fix: convert f64 to f32 and i64 to i32 when loading weights

ggml_extend.hpp

@@ -840,6 +840,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_attention_ext(struct ggml_context*
 
     float scale = (1.0f / sqrt((float)d_head));
 
+    int kv_pad = 0;
     //if (flash_attn) {
     //    LOG_DEBUG("attention_ext L_q:%d L_k:%d n_head:%d C:%d d_head:%d N:%d", L_q, L_k, n_head, C, d_head, N);
     //}
@@ -847,11 +848,26 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_attention_ext(struct ggml_context*
     GGML_ASSERT(((L_k % 256 == 0) && L_q == L_k) || !(L_k % 256 == 0));
 
     bool can_use_flash_attn = true;
+    can_use_flash_attn = can_use_flash_attn && (
+        d_head == 64 ||
+        d_head == 80 ||
+        d_head == 96 ||
+        d_head == 112 ||
+        d_head == 128 ||
+        d_head == 256
+    );
+#if 0
     can_use_flash_attn      = can_use_flash_attn && L_k % 256 == 0;
-    can_use_flash_attn      = can_use_flash_attn && d_head % 64 == 0;  // double check
+#else
-
+    if (can_use_flash_attn && L_k % 256 != 0) {
-    // cuda max d_head seems to be 256, cpu does seem to work with 512
+        // TODO(Green-Sky): might be worth just padding by default
-    can_use_flash_attn = can_use_flash_attn && d_head <= 256;  // double check
+        if (L_k == 77 || L_k == 4208 || L_k == 3952) {
+            kv_pad = GGML_PAD(L_k, 256) - L_k;
+        } else {
+            can_use_flash_attn = false;
+        }
+    }
+#endif
 
     if (mask != nullptr) {
         // TODO(Green-Sky): figure out if we can bend t5 to work too
@@ -864,11 +880,18 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_attention_ext(struct ggml_context*
     ggml_tensor* kqv = nullptr;
     // GGML_ASSERT((flash_attn && can_use_flash_attn) || !flash_attn);
     if (can_use_flash_attn && flash_attn) {
-        // LOG_DEBUG("using flash attention");
+        //LOG_DEBUG(" uses flash attention");
+        if (kv_pad != 0) {
+            //LOG_DEBUG(" padding k and v dim1 by %d", kv_pad);
+            k = ggml_pad(ctx, k, 0, kv_pad, 0, 0);
+        }
         k = ggml_cast(ctx, k, GGML_TYPE_F16);
 
         v = ggml_cont(ctx, ggml_permute(ctx, v, 0, 2, 1, 3));  // [N, n_head, L_k, d_head]
         v = ggml_reshape_3d(ctx, v, d_head, L_k, n_head * N);  // [N * n_head, L_k, d_head]
+        if (kv_pad != 0) {
+            v = ggml_pad(ctx, v, 0, kv_pad, 0, 0);
+        }
         v = ggml_cast(ctx, v, GGML_TYPE_F16);
 
         if (mask != nullptr) {

model.cpp

@@ -815,6 +815,7 @@ void f8_e4m3_to_f16_vec(uint8_t* src, uint16_t* dst, int64_t n) {
         dst[i] = f8_e4m3_to_f16(src[i]);
     }
 }
+
 void f8_e5m2_to_f16_vec(uint8_t* src, uint16_t* dst, int64_t n) {
     // support inplace op
     for (int64_t i = n - 1; i >= 0; i--) {
@@ -822,6 +823,20 @@ void f8_e5m2_to_f16_vec(uint8_t* src, uint16_t* dst, int64_t n) {
     }
 }
 
+void f64_to_f32_vec(double* src, float* dst, int64_t n) {
+    // support inplace op
+    for (int64_t i = 0; i < n; i++) {
+        dst[i] = (float)src[i];
+    }
+}
+
+void i64_to_i32_vec(int64_t* src, int32_t* dst, int64_t n) {
+    // support inplace op
+    for (int64_t i = 0; i < n; i++) {
+        dst[i] = (int32_t)src[i];
+    }
+}
+
 void convert_tensor(void* src,
                     ggml_type src_type,
                     void* dst,
@@ -1057,13 +1072,13 @@ ggml_type str_to_ggml_type(const std::string& dtype) {
     } else if (dtype == "F32") {
         ttype = GGML_TYPE_F32;
     } else if (dtype == "F64") {
-        ttype = GGML_TYPE_F64;
+        ttype = GGML_TYPE_F32;
     } else if (dtype == "F8_E4M3") {
         ttype = GGML_TYPE_F16;
     } else if (dtype == "F8_E5M2") {
         ttype = GGML_TYPE_F16;
     } else if (dtype == "I64") {
-        ttype = GGML_TYPE_I64;
+        ttype = GGML_TYPE_I32;
     }
     return ttype;
 }
@@ -1185,6 +1200,14 @@ bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const
             tensor_storage.is_f8_e5m2 = true;
             // f8 -> f16
             GGML_ASSERT(tensor_storage.nbytes() == tensor_data_size * 2);
+        } else if (dtype == "F64") {
+            tensor_storage.is_f64 = true;
+            // f64 -> f32
+            GGML_ASSERT(tensor_storage.nbytes() * 2 == tensor_data_size);
+        } else if (dtype == "I64") {
+            tensor_storage.is_i64 = true;
+            // i64 -> i32
+            GGML_ASSERT(tensor_storage.nbytes() * 2 == tensor_data_size);
         } else {
             GGML_ASSERT(tensor_storage.nbytes() == tensor_data_size);
         }
@@ -1945,7 +1968,12 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
                 // for the CPU and Metal backend, we can copy directly into the tensor
                 if (tensor_storage.type == dst_tensor->type) {
                     GGML_ASSERT(ggml_nbytes(dst_tensor) == tensor_storage.nbytes());
+                    if (tensor_storage.is_f64 || tensor_storage.is_i64) {
+                        read_buffer.resize(tensor_storage.nbytes_to_read());
+                        read_data(tensor_storage, (char*)read_buffer.data(), nbytes_to_read);
+                    } else {
                         read_data(tensor_storage, (char*)dst_tensor->data, nbytes_to_read);
+                    }
 
