->api_version, GGML_BACKEND_API_VERSION);
                  }
              }
              return nullptr;
          }
  
 -        GGML_LOG_INFO("%s: loaded %s backend from %s\n", __func__, ggml_backend_reg_name(reg), utf16_to_utf8(path).c_str());
-+        GGML_LOG_INFO("%s: loaded %s backend from %s\n", __func__, ggml_backend_reg_name(reg), path_to_string(path).c_str());
++        GGML_LOG_INFO("%s: loaded %s backend from %s\n", __func__, ggml_backend_reg_name(reg), path.string().c_str());
  
          register_backend(reg, score_fn ? score_fn() : -1, std::move(handle));
  
-@@ -396,14 +395,14 @@ ggml_backend_t ggml_backend_init_best(void) {
+@@ -396,14 +386,14 @@ ggml_backend_t ggml_backend_init_best(void) {
  
  // Dynamic loading
  ggml_backend_reg_t ggml_backend_load(const char * path) {
@@ -153,7 +123,7 @@ index 84b21dd8..e35a6936 100644
  #if defined(__APPLE__)
      // get executable path
      std::vector<char> path;
-@@ -415,15 +414,9 @@ static std::wstring get_executable_path() {
+@@ -415,15 +405,9 @@ static std::wstring get_executable_path() {
          }
          path.resize(size);
      }
@@ -171,7 +141,7 @@ index 84b21dd8..e35a6936 100644
      std::vector<char> path(1024);
      while (true) {
          // get executable path
-@@ -436,76 +429,55 @@ static std::wstring get_executable_path() {
+@@ -436,76 +420,56 @@ static std::wstring get_executable_path() {
              break;
          }
          if (len < (ssize_t) path.size()) {
@@ -209,11 +179,11 @@ index 84b21dd8..e35a6936 100644
 -static std::wstring backend_filename_prefix() {
 -#ifdef _WIN32
 -    return L"ggml-";
--#else
--    return L"libggml-";
 +    return std::filesystem::path(path.data()).parent_path();
- #endif
+ #else
+-    return L"libggml-";
 +    return {};
+ #endif
  }
  
 -static std::wstring backend_filename_suffix() {
@@ -264,7 +234,7 @@ index 84b21dd8..e35a6936 100644
      for (const auto & search_path : search_paths) {
          if (!fs::exists(search_path)) {
              continue;
-@@ -514,31 +486,31 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
+@@ -514,31 +478,31 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
          for (const auto & entry : dir_it) {
              try {
                  if (entry.is_regular_file()) {
@@ -277,20 +247,20 @@ index 84b21dd8..e35a6936 100644
 +                        dl_handle_ptr handle { dl_load_library(entry.path()) };
                          if (!handle) {
 -                            GGML_LOG_ERROR("%s: failed to load %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str());
-+                            GGML_LOG_ERROR("%s: failed to load %s\n", __func__, path_to_string(entry.path()).c_str());
++                            GGML_LOG_ERROR("%s: failed to load %s\n", __func__, entry.path().string().c_str());
                              continue;
                          }
  
                          auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
                          if (!score_fn) {
 -                            GGML_LOG_DEBUG("%s: failed to find ggml_backend_score in %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str());
-+                            GGML_LOG_DEBUG("%s: failed to find ggml_backend_score in %s\n", __func__, path_to_string(entry.path()).c_str());
++                            GGML_LOG_DEBUG("%s: failed to find ggml_backend_score in %s\n", __func__, entry.path().string().c_str());
                              continue;
                          }
  
                          int s = score_fn();
 -                        GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str(), s);
-+                        GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, path_to_string(entry.path()).c_str(), s);
++                        GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, entry.path().string().c_str(), s);
                          if (s > best_score) {
                              best_score = s;
 -                            best_path = entry.path().wstring();
@@ -300,11 +270,11 @@ index 84b21dd8..e35a6936 100644
                  }
              } catch (const std::exception & e) {
 -                GGML_LOG_ERROR("%s: failed to load %s: %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str(), e.what());
-+                GGML_LOG_ERROR("%s: failed to load %s: %s\n", __func__, path_to_string(entry.path()).c_str(), e.what());
++                GGML_LOG_ERROR("%s: failed to load %s: %s\n", __func__, entry.path().string().c_str(), e.what());
              }
          }
      }
-@@ -546,7 +518,7 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
+@@ -546,7 +510,7 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
      if (best_score == 0) {
          // try to load the base backend
          for (const auto & search_path : search_paths) {

ml/backend.go

@@ -2,6 +2,7 @@ package ml
 
 import (
 	"bytes"
+	"cmp"
 	"encoding/binary"
 	"fmt"
 	"os"
@@ -26,24 +27,9 @@ type Backend interface {
 	SystemInfo() string
 }
 
-// BackendParams controls how the backend loads and executes models
-type BackendParams struct {
-	// NumThreads sets the number of threads to use if running on the CPU
-	NumThreads int
+var backends = make(map[string]func(*os.File) (Backend, error))
 
-	// MainGPU is the index of the primary GPU to use
-	MainGPU int
-
-	// NumGPULayers is the number of layers to offload to GPUs
-	NumGPULayers int
-
-	// TensorSplit is the fraction of the model to offload to each GPU
-	TensorSplit []float32
-}
-
-var backends = make(map[string]func(*os.File, BackendParams) (Backend, error))
-
-func RegisterBackend(name string, f func(*os.File, BackendParams) (Backend, error)) {
+func RegisterBackend(name string, f func(*os.File) (Backend, error)) {
 	if _, ok := backends[name]; ok {
 		panic("backend: backend already registered")
 	}
@@ -51,9 +37,9 @@ func RegisterBackend(name string, f func(*os.File, BackendParams) (Backend, erro
 	backends[name] = f
 }
 
