f32 add all tests passing

ggml/src/ggml-webgpu/ggml-webgpu.cpp

@@ -451,6 +451,16 @@ static void ggml_webgpu_mul_mat(webgpu_context & ctx, ggml_tensor * src0, ggml_t
     ggml_backend_webgpu_build_and_enqueue(ctx, ctx->mul_mat_pipeline, params, entries, wg_x);
 }
 
+// sample test
+// ADD(type=f32, ne=[10,5,4,3], nr=[2,1,1,1], nf=1)
+    // ne: number of elements in each dimension of tensor b
+    // nr: number of repetitions in each dimension
+        // tensor b is the smaller tensor, and is broadcasted with repetitions to match the size of a 
+        // broadcasted with ne * nr
+            // 10*2, 5*1, 4*1, 3*1 = [20, 5, 4, 3] is the shape of dst and a
+            // essentially, if nr[x] is > 1, that dimension of b is repeated 
+    // nf: number of fused operations (1 means singular addition)
+
 // adds src0 and src1 and puts in dst
 static void ggml_webgpu_add(webgpu_context & ctx, ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst) {
     // each tensor in GGML is stored inside a buffer on the GPU
@@ -464,15 +474,13 @@ static void ggml_webgpu_add(webgpu_context & ctx, ggml_tensor * src0, ggml_tenso
     src0_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
 
     size_t src1_offset = ggml_backend_webgpu_tensor_offset(src1);
-    // assumes power of 2 offset alignment
     size_t src1_misalignment = src1_offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
-    // align to minimum offset alignment
     src1_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
 
     size_t dst_offset = ggml_backend_webgpu_tensor_offset(dst);
     size_t dst_misalignment = dst_offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
     dst_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
-    
+
     // set up parameters
     std::vector<uint32_t> params = {
         // number of elements-- determines how many threads to dispatch (one for each addition operation)
@@ -480,11 +488,13 @@ static void ggml_webgpu_add(webgpu_context & ctx, ggml_tensor * src0, ggml_tenso
         
         // even though tensors are 4d, the actual data is stored linearly
         // stride = how many elements (or bytes) we must skip in memory to move from one value to another along a certain dimension
-            // i.e.
+            // i.e. tensor: [5, 6, 3, 2], ggml_type_size: 4 (each number is 4 bytes) 
-            // nb[0] = 1                   // each element is next to the previous
+                // (nb = number of bytes to skip for each element (stride))
-            // nb[1] = nb[0] * ne[0] = 5   // to move to next row, skip 5 elements
+                // (ne = number of elements in that dimension)
-            // nb[2] = nb[1] * ne[1] = 20  // to next matrix, skip 20 elements
+            // nb[0] = 4                                // each element is next to the previous, so only 4 bytes in between
-            // nb[3] = nb[2] * ne[2] = 60  // to next batch, skip 60 elements
+            // nb[1] = nb[0] * ne[0] = 4 * 5 = 20       // to move to next row, skip 20 bytes
+            // nb[2] = nb[1] * ne[1] = 20 * 6 = 120     // to next matrix, skip 120 elements
+            // nb[3] = nb[2] * ne[2] = 120 * 3 = 360    // to next batch, skip 60 elements
 
         // calculate element strides for each tensor
         (uint32_t) (src0->nb[0] / ggml_type_size(src0->type)),
@@ -502,16 +512,24 @@ static void ggml_webgpu_add(webgpu_context & ctx, ggml_tensor * src0, ggml_tenso
         (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)),
         (uint32_t) (dst->nb[3] / ggml_type_size(dst->type)),
 
-        // number of elements in each dimension
+        // number of elements in each dimension of larger tensors (src0 and dst)
         (uint32_t) dst->ne[0],
         (uint32_t) dst->ne[1],
         (uint32_t) dst->ne[2],
         (uint32_t) dst->ne[3],
 
+        // number of elements in each dimension of smaller tensor to be broadcasted (src1)
+        (uint32_t) src1->ne[0],
+        (uint32_t) src1->ne[1],
+        (uint32_t) src1->ne[2],
+        (uint32_t) src1->ne[3],
+
         // offsets in terms of elements instead of bytes
         (uint32_t) (src0_misalignment / ggml_type_size(src0->type)),
         (uint32_t) (src1_misalignment / ggml_type_size(src1->type)),
         (uint32_t) (dst_misalignment / ggml_type_size(dst->type)),
+
+        
     };
 
