Support pure float16 add/sub/mul/div operations in the CUDA (and CPU) backend (ggml...

* Support float16-to-float16 add/sub/mul/div operations in the CUDA backend

* Add fp16 support for add/sub/mul/div on the CPU backend

* Add test cases for fp16 add/sub/mul/div

diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 723253495a70e26eafdcf096a9424d64b88bb7c0..33ab5e9c6e766c0c2589eda46ed726e5b8903b63 100644 (file)
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -1415,15 +1415,35 @@ inline static void ggml_vec_cpy_i32(const int n, int32_t * y, const int32_t * x)
 inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
 inline static void ggml_vec_set_bf16(const int n, ggml_bf16_t * x, const ggml_bf16_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
 inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i] + y[i]; }
+inline static void ggml_vec_add_f16 (const int n, ggml_fp16_t * z, const ggml_fp16_t * x, const ggml_fp16_t * y) {
+    for (int i = 0; i < n; ++i) {
+        z[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(x[i]) + GGML_FP16_TO_FP32(y[i]));
+    }
+}
 inline static void ggml_vec_add1_f32(const int n, float * z, const float * x, const float   v) { for (int i = 0; i < n; ++i) z[i]  = x[i] + v;    }
 inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x)                  { for (int i = 0; i < n; ++i) y[i] += x[i];        }
 inline static void ggml_vec_acc1_f32(const int n, float * y, const float   v)                  { for (int i = 0; i < n; ++i) y[i] += v;           }
 inline static void ggml_vec_sub_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i] - y[i]; }
+inline static void ggml_vec_sub_f16 (const int n, ggml_fp16_t * z, const ggml_fp16_t * x, const ggml_fp16_t * y) {
+    for (int i = 0; i < n; ++i) {
+        z[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(x[i]) - GGML_FP16_TO_FP32(y[i]));
+    }
+}
 inline static void ggml_vec_set_f32 (const int n, float * x, const float   v)                  { for (int i = 0; i < n; ++i) x[i]  = v;           }
 inline static void ggml_vec_cpy_f32 (const int n, float * y, const float * x)                  { for (int i = 0; i < n; ++i) y[i]  = x[i];        }
 inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x)                  { for (int i = 0; i < n; ++i) y[i]  = -x[i];       }
 inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i]*y[i];   }
+inline static void ggml_vec_mul_f16 (const int n, ggml_fp16_t * z, const ggml_fp16_t * x, const ggml_fp16_t * y) {
+    for (int i = 0; i < n; ++i) {
+        z[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(x[i]) * GGML_FP16_TO_FP32(y[i]));
+    }
+}
 inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i]/y[i];   }
+inline static void ggml_vec_div_f16 (const int n, ggml_fp16_t * z, const ggml_fp16_t * x, const ggml_fp16_t * y) {
+    for (int i = 0; i < n; ++i) {
+        z[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(x[i]) / GGML_FP16_TO_FP32(y[i]));
+    }
+}
 
 static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc) {
    assert(nrc == 1);
@@ -4379,7 +4399,7 @@ static void ggml_compute_forward_add_f16_f16(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
-    GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
+    GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -4404,17 +4424,22 @@ static void ggml_compute_forward_add_f16_f16(
 
     if (nb10 == sizeof(ggml_fp16_t)) {
         for (int ir = ir0; ir < ir1; ++ir) {
-            // src0, src1 and dst are same shape => same indices
-            const int i3 = ir/(ne2*ne1);
-            const int i2 = (ir - i3*ne2*ne1)/ne1;
-            const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+            // src1 is broadcastable across src0 and dst in i1, i2, i3
+            const int64_t i03 = ir/(ne02*ne01);
+            const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
+            const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
 
-            ggml_fp16_t * dst_ptr  = (ggml_fp16_t *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1);
-            ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
-            ggml_fp16_t * src1_ptr = (ggml_fp16_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11);
+            const int64_t i13 = i03 % ne13;
+            const int64_t i12 = i02 % ne12;
+            const int64_t i11 = i01 % ne11;
+            const int64_t nr0 = ne00 / ne10;
 
-            for (int i = 0; i < ne0; i++) {
-                dst_ptr[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(src0_ptr[i]) + GGML_FP16_TO_FP32(src1_ptr[i]));
+            ggml_fp16_t * dst_ptr  = (ggml_fp16_t *) ((char *) dst->data  + i03*nb3  + i02*nb2  + i01*nb1 );
+            ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
+            ggml_fp16_t * src1_ptr = (ggml_fp16_t *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
+
+            for (int64_t r = 0; r < nr0; ++r) {
+                ggml_vec_add_f16(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr);
             }
         }
     }
@@ -5202,6 +5227,62 @@ static void ggml_compute_forward_sub_f32(
     }
 }
 