-func NewBackend(f *os.File, params BackendParams) (Backend, error) {
-	if backend, ok := backends["ggml"]; ok {
-		return backend(f, params)
+func NewBackend(f *os.File) (Backend, error) {
+	if backend, ok := backends[cmp.Or(os.Getenv("OLLAMA_BACKEND"), "ggml")]; ok {
+		return backend(f)
 	}
 
 	return nil, fmt.Errorf("unsupported backend")
@@ -68,6 +54,30 @@ type Context interface {
 	Compute(...Tensor)
 	MaxTensors() int
 	Close()
+
+	Timing() []OpTiming
+}
+
+// OpType is the type of operation performed during a forward pass.
+type OpType string
+
+const (
+	View       OpType = "View"
+	Copy       OpType = "Copy"
+	Reshape    OpType = "Reshape"
+	Permute    OpType = "Permute"
+	Contiguous OpType = "Contiguous"
+	Input      OpType = "Input"
+	ComputeOp  OpType = "Compute"
+	Transpose  OpType = "Transpose"
+)
+
+// OpTiming stores the timing information for a single operation.
+type OpTiming struct {
+	Type      OpType
+	Operation string
+	Duration  float64
+	Order     int
 }
 
 type Tensor interface {
@@ -111,26 +121,6 @@ type Tensor interface {
 	Copy(ctx Context, t2 Tensor) Tensor
 }
 
-// ScaledDotProductAttention implements a fused attention
-// operation equivalent to following code on a tensor named
-// query:
-//
-// kq := key.MulmatFullPrec(ctx, query)
-//
-// kq = kq.Scale(ctx, scale)
-//
-//	if mask != nil {
-//		kq = kq.Add(ctx, mask)
-//	}
-//
-// kq = kq.Softmax(ctx)
-//
-// kqv := value.Mulmat(ctx, kq)
-// return kqv.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
-type ScaledDotProductAttention interface {
-	ScaledDotProductAttention(ctx Context, key, value, mask Tensor, scale float64) Tensor
-}
-
 type number interface {
 	~int | ~int8 | ~int16 | ~int32 | ~int64 |
 		~uint | ~uint8 | ~uint16 | ~uint32 | ~uint64 |

ml/backend/ggml/ggml.go

@@ -4,6 +4,8 @@ package ggml
 #cgo CPPFLAGS: -I${SRCDIR}/ggml/include
 #include <stdlib.h>
 #include <stdint.h>
+#include <time.h>
+#include <string.h>
 #include "ggml.h"
 #include "ggml-cpu.h"
 #include "ggml-backend.h"
@@ -21,6 +23,54 @@ COMPILER inline get_compiler() {
 #endif
 }
 
+// Define a fixed-size struct to store timing data
+#define MAX_TENSOR_NAME 256
+#define MAX_TIMINGS 1000
+
+typedef struct {
+    char tensor_name[MAX_TENSOR_NAME];
+    double duration_ms;
+} timing_entry;
+
+typedef struct {
+    timing_entry entries[MAX_TIMINGS];
+    int count;
+} timing_data;
+
+// Global timing data structure
+timing_data g_timings = {0};
+
+double get_time_ms() {
+    struct timespec ts;
+    clock_gettime(CLOCK_MONOTONIC, &ts);
+    return ts.tv_sec * 1000.0 + ts.tv_nsec / 1000000.0;
+}
+
+bool debug_callback(struct ggml_tensor * t, bool ask, void * user_data) {
+    static double start_time;
+    static char current_tensor[MAX_TENSOR_NAME];
+
+    if (ask) {
+        start_time = get_time_ms();
+        strncpy(current_tensor, t->name, MAX_TENSOR_NAME - 1);
+        current_tensor[MAX_TENSOR_NAME - 1] = '\0';
+    } else {
+        double end_time = get_time_ms();
+        double duration = end_time - start_time;
+
+        if (g_timings.count < MAX_TIMINGS) {
+            strncpy(g_timings.entries[g_timings.count].tensor_name, current_tensor, MAX_TENSOR_NAME - 1);
+            g_timings.entries[g_timings.count].duration_ms = duration;
+            g_timings.count++;
+        }
+    }
+    return true;
+}
+
+void clear_timings() {
+    g_timings.count = 0;
+}
+
 */
 import "C"
 
@@ -29,9 +79,11 @@ import (
 	"io"
 	"log/slog"
 	"os"
+	"strings"
 	"sync"
 	"unsafe"
 
+	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
 	fs "github.com/ollama/ollama/fs/ggml"
 	"github.com/ollama/ollama/ml"
@@ -82,11 +134,9 @@ type Backend struct {
 	meta       *fs.GGML
 	cpus, gpus []Context
 	tensors    map[string]*Context
-
-	sched *C.struct_ggml_backend_sched
 }
 
-func New(r *os.File, params ml.BackendParams) (ml.Backend, error) {
+func New(r *os.File) (ml.Backend, error) {
 	meta, n, err := fs.Decode(r, -1)
 	if err != nil {
 		return nil, err
@@ -184,24 +234,10 @@ func New(r *os.File, params ml.BackendParams) (ml.Backend, error) {
 		return nil, err
 	}
 
-	backends := make([]*C.struct_ggml_backend, len(gpus)+len(cpus))
-	bufts := make([]*C.struct_ggml_backend_buffer_type, len(gpus)+len(cpus))
-	for i, c := range append(gpus, cpus...) {
-		backends[i] = c.backend
-		bufts[i] = C.ggml_backend_get_default_buffer_type(c.backend)
-	}
-
 	return &Backend{
 		meta: meta,
 		cpus: cpus,
 		gpus: gpus,
-		sched: C.ggml_backend_sched_new(
-			(*C.ggml_backend_t)(unsafe.Pointer(&backends[0])),
-			(*C.ggml_backend_buffer_type_t)(unsafe.Pointer(&bufts[0])),
-			C.int(len(backends)),
-			C.size_t(max(8192, len(meta.Tensors().Items())*5)),
-			true,
-		),
 	}, nil
 }
 