     // bind group = groups together several GPU resources that shaders will use (e.g., buffers holding tensor data)

ggml/src/ggml-webgpu/wgsl-shaders/add.wgsl

@@ -10,7 +10,7 @@ var<storage, read_write> src1: array<f32>;
 var<storage, read_write> dst: array<f32>;
 
 struct Params {
-    ne: u32,             // total number of elements
+    ne: u32,
 
     stride_src0_0: u32,
     stride_src0_1: u32,
@@ -27,10 +27,15 @@ struct Params {
     stride_dst_2: u32,
     stride_dst_3: u32,
 
-    ne0: u32,
+    a_ne0: u32,
-    ne1: u32,
+    a_ne1: u32,
-    ne2: u32,
+    a_ne2: u32,
-    ne3: u32,
+    a_ne3: u32,
+
+    b_ne0: u32,
+    b_ne1: u32,
+    b_ne2: u32,
+    b_ne3: u32,
 
     // offsets in elements
     offset_src0: u32,
@@ -48,31 +53,41 @@ fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
         return;
     }
 
-    var i = gid.x; // i = thread id
+    // i = thread id, ranges from 0 --> total ne - 1 
+    // represents the position in the flat array a we are adding with array b
+    var i = gid.x;  
 
-    // compute indexes for each dimension of the tensor 
+    // given the index of linear a, we want to compute the 4d index [a_i0, a_i1, a_i2, a_i3]
-    let i3 = i / (params.ne2 * params.ne1 * params.ne0);
+    // we need this because tensor a and b are different shapes 
-    i = i % (params.ne2 * params.ne1 * params.ne0);
+    // so the same linear index won't work for b, and we can only compute b's linear index from the 4d index of a
+ 
+    let a_i3 = i / (params.a_ne2 * params.a_ne1 * params.a_ne0);
+    i = i % (params.a_ne2 * params.a_ne1 * params.a_ne0);
 
-    let i2 = i / (params.ne1 * params.ne0);
+    let a_i2 = i / (params.a_ne1 * params.a_ne0);
-    i = i % (params.ne1 * params.ne0);
+    i = i % (params.a_ne1 * params.a_ne0);
 
-    let i1 = i / params.ne0;
+    let a_i1 = i / params.a_ne0;
 
-    let i0 = i % params.ne0;
+    let a_i0 = i % params.a_ne0;
-
-    // compute indexes for position in each flat array
-    let src0_idx = i0 * params.stride_src0_0 + i1 * params.stride_src0_1 +
-                   i2 * params.stride_src0_2 + i3 * params.stride_src0_3;
-
-    let src1_idx = i0 * params.stride_src1_0 + i1 * params.stride_src1_1 +
-                   i2 * params.stride_src1_2 + i3 * params.stride_src1_3;
-
-    let dst_idx = i0 * params.stride_dst_0 + i1 * params.stride_dst_1 +
-                  i2 * params.stride_dst_2 + i3 * params.stride_dst_3;
 
 
-    // dst[dst_idx] = src0[src0_idx] + src1[src1_idx];
+    // handle repetition of b 
+        // index loops back to the beginning and repeats after elements are exhausted = modulo
+    let b_i0 = a_i0 % params.b_ne0;
+    let b_i1 = a_i1 % params.b_ne1;
+    let b_i2 = a_i2 % params.b_ne2;
+    let b_i3 = a_i3 % params.b_ne3;
 
-    dst[params.offset_dst + dst_idx] = src0[params.offset_src0 + src0_idx] + src1[params.offset_src1 + src1_idx];
+
+    // compute index for position in b's flat array
+    let src1_idx = b_i0 * params.stride_src1_0 +
+                b_i1 * params.stride_src1_1 +
+                b_i2 * params.stride_src1_2 +
+                b_i3 * params.stride_src1_3;
+
+    // actual addition operation, now that the indexes are all figured out
+    // ensuring that the offsets are included
+    // gid.x used for flat indexing into dst and a, since variable i was modified during calcs
+    dst[params.offset_dst + gid.x] = src0[params.offset_src0 + gid.x] + src1[params.offset_src1 + src1_idx];
 }