                     if (tensor_storage.is_bf16) {
                         // inplace op
@@ -1956,9 +1984,13 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
                     } else if (tensor_storage.is_f8_e5m2) {
                         // inplace op
                         f8_e5m2_to_f16_vec((uint8_t*)dst_tensor->data, (uint16_t*)dst_tensor->data, tensor_storage.nelements());
+                    } else if (tensor_storage.is_f64) {
+                        f64_to_f32_vec((double*)read_buffer.data(), (float*)dst_tensor->data, tensor_storage.nelements());
+                    } else if (tensor_storage.is_i64) {
+                        i64_to_i32_vec((int64_t*)read_buffer.data(), (int32_t*)dst_tensor->data, tensor_storage.nelements());
                     }
                 } else {
-                    read_buffer.resize(tensor_storage.nbytes());
+                    read_buffer.resize(std::max(tensor_storage.nbytes(), tensor_storage.nbytes_to_read()));
                     read_data(tensor_storage, (char*)read_buffer.data(), nbytes_to_read);
 
                     if (tensor_storage.is_bf16) {
@@ -1970,13 +2002,19 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
                     } else if (tensor_storage.is_f8_e5m2) {
                         // inplace op
                         f8_e5m2_to_f16_vec((uint8_t*)read_buffer.data(), (uint16_t*)read_buffer.data(), tensor_storage.nelements());
+                    } else if (tensor_storage.is_f64) {
+                        // inplace op
+                        f64_to_f32_vec((double*)read_buffer.data(), (float*)read_buffer.data(), tensor_storage.nelements());
+                    } else if (tensor_storage.is_i64) {
+                        // inplace op
+                        i64_to_i32_vec((int64_t*)read_buffer.data(), (int32_t*)read_buffer.data(), tensor_storage.nelements());
                     }
 
                     convert_tensor((void*)read_buffer.data(), tensor_storage.type, dst_tensor->data,
                                    dst_tensor->type, (int)tensor_storage.nelements() / (int)tensor_storage.ne[0], (int)tensor_storage.ne[0]);
                 }
             } else {
-                read_buffer.resize(tensor_storage.nbytes());
+                read_buffer.resize(std::max(tensor_storage.nbytes(), tensor_storage.nbytes_to_read()));
                 read_data(tensor_storage, (char*)read_buffer.data(), nbytes_to_read);
 
                 if (tensor_storage.is_bf16) {
@@ -1988,6 +2026,12 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, ggml_backend
                 } else if (tensor_storage.is_f8_e5m2) {
                     // inplace op
                     f8_e5m2_to_f16_vec((uint8_t*)read_buffer.data(), (uint16_t*)read_buffer.data(), tensor_storage.nelements());
+                } else if (tensor_storage.is_f64) {
+                    // inplace op
+                    f64_to_f32_vec((double*)read_buffer.data(), (float*)read_buffer.data(), tensor_storage.nelements());
+                } else if (tensor_storage.is_i64) {
+                    // inplace op
+                    i64_to_i32_vec((int64_t*)read_buffer.data(), (int32_t*)read_buffer.data(), tensor_storage.nelements());
                 }
 
                 if (tensor_storage.type == dst_tensor->type) {

model.h

@@ -102,6 +102,8 @@ struct TensorStorage {
     bool is_bf16            = false;
     bool is_f8_e4m3         = false;
     bool is_f8_e5m2         = false;
+    bool is_f64             = false;
+    bool is_i64             = false;
     int64_t ne[SD_MAX_DIMS] = {1, 1, 1, 1, 1};
     int n_dims              = 0;
 
@@ -133,6 +135,8 @@ struct TensorStorage {
     int64_t nbytes_to_read() const {
         if (is_bf16 || is_f8_e4m3 || is_f8_e5m2) {
             return nbytes() / 2;
+        } else if (is_f64 || is_i64) {
+            return nbytes() * 2;
         } else {
             return nbytes();
         }
@@ -183,6 +187,10 @@ struct TensorStorage {
             type_name = "f8_e4m3";
         } else if (is_f8_e5m2) {
             type_name = "f8_e5m2";
+        } else if (is_f64) {
+            type_name = "f64";
+        } else if (is_i64) {
+            type_name = "i64";
         }
         ss << name << " | " << type_name << " | ";
         ss << n_dims << " [";