@@ -235,23 +271,31 @@ func (b *Backend) NewContext() ml.Context {
 	})
 
 	backends := make([]*C.struct_ggml_backend, len(b.gpus)+len(b.cpus))
+	bufts := make([]*C.struct_ggml_backend_buffer_type, len(b.gpus)+len(b.cpus))
 	for i, c := range append(b.gpus, b.cpus...) {
 		backends[i] = c.backend
+		bufts[i] = C.ggml_backend_get_default_buffer_type(c.backend)
 	}
 
 	return &Context{
-		b:       b,
 		ctx:     c,
 		backend: backends[0],
 		nodes:   nodes,
+		sched: C.ggml_backend_sched_new(
+			(*C.ggml_backend_t)(unsafe.Pointer(&backends[0])),
+			(*C.ggml_backend_buffer_type_t)(unsafe.Pointer(&bufts[0])),
+			C.int(len(backends)),
+			C.size_t(nodes),
+			true,
+		),
 	}
 }
 
 type Context struct {
-	b       *Backend
 	ctx     *C.struct_ggml_context
 	backend *C.struct_ggml_backend
 
+	sched *C.struct_ggml_backend_sched
 	graph *C.struct_ggml_cgraph
 	nodes int
 }
@@ -264,14 +308,68 @@ func (c *Context) Forward(t ml.Tensor) {
 	C.ggml_build_forward_expand(c.graph, t.(*Tensor).t)
 }
 
+// Timing retrieves the collected timing data
+func (c *Context) Timing() []ml.OpTiming {
+	sequence := make([]ml.OpTiming, C.g_timings.count)
+
+	for i := range int(C.g_timings.count) {
+		entry := C.g_timings.entries[i]
+		tensorName := C.GoString(&entry.tensor_name[0])
+
+		// Determine operation type and description based on tensor name
+		var opType ml.OpType
+		var opDesc string
+
+		switch {
+		case strings.Contains(tensorName, "(view)"):
+			opType, opDesc = ml.View, "Memory view"
+		case strings.Contains(tensorName, "(copy)") || strings.Contains(tensorName, "(copy of"):
+			opType, opDesc = ml.Copy, "Memory copy"
+		case strings.Contains(tensorName, "(reshaped)"):
+			opType, opDesc = ml.Reshape, "Reshape"
+		case strings.Contains(tensorName, "(permuted)"):
+			opType, opDesc = ml.Permute, "Permute dimensions"
+		case strings.Contains(tensorName, "(cont)"):
+			opType, opDesc = ml.Contiguous, "Make contiguous"
+		case strings.Contains(tensorName, "(transposed)"):
+			opType, opDesc = ml.Transpose, "Transpose"
+		case strings.HasPrefix(tensorName, "leaf_"):
+			opType, opDesc = ml.Input, fmt.Sprintf("Input tensor %s", tensorName)
+		case strings.HasPrefix(tensorName, "node_"):
+			opType, opDesc = ml.ComputeOp, fmt.Sprintf("Computation %s", tensorName)
+		default:
+			opType, opDesc = "Unknown", tensorName
+		}
+
+		sequence[i] = ml.OpTiming{
+			Type:      opType,
+			Operation: opDesc,
+			Duration:  float64(entry.duration_ms),
+			Order:     i,
+		}
+	}
+
+	return sequence
+}
+
 func (c *Context) Compute(tensors ...ml.Tensor) {
-	C.ggml_backend_sched_graph_compute_async(c.b.sched, c.graph)
-	C.ggml_backend_sched_reset(c.b.sched)
+	if envconfig.Benchmark() {
+		// Clear previous timings before new computation
+		C.clear_timings()
+
+		C.ggml_backend_sched_set_eval_callback(
+			c.sched,
+			C.ggml_backend_eval_callback(C.debug_callback),
+			nil,
+		)
+	}
+
+	C.ggml_backend_sched_graph_compute_async(c.sched, c.graph)
 
 	needSync := true
 	sync := func() {
 		if needSync {
-			C.ggml_backend_sched_synchronize(c.b.sched)
+			C.ggml_backend_sched_synchronize(c.sched)
 			needSync = false
 		}
 	}
@@ -359,6 +457,7 @@ func (c Context) FromIntSlice(s []int32, shape ...int) (ml.Tensor, error) {
 
 func (c *Context) Close() {
 	if c != nil {
+		C.ggml_backend_sched_free(c.sched)
 		C.ggml_free(c.ctx)
 	}
 }
@@ -485,7 +584,7 @@ func (t *Tensor) LayerNorm(ctx ml.Context, w, b ml.Tensor, eps float32) ml.Tenso
 }
 
 func (t *Tensor) RMSNorm(ctx ml.Context, w ml.Tensor, eps float32) ml.Tensor {
-	return (&Tensor{t: C.ggml_rms_norm(ctx.(*Context).ctx, t.t, C.float(eps))}).Mul(ctx, w)
+	return (&Tensor{t: C.ggml_norm(ctx.(*Context).ctx, t.t, C.float(eps))}).Mul(ctx, w)
 }
 
 func (t *Tensor) Pad(ctx ml.Context, shape ...int) ml.Tensor {
@@ -651,21 +750,6 @@ func (t *Tensor) Conv2D(ctx ml.Context, t2 ml.Tensor, s0, s1, p0, p1, d0, d1 int
 	}
 }
 
-func (t *Tensor) ScaledDotProductAttention(ctx ml.Context, key, value, mask ml.Tensor, scale float64) ml.Tensor {
-	var kqMask *C.struct_ggml_tensor
-	if mask != nil {
-		kqMask = mask.(*Tensor).t
-	}
-
-	kq := key.MulmatFullPrec(ctx, t)
-	kq = &Tensor{
-		t: C.ggml_soft_max_ext(ctx.(*Context).ctx, kq.(*Tensor).t, kqMask, C.float(scale), 0),
-	}
-
-	kqv := value.Mulmat(ctx, kq)
-	return kqv.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
-}
-
 func (b *Backend) SystemInfo() string {
 	var compiler string
 	switch C.get_compiler() {

ml/backend/ggml/ggml/src/ggml-backend-reg.cpp

@@ -66,6 +66,16 @@
 #include "ggml-kompute.h"
 #endif
 
+// disable C++17 deprecation warning for std::codecvt_utf8
+#if defined(__clang__)
+#    pragma clang diagnostic push
+#    pragma clang diagnostic ignored "-Wdeprecated-declarations"
+#endif
+
+#if defined(__clang__)
+#    pragma clang diagnostic pop
+#endif
+
 #ifdef _WIN32
 
 using dl_handle = std::remove_pointer_t<HMODULE>;
@@ -81,7 +91,7 @@ static dl_handle * dl_load_library(const std::filesystem::path & path) {
     DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS);
     SetErrorMode(old_mode | SEM_FAILCRITICALERRORS);
 