+static void ggml_compute_forward_sub_f16(
+    const struct ggml_compute_params * params,
+    struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    assert(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr  = ggml_nrows(src0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F16);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F16);
+
+    GGML_ASSERT( nb0 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    if (nb10 == sizeof(ggml_fp16_t)) {
+        for (int ir = ir0; ir < ir1; ++ir) {
+            // src1 is broadcastable across src0 and dst in i1, i2, i3
+            const int64_t i03 = ir/(ne02*ne01);
+            const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
+            const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
+
+            const int64_t i13 = i03 % ne13;
+            const int64_t i12 = i02 % ne12;
+            const int64_t i11 = i01 % ne11;
+            const int64_t nr0 = ne00 / ne10;
+
+            ggml_fp16_t * dst_ptr  = (ggml_fp16_t *) ((char *) dst->data  + i03*nb3  + i02*nb2  + i01*nb1 );
+            ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
+            ggml_fp16_t * src1_ptr = (ggml_fp16_t *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
+
+            for (int64_t r = 0; r < nr0; ++r) {
+                ggml_vec_sub_f16(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr);
+            }
+        }
+    } else {
+        // src1 is not contiguous
+        GGML_ABORT("unimplemented error");
+    }
+}
+
 static void ggml_compute_forward_sub(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst) {
@@ -5213,6 +5294,10 @@ static void ggml_compute_forward_sub(
             {
                 ggml_compute_forward_sub_f32(params, dst);
             } break;
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_sub_f16(params, dst);
+            } break;
         default:
             {
                 GGML_ABORT("fatal error");
@@ -5293,6 +5378,55 @@ static void ggml_compute_forward_mul_f32(
     }
 }
 
+static void ggml_compute_forward_mul_f16(
+    const struct ggml_compute_params * params,
+    struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int64_t nr = ggml_nrows(src0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F16);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F16);
+
+    GGML_ASSERT( nb0 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+
+    if (nb10 == sizeof(ggml_fp16_t)) {
+        for (int64_t ir = ith; ir < nr; ir += nth) {
+            // src0 and dst are same shape => same indices
+            const int64_t i03 = ir/(ne02*ne01);
+            const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
+            const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
+
+            const int64_t i13 = i03 % ne13;
+            const int64_t i12 = i02 % ne12;
+            const int64_t i11 = i01 % ne11;
+            const int64_t nr0 = ne00 / ne10;
+
+            ggml_fp16_t * dst_ptr  = (ggml_fp16_t *) ((char *) dst->data  + i03*nb3  + i02*nb2  + i01*nb1 );
+            ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
+            ggml_fp16_t * src1_ptr = (ggml_fp16_t *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
+
+            for (int64_t r = 0 ; r < nr0; ++r) {
+                ggml_vec_mul_f16(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr);
+            }
+        }
+    } else {
+        // src1 is not contiguous
+        GGML_ABORT("unimplemented error");
+    }
+}
+
 static void ggml_compute_forward_mul(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst) {
@@ -5300,13 +5434,17 @@ static void ggml_compute_forward_mul(
     const struct ggml_tensor * src0 = dst->src[0];
     const struct ggml_tensor * src1 = dst->src[1];
 
-    GGML_ASSERT(src1->type == GGML_TYPE_F32 && "only f32 src1 supported for now");
+    GGML_ASSERT((src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16) && "only f32/f16 src1 supported for now");
 
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
                 ggml_compute_forward_mul_f32(params, dst);
             } break;
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_mul_f16(params, dst);
+            } break;
         default:
             {
                 GGML_ABORT("fatal error");
@@ -5387,6 +5525,55 @@ static void ggml_compute_forward_div_f32(
     }
 }
 
+static void ggml_compute_forward_div_f16(
+    const struct ggml_compute_params * params,
+    struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int64_t nr = ggml_nrows(src0);
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F16);
+    GGML_ASSERT(src1->type == GGML_TYPE_F16);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F16);
+
+    GGML_ASSERT( nb0 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+
+    if (nb10 == sizeof(ggml_fp16_t)) {
+        for (int64_t ir = ith; ir < nr; ir += nth) {
+            // src0 and dst are same shape => same indices
+            const int64_t i03 = ir/(ne02*ne01);
+            const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
+            const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
+
+            const int64_t i13 = i03 % ne13;
+            const int64_t i12 = i02 % ne12;
+            const int64_t i11 = i01 % ne11;
+            const int64_t nr0 = ne00 / ne10;
+
+            ggml_fp16_t * dst_ptr  = (ggml_fp16_t *) ((char *) dst->data  + i03*nb3  + i02*nb2  + i01*nb1 );
+            ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
+            ggml_fp16_t * src1_ptr = (ggml_fp16_t *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
+
+            for (int64_t r = 0; r < nr0; ++r) {
+                ggml_vec_div_f16(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr);
+            }
+        }
+    } else {
+        // src1 is not contiguous
+        GGML_ABORT("unimplemented error");
+    }
+}
+
 static void ggml_compute_forward_div(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst) {
@@ -5398,6 +5585,10 @@ static void ggml_compute_forward_div(
             {
                 ggml_compute_forward_div_f32(params, dst);
             } break;
+        case GGML_TYPE_F16:
+            {
+                ggml_compute_forward_div_f16(params, dst);
+            } break;
         default:
             {
                 GGML_ABORT("fatal error");