-    HMODULE handle = LoadLibraryW(path.c_str());
+    HMODULE handle = LoadLibraryW(path.wstring().c_str());
 
     SetErrorMode(old_mode);
 
@@ -110,7 +120,7 @@ struct dl_handle_deleter {
 };
 
 static void * dl_load_library(const std::filesystem::path & path) {
-    dl_handle * handle = dlopen(path.c_str(), RTLD_NOW | RTLD_LOCAL);
+    dl_handle * handle = dlopen(path.string().c_str(), RTLD_NOW | RTLD_LOCAL);
 
     return handle;
 }
@@ -121,25 +131,6 @@ static void * dl_get_sym(dl_handle * handle, const char * name) {
 
 #endif
 
-static std::string path_to_string(const std::filesystem::path & path)
-{
-#ifdef _WIN32
-    const std::wstring wstr = path.wstring();
-    const int size_needed = WideCharToMultiByte(CP_UTF8, 0, wstr.c_str(), -1, nullptr, 0, nullptr, nullptr);
-    if (size_needed <= 0) {
-        return std::string();
-    }
-
-    // size_needed includes the null terminator
-    std::string str(size_needed - 1, '\0');
-    WideCharToMultiByte(CP_UTF8, 0, wstr.c_str(), -1, str.data(), size_needed, nullptr, nullptr);
-    return str;
-#else
-    return path.string();
-#endif
-}
-
-
 using dl_handle_ptr = std::unique_ptr<dl_handle, dl_handle_deleter>;
 
 struct ggml_backend_reg_entry {
@@ -225,7 +216,7 @@ struct ggml_backend_registry {
         dl_handle_ptr handle { dl_load_library(path) };
         if (!handle) {
             if (!silent) {
-                GGML_LOG_ERROR("%s: failed to load %s\n", __func__, path_to_string(path).c_str());
+                GGML_LOG_ERROR("%s: failed to load %s\n", __func__, path.string().c_str());
             }
             return nullptr;
         }
@@ -233,7 +224,7 @@ struct ggml_backend_registry {
         auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
         if (score_fn && score_fn() == 0) {
             if (!silent) {
-                GGML_LOG_INFO("%s: backend %s is not supported on this system\n", __func__, path_to_string(path).c_str());
+                GGML_LOG_INFO("%s: backend %s is not supported on this system\n", __func__, path.string().c_str());
             }
             return nullptr;
         }
@@ -241,7 +232,7 @@ struct ggml_backend_registry {
         auto backend_init_fn = (ggml_backend_init_t) dl_get_sym(handle.get(), "ggml_backend_init");
         if (!backend_init_fn) {
             if (!silent) {
-                GGML_LOG_ERROR("%s: failed to find ggml_backend_init in %s\n", __func__, path_to_string(path).c_str());
+                GGML_LOG_ERROR("%s: failed to find ggml_backend_init in %s\n", __func__, path.string().c_str());
             }
             return nullptr;
         }
@@ -250,16 +241,16 @@ struct ggml_backend_registry {
         if (!reg || reg->api_version != GGML_BACKEND_API_VERSION) {
             if (!silent) {
                 if (!reg) {
-                    GGML_LOG_ERROR("%s: failed to initialize backend from %s: ggml_backend_init returned NULL\n", __func__, path_to_string(path).c_str());
+                    GGML_LOG_ERROR("%s: failed to initialize backend from %s: ggml_backend_init returned NULL\n", __func__, path.string().c_str());
                 } else {
                     GGML_LOG_ERROR("%s: failed to initialize backend from %s: incompatible API version (backend: %d, current: %d)\n",
-                        __func__, path_to_string(path).c_str(), reg->api_version, GGML_BACKEND_API_VERSION);
+                        __func__, path.string().c_str(), reg->api_version, GGML_BACKEND_API_VERSION);
                 }
             }
             return nullptr;
         }
 
-        GGML_LOG_INFO("%s: loaded %s backend from %s\n", __func__, ggml_backend_reg_name(reg), path_to_string(path).c_str());
+        GGML_LOG_INFO("%s: loaded %s backend from %s\n", __func__, ggml_backend_reg_name(reg), path.string().c_str());
 
         register_backend(reg, score_fn ? score_fn() : -1, std::move(handle));
 
@@ -441,8 +432,9 @@ static std::filesystem::path get_executable_path() {
     }
 
     return std::filesystem::path(path.data()).parent_path();
-#endif
+#else
     return {};
+#endif
 }
 
 static std::string backend_filename_prefix() {
@@ -491,18 +483,18 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
                     if (filename.find(file_prefix) == 0 && ext == backend_filename_suffix()) {
                         dl_handle_ptr handle { dl_load_library(entry.path()) };
                         if (!handle) {
-                            GGML_LOG_ERROR("%s: failed to load %s\n", __func__, path_to_string(entry.path()).c_str());
+                            GGML_LOG_ERROR("%s: failed to load %s\n", __func__, entry.path().string().c_str());
                             continue;
                         }
 
                         auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
                         if (!score_fn) {
-                            GGML_LOG_DEBUG("%s: failed to find ggml_backend_score in %s\n", __func__, path_to_string(entry.path()).c_str());
+                            GGML_LOG_DEBUG("%s: failed to find ggml_backend_score in %s\n", __func__, entry.path().string().c_str());
                             continue;
                         }
 
                         int s = score_fn();
-                        GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, path_to_string(entry.path()).c_str(), s);
+                        GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, entry.path().string().c_str(), s);
                         if (s > best_score) {
                             best_score = s;
                             best_path = entry.path();
@@ -510,7 +502,7 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
                     }
                 }
             } catch (const std::exception & e) {
-                GGML_LOG_ERROR("%s: failed to load %s: %s\n", __func__, path_to_string(entry.path()).c_str(), e.what());
+                GGML_LOG_ERROR("%s: failed to load %s: %s\n", __func__, entry.path().string().c_str(), e.what());
             }
         }
     }

ml/nn/attention.go

@@ -1,59 +0,0 @@
-package nn
-
-import (
-	"fmt"
-
-	"github.com/ollama/ollama/ml"
-)
-
-// Attention implements scaled dot-product attention for transformer models:
-// Attention(Q, K, V) = softmax(QK^T/√d_k)V
-//
-// Parameters:
-//   - ctx: Context for tensor operations
-//   - query: Query tensor (Q) with shape [d_k, seq_len_q, heads]
-//   - key: Key tensor (K) with shape [d_k, seq_len_k, kv_heads]
-//   - value: Value tensor (V) with shape [seq_len_k, d_v, kv_heads]
-//   - mask: Optional attention mask that is added to the attention score. If
-//     provided, should broadcast to [seq_len_k, seq_len_q, heads]
-//   - scale: Scaling factor, typically 1/√d_k where d_k is the key dimension
-//
-// Returns:
-//
-//	Attention output with shape [d_v, heads, seq_len_q]
-func Attention(ctx ml.Context, query, key, value, mask ml.Tensor, scale float64) ml.Tensor {
-	if query.Dim(0) != key.Dim(0) {
-		panic(fmt.Errorf("d_k in attention operation does not match between query(%v) and key(%v)", query.Dim(0), key.Dim(0)))
-	}
-
-	if mask != nil && query.Dim(1) != mask.Dim(1) {
-		panic(fmt.Errorf("seq_len_q in attention operation does not match between query(%v) and mask(%v)", query.Dim(1), mask.Dim(1)))
-	}
-
-	if key.Dim(1) != value.Dim(0) {
-		panic(fmt.Errorf("seq_len_k in attention operation does not match between key(%v) and value(%v)", key.Dim(1), value.Dim(0)))
-	}
-
-	if mask != nil && key.Dim(1) != mask.Dim(0) {
-		panic(fmt.Errorf("seq_len_k in attention operation does not match between key(%v) and mask(%v)", key.Dim(1), mask.Dim(0)))
-	}
-
-	if key.Dim(2) != value.Dim(2) {
-		panic(fmt.Errorf("kv_heads in attention operation does not match between key(%v) and value(%v)", key.Dim(2), value.Dim(2)))
-	}
-
-	if sdpa, ok := query.(ml.ScaledDotProductAttention); ok {
-		return sdpa.ScaledDotProductAttention(ctx, key, value, mask, scale)
-	} else {
-		kq := key.MulmatFullPrec(ctx, query)
-
-		kq = kq.Scale(ctx, scale)
-		if mask != nil {
-			kq = kq.Add(ctx, mask)
-		}
-		kq = kq.Softmax(ctx)
-
-		kqv := value.Mulmat(ctx, kq)
-		return kqv.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
-	}
-}

model/model.go

@@ -70,14 +70,14 @@ func Register(name string, f func(ml.Config) (Model, error)) {
 }
 
 // New initializes a new model instance with the provided configuration based on the metadata in the model file
-func New(modelPath string, params ml.BackendParams) (Model, error) {
+func New(modelPath string) (Model, error) {
 	r, err := os.Open(modelPath)
 	if err != nil {
 		return nil, err
 	}
 	defer r.Close()
 
-	b, err := ml.NewBackend(r, params)
+	b, err := ml.NewBackend(r)
 	if err != nil {
 		return nil, err
 	}

model/models/llama/model.go

@@ -86,8 +86,13 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten
 	k = k.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
 	v = v.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
 
-	scaleFactor := 1.0 / math.Sqrt(float64(headDim))
-	kqv := nn.Attention(ctx, q, k, v, mask, scaleFactor)
+	kq := k.MulmatFullPrec(ctx, q)
+	kq = kq.Scale(ctx, 1.0/math.Sqrt(float64(headDim)))
+	kq = kq.Add(ctx, mask)
+	kq = kq.Softmax(ctx)
+
+	kqv := v.Mulmat(ctx, kq)
+	kqv = kqv.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
 	kqv = kqv.Reshape(ctx, opts.hiddenSize, batchSize)
 
 	return sa.Output.Forward(ctx, kqv)
@@ -115,19 +120,11 @@ type Layer struct {
 	MLP           *MLP
 }
 
-func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs, outputs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
+func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
 	residual := hiddenState
 
 	hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
 	hiddenState = l.SelfAttention.Forward(ctx, hiddenState, positionIDs, cache, opts)
-
-	// In the final layer (outputs != nil), optimize by pruning to just the token positions
-	// we need logits for.
-	if outputs != nil {
-		hiddenState = hiddenState.Rows(ctx, outputs)
-		residual = residual.Rows(ctx, outputs)
-	}
-
 	hiddenState = hiddenState.Add(ctx, residual)
 	residual = hiddenState
 
@@ -147,26 +144,22 @@ func (m *Model) Forward(ctx ml.Context, opts model.Options) (ml.Tensor, error) {
 		return nil, err
 	}
 
+	hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
+
+	for i, layer := range m.Layers {
+		m.Cache.SetLayer(i)
+		hiddenState = layer.Forward(ctx, hiddenState, positions, m.Cache, m.Options)
+	}
+
+	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
+	hiddenState = m.Output.Forward(ctx, hiddenState)
+
 	outputs, err := ctx.FromIntSlice(opts.Outputs, len(opts.Outputs))
 	if err != nil {
 		return nil, err
 	}
 
-	hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
-
-	for i, layer := range m.Layers {
-		m.Cache.SetLayer(i)
-
-		var lastLayerOutputs ml.Tensor
-		if i == len(m.Layers)-1 {
-			lastLayerOutputs = outputs
-		}
-
-		hiddenState = layer.Forward(ctx, hiddenState, positions, lastLayerOutputs, m.Cache, m.Options)
-	}
-
-	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
-	return m.Output.Forward(ctx, hiddenState), nil
+	return hiddenState.Rows(ctx, outputs), nil
 }
 
 func init() {

model/models/mllama/model.go

@@ -93,13 +93,15 @@ func (m *Model) Forward(ctx ml.Context, opts model.Options) (ml.Tensor, error) {
 		return nil, err
 	}
 
+	// TODO: attention mask, cross attention mask
+	hiddenState := m.TextModel.Forward(ctx, inputs, positions, nil, crossAttentionStates, nil, m.Cache.(*kvcache.WrapperCache))
+
 	outputs, err := ctx.FromIntSlice(opts.Outputs, len(opts.Outputs))
 	if err != nil {
 		return nil, err
 	}
 
-	// TODO: attention mask, cross attention mask
-	return m.TextModel.Forward(ctx, inputs, positions, outputs, nil, crossAttentionStates, nil, m.Cache.(*kvcache.WrapperCache)), nil
+	return hiddenState.Rows(ctx, outputs), nil
 }
 
 func init() {

model/models/mllama/model_text.go

@@ -38,8 +38,13 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, hiddenState, positions, _ m
 	key = key.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
 	value = value.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
 
-	scaleFactor := 1.0 / math.Sqrt(float64(headDim))
-	attention := nn.Attention(ctx, query, key, value, mask, scaleFactor)
+	scores := key.MulmatFullPrec(ctx, query)
+	scores = scores.Scale(ctx, 1.0/math.Sqrt(float64(headDim)))
+	scores = scores.Add(ctx, mask)
+	scores = scores.Softmax(ctx)
+
+	attention := value.Mulmat(ctx, scores)
+	attention = attention.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
 	attention = attention.Reshape(ctx, opts.hiddenSize, batchSize)
 
 	return sa.Output.Forward(ctx, attention)
@@ -69,19 +74,11 @@ type TextSelfAttentionDecoderLayer struct {
 	MLP     *TextMLP
 }
 
-func (d *TextSelfAttentionDecoderLayer) Forward(ctx ml.Context, hiddenState, positions, outputs, mask, _, _ ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor {
+func (d *TextSelfAttentionDecoderLayer) Forward(ctx ml.Context, hiddenState, positions, mask, _, _ ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor {
 	residual := hiddenState
 
 	hiddenState = d.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
 	hiddenState = d.SelfAttention.Forward(ctx, hiddenState, positions, mask, cache, opts)
-
-	// In the final layer (outputs != nil), optimize by pruning to just the token positions
-	// we need logits for.
-	if outputs != nil {
-		hiddenState = hiddenState.Rows(ctx, outputs)
-		residual = residual.Rows(ctx, outputs)
-	}
-
 	hiddenState = hiddenState.Add(ctx, residual)
 	residual = hiddenState
 
@@ -107,7 +104,7 @@ func (ca *TextCrossAttention) Forward(ctx ml.Context, hiddenState, crossAttentio
 	query = query.Reshape(ctx, headDim, opts.numHeads, batchSize)
 	query = ca.QueryNorm.Forward(ctx, query, opts.eps)
 
-	var key, value, mask ml.Tensor
+	var key, value ml.Tensor
 	if crossAttentionStates != nil {
 		numVisionTokens, numTiles := crossAttentionStates.Dim(1), crossAttentionStates.Dim(2)
 
@@ -120,15 +117,19 @@ func (ca *TextCrossAttention) Forward(ctx ml.Context, hiddenState, crossAttentio
 
 		cache.Put(ctx, key, value)
 	} else {
-		key, value, mask = cache.Get(ctx)
+		key, value, _ = cache.Get(ctx)
 	}
 
 	query = query.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
 	key = key.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
 	value = value.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
 
-	scaleFactor := 1.0 / math.Sqrt(float64(headDim))
-	attention := nn.Attention(ctx, query, key, value, mask, scaleFactor)
+	scores := key.Mulmat(ctx, query)
+	scores = scores.Scale(ctx, 1.0/math.Sqrt(float64(headDim)))
+	scores = scores.Softmax(ctx)
+
+	attention := value.Mulmat(ctx, scores)
+	attention = attention.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
 	attention = attention.Reshape(ctx, opts.hiddenSize, batchSize)
 
 	return ca.Output.Forward(ctx, attention)
@@ -144,7 +145,7 @@ type TextCrossAttentionDecoderLayer struct {
 	MLPGate ml.Tensor `gguf:"cross_attn_mlp_gate"`
 }
 
-func (d *TextCrossAttentionDecoderLayer) Forward(ctx ml.Context, hiddenState, _, _, _, crossAttentionStates, crossAttentionMask ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor {
+func (d *TextCrossAttentionDecoderLayer) Forward(ctx ml.Context, hiddenState, _, _, crossAttentionStates, crossAttentionMask ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor {
 	residual := hiddenState
 
 	hiddenState = d.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
@@ -160,14 +161,14 @@ func (d *TextCrossAttentionDecoderLayer) Forward(ctx ml.Context, hiddenState, _,
 }
 
 type TextDecoderLayer interface {
-	Forward(ctx ml.Context, hiddenState, positionIDs, outputs, mask, crossAttentionStates, crossAttentionMask ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor
+	Forward(ctx ml.Context, hiddenState, positionIDs, mask, crossAttentionStates, crossAttentionMask ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor
 }
 
 type TextDecoder struct {
 	Layers []TextDecoderLayer
 }
 
-func (d *TextDecoder) Forward(ctx ml.Context, hiddenState, positionIDs, outputs, mask, crossAttentionStates, crossAttentionMask ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor {
+func (d *TextDecoder) Forward(ctx ml.Context, hiddenState, positionIDs, mask, crossAttentionStates, crossAttentionMask ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor {
 	for i, layer := range d.Layers {
 		layerType := selfAttentionLayer
 		if slices.Contains(opts.crossAttentionLayers, uint32(i)) {
@@ -178,12 +179,7 @@ func (d *TextDecoder) Forward(ctx ml.Context, hiddenState, positionIDs, outputs,
 		cache.SetLayerType(layerType)
 
 		if layerType == selfAttentionLayer || crossAttentionStates != nil || cache.UnderlyingCache().(*kvcache.EncoderCache).EncoderCached() {
-			var lastLayerOutputs ml.Tensor
-			if i == len(d.Layers)-1 {
-				lastLayerOutputs = outputs
-			}
-
-			hiddenState = layer.Forward(ctx, hiddenState, positionIDs, lastLayerOutputs, mask, crossAttentionStates, crossAttentionMask, cache, opts)
+			hiddenState = layer.Forward(ctx, hiddenState, positionIDs, mask, crossAttentionStates, crossAttentionMask, cache, opts)
 		}
 	}
 
@@ -209,9 +205,9 @@ type TextModel struct {
 	*TextModelOptions
 }
 
-func (m *TextModel) Forward(ctx ml.Context, inputIDs, positionIDs, outputs, mask, crossAttentionStates, crossAttentionMask ml.Tensor, cache *kvcache.WrapperCache) ml.Tensor {
+func (m *TextModel) Forward(ctx ml.Context, inputIDs, positionIDs, mask, crossAttentionStates, crossAttentionMask ml.Tensor, cache *kvcache.WrapperCache) ml.Tensor {
 	hiddenState := m.TokenEmbedding.Forward(ctx, inputIDs)
-	hiddenState = m.Transformer.Forward(ctx, hiddenState, positionIDs, outputs, mask, crossAttentionStates, crossAttentionMask, cache, m.TextModelOptions)
+	hiddenState = m.Transformer.Forward(ctx, hiddenState, positionIDs, mask, crossAttentionStates, crossAttentionMask, cache, m.TextModelOptions)
 	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
 	return m.Output.Forward(ctx, hiddenState)
 }

runner/ollamarunner/runner.go

@@ -25,7 +25,6 @@ import (
 	"golang.org/x/sync/semaphore"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/ml"
 	"github.com/ollama/ollama/model"
 	"github.com/ollama/ollama/runner/common"
 	"github.com/ollama/ollama/sample"
@@ -802,7 +801,6 @@ func (m *multiLPath) String() string {
 
 func (s *Server) loadModel(
 	mpath string,
-	params ml.BackendParams,
 	lpath multiLPath,
 	parallel int,
 	kvCacheType string,
@@ -810,12 +808,12 @@ func (s *Server) loadModel(
 	multiUserCache bool,
 ) {
 	var err error
-	s.model, err = model.New(mpath, params)
+	s.model, err = model.New(mpath)
 	if err != nil {
 		panic(err)
 	}
 
-	slog.Info("system", "info", s.model.Backend().SystemInfo(), "threads", params.NumThreads)
+	slog.Info("system", "info", s.model.Backend().SystemInfo() /* "threads", *threads */)
 
 	// TODO(jessegross): LoRA loading
 	if lpath.String() != "" {
@@ -845,17 +843,17 @@ func Execute(args []string) error {
 	mpath := fs.String("model", "", "Path to model binary file")
 	parallel := fs.Int("parallel", 1, "Number of sequences to handle simultaneously")
 	batchSize := fs.Int("batch-size", 512, "Batch size")
-	numGPULayers := fs.Int("n-gpu-layers", 0, "Number of layers to offload to GPU")
-	mainGPU := fs.Int("main-gpu", 0, "Main GPU")
+	_ = fs.Int("n-gpu-layers", 0, "Number of layers to offload to GPU")
+	_ = fs.Int("main-gpu", 0, "Main GPU")
 	_ = fs.Bool("flash-attn", false, "Enable flash attention")
 	kvSize := fs.Int("ctx-size", 2048, "Context (or KV cache) size")
 	kvCacheType := fs.String("kv-cache-type", "", "quantization type for KV cache (default: f16)")
 	port := fs.Int("port", 8080, "Port to expose the server on")
-	threads := fs.Int("threads", runtime.NumCPU(), "Number of threads to use during generation")
+	_ = fs.Int("threads", runtime.NumCPU(), "Number of threads to use during generation")
 	verbose := fs.Bool("verbose", false, "verbose output (default: disabled)")
 	_ = fs.Bool("no-mmap", false, "do not memory-map model (slower load but may reduce pageouts if not using mlock)")
 	_ = fs.Bool("mlock", false, "force system to keep model in RAM rather than swapping or compressing")
-	tensorSplit := fs.String("tensor-split", "", "fraction of the model to offload to each GPU, comma-separated list of proportions")
+	_ = fs.String("tensor-split", "", "fraction of the model to offload to each GPU, comma-separated list of proportions")
 	multiUserCache := fs.Bool("multiuser-cache", false, "optimize input cache algorithm for multiple users")
 
 	var lpaths multiLPath
@@ -892,11 +890,15 @@ func Execute(args []string) error {
 	}
 
 	// TODO(jessegross): Parameters that need to be implemented:
+	//	n-gpu-layers
+	//	main-gpu
 	//	flash-attn
+	//	threads
 	//	no-mmap
 	//	mlock
+	//	tensor-split
 
-	var tensorSplitFloats []float32
+	/*var tensorSplitFloats []float32
 	if *tensorSplit != "" {
 		stringFloats := regexp.MustCompile(",").Split(*tensorSplit, -1)
 
@@ -905,17 +907,10 @@ func Execute(args []string) error {
 			f, _ := strconv.ParseFloat(s, 32)
 			tensorSplitFloats = append(tensorSplitFloats, float32(f))
 		}
-	}
-
-	params := ml.BackendParams{
-		NumThreads:   *threads,
-		NumGPULayers: *numGPULayers,
-		MainGPU:      *mainGPU,
-		TensorSplit:  tensorSplitFloats,
-	}
+	}*/
 
 	server.ready.Add(1)
-	go server.loadModel(*mpath, params, lpaths, *parallel, *kvCacheType, *kvSize, *multiUserCache)
+	go server.loadModel(*mpath, lpaths, *parallel, *kvCacheType, *kvSize, *multiUserCache)
 
 	server.cond = sync.NewCond(&server.mu)
 

server/routes.go

@@ -1127,72 +1127,54 @@ func allowedHostsMiddleware(addr net.Addr) gin.HandlerFunc {
 }
 
 func (s *Server) GenerateRoutes() http.Handler {
-	corsConfig := cors.DefaultConfig()
-	corsConfig.AllowWildcard = true
-	corsConfig.AllowBrowserExtensions = true
-	corsConfig.AllowHeaders = []string{
-		"Authorization",
-		"Content-Type",
-		"User-Agent",
-		"Accept",
-		"X-Requested-With",
-
-		// OpenAI compatibility headers
-		"x-stainless-lang",
-		"x-stainless-package-version",
-		"x-stainless-os",
-		"x-stainless-arch",
-		"x-stainless-retry-count",
-		"x-stainless-runtime",
-		"x-stainless-runtime-version",
-		"x-stainless-async",
-		"x-stainless-helper-method",
-		"x-stainless-poll-helper",
-		"x-stainless-custom-poll-interval",
-		"x-stainless-timeout",
+	config := cors.DefaultConfig()
+	config.AllowWildcard = true
+	config.AllowBrowserExtensions = true
+	config.AllowHeaders = []string{"Authorization", "Content-Type", "User-Agent", "Accept", "X-Requested-With"}
+	openAIProperties := []string{"lang", "package-version", "os", "arch", "retry-count", "runtime", "runtime-version", "async", "helper-method", "poll-helper", "custom-poll-interval"}
+	for _, prop := range openAIProperties {
+		config.AllowHeaders = append(config.AllowHeaders, "x-stainless-"+prop)
 	}
-	corsConfig.AllowOrigins = envconfig.AllowedOrigins()
+	config.AllowOrigins = envconfig.Origins()
 
 	r := gin.Default()
 	r.Use(
-		cors.New(corsConfig),
+		cors.New(config),
 		allowedHostsMiddleware(s.addr),
 	)
 
-	// General
-	r.HEAD("/", func(c *gin.Context) { c.String(http.StatusOK, "Ollama is running") })
-	r.GET("/", func(c *gin.Context) { c.String(http.StatusOK, "Ollama is running") })
-	r.HEAD("/api/version", func(c *gin.Context) { c.JSON(http.StatusOK, gin.H{"version": version.Version}) })
-	r.GET("/api/version", func(c *gin.Context) { c.JSON(http.StatusOK, gin.H{"version": version.Version}) })
-
-	// Local model cache management
 	r.POST("/api/pull", s.PullHandler)
-	r.POST("/api/push", s.PushHandler)
-	r.DELETE("/api/delete", s.DeleteHandler)
-	r.HEAD("/api/tags", s.ListHandler)
-	r.GET("/api/tags", s.ListHandler)
-	r.POST("/api/show", s.ShowHandler)
-
-	// Create
-	r.POST("/api/create", s.CreateHandler)
-	r.POST("/api/blobs/:digest", s.CreateBlobHandler)
-	r.HEAD("/api/blobs/:digest", s.HeadBlobHandler)
-	r.POST("/api/copy", s.CopyHandler)
-
-	// Inference
-	r.GET("/api/ps", s.PsHandler)
 	r.POST("/api/generate", s.GenerateHandler)
 	r.POST("/api/chat", s.ChatHandler)
 	r.POST("/api/embed", s.EmbedHandler)
 	r.POST("/api/embeddings", s.EmbeddingsHandler)
+	r.POST("/api/create", s.CreateHandler)
+	r.POST("/api/push", s.PushHandler)
+	r.POST("/api/copy", s.CopyHandler)
+	r.DELETE("/api/delete", s.DeleteHandler)
+	r.POST("/api/show", s.ShowHandler)
+	r.POST("/api/blobs/:digest", s.CreateBlobHandler)
+	r.HEAD("/api/blobs/:digest", s.HeadBlobHandler)
+	r.GET("/api/ps", s.PsHandler)
 
-	// Inference (OpenAI compatibility)
+	// Compatibility endpoints
 	r.POST("/v1/chat/completions", openai.ChatMiddleware(), s.ChatHandler)
 	r.POST("/v1/completions", openai.CompletionsMiddleware(), s.GenerateHandler)
 	r.POST("/v1/embeddings", openai.EmbeddingsMiddleware(), s.EmbedHandler)
 	r.GET("/v1/models", openai.ListMiddleware(), s.ListHandler)
 	r.GET("/v1/models/:model", openai.RetrieveMiddleware(), s.ShowHandler)
 
+	for _, method := range []string{http.MethodGet, http.MethodHead} {
+		r.Handle(method, "/", func(c *gin.Context) {
+			c.String(http.StatusOK, "Ollama is running")
+		})
+
+		r.Handle(method, "/api/tags", s.ListHandler)
+		r.Handle(method, "/api/version", func(c *gin.Context) {
+			c.JSON(http.StatusOK, gin.H{"version": version.Version})
+		})
+	}
+
 	return r
 }
 

server/sched.go

@@ -179,7 +179,7 @@ func (s *Scheduler) processPending(ctx context.Context) {
 						if allReliable {
 							// HACK
 							os.Setenv("OLLAMA_MAX_LOADED_MODELS", strconv.Itoa(defaultModelsPerGPU*len(gpus)))
-							slog.Debug("updating default concurrency", "OLLAMA_MAX_LOADED_MODELS", envconfig.MaxRunners(), "gpu_count", len(gpus))
+							slog.Debug("updating default concurrency", "OLLAMA_MAX_LOADED_MODELS", envconfig.MaxRunners, "gpu_count", len(gpus))
 						} else {
 							// HACK
 							os.Setenv("OLLAMA_MAX_LOADED_MODELS", strconv.Itoa(len(gpus)))