diff --git a/ggml/src/ggml-cuda/binbcast.cu b/ggml/src/ggml-cuda/binbcast.cu
index ce4b9cfb51bff26bb152a388e1ec6860739ea3af..e1fbf0e13665d016e68d19c07f77e6970dfb3301 100644 (file)
--- a/ggml/src/ggml-cuda/binbcast.cu
+++ b/ggml/src/ggml-cuda/binbcast.cu
@@ -294,11 +294,13 @@ static void ggml_cuda_op_bin_bcast(
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
     const void * src0_dd, const void * src1_dd, void * dst_dd, cudaStream_t stream) {
 
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);
 
     if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
         op()(src0, src1, dst, (const float *)src0_dd, (const float *)src1_dd, (float *)dst_dd, stream);
-    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
+        op()(src0, src1, dst, (const half *) src0_dd, (const half *)src1_dd, (half *) dst_dd, stream);
+    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) {
         op()(src0, src1, dst, (const half *) src0_dd, (const float *)src1_dd, (half *) dst_dd, stream);
     } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
         op()(src0, src1, dst, (const half *) src0_dd, (const float *)src1_dd, (float *)dst_dd, stream);

diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index b544dee454952dc920dda11ce7ab316ec2ae6cc9..e2412bf31165a4f46ce06b6fac63cbf1e55fb27d 100644 (file)
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -3961,37 +3961,38 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
             test_cases.emplace_back(new test_bin_bcast(op, type, ne, nr));
         }
     };
-
-    add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 8, 1}, {1, 1, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1, 1}, {32, 1, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 320, 320}, {1, 1, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 1, 1}, {1, 1, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 4, 1}, {1, 1, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 4, 3}, {1, 1, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 4, 3}, {2, 1, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 4, 3}, {1, 2, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 4, 3}, {1, 1, 2, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 4, 3}, {1, 1, 1, 2});
-    add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 4, 3}, {1, 1, 2, 2});
-    add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 4, 3}, {1, 2, 2, 2});
-    add_test_bin_bcast(GGML_TYPE_F32, {10, 5, 4, 3}, {2, 2, 2, 2});
-
-    // stable diffusion
-    add_test_bin_bcast(GGML_TYPE_F32, {1280, 1, 1, 1}, {1, 1, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {1280, 1, 1, 1}, {1, 16, 16, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {1280, 16, 16, 1}, {1, 1, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {1280, 1, 1, 1}, {1, 256, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1280, 1}, {16, 16, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {16, 16, 1280, 1}, {1, 1, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1920, 1}, {16, 16, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 2560, 1}, {16, 16, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1280, 1}, {32, 32, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 1920, 1}, {32, 32, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {1, 1, 640, 1}, {32, 32, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {5120, 1, 1, 1}, {1, 256, 1, 1});
-    add_test_bin_bcast(GGML_TYPE_F32, {640, 1, 1, 1}, {1, 1, 1, 1});
-    //add_test_bin_bcast(GGML_TYPE_F32, {3, 3, 2560, 1280}, {1, 1, 1, 1});
-    //add_test_bin_bcast(GGML_TYPE_F32, {3, 3, 2560, 1280}, {2, 1, 1, 1});
+    for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32}) {
+        add_test_bin_bcast(type, {1, 1, 8, 1}, {1, 1, 1, 1});
+        add_test_bin_bcast(type, {1, 1, 1, 1}, {32, 1, 1, 1});
+        add_test_bin_bcast(type, {1, 1, 320, 320}, {1, 1, 1, 1});
+        add_test_bin_bcast(type, {10, 5, 1, 1}, {1, 1, 1, 1});
+        add_test_bin_bcast(type, {10, 5, 4, 1}, {1, 1, 1, 1});
+        add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 1, 1, 1});
+        add_test_bin_bcast(type, {10, 5, 4, 3}, {2, 1, 1, 1});
+        add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 2, 1, 1});
+        add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 1, 2, 1});
+        add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 1, 1, 2});
+        add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 1, 2, 2});
+        add_test_bin_bcast(type, {10, 5, 4, 3}, {1, 2, 2, 2});
+        add_test_bin_bcast(type, {10, 5, 4, 3}, {2, 2, 2, 2});
+
+        // stable diffusion
+        add_test_bin_bcast(type, {1280, 1, 1, 1}, {1, 1, 1, 1});
+        add_test_bin_bcast(type, {1280, 1, 1, 1}, {1, 16, 16, 1});
+        add_test_bin_bcast(type, {1280, 16, 16, 1}, {1, 1, 1, 1});
+        add_test_bin_bcast(type, {1280, 1, 1, 1}, {1, 256, 1, 1});
+        add_test_bin_bcast(type, {1, 1, 1280, 1}, {16, 16, 1, 1});
+        add_test_bin_bcast(type, {16, 16, 1280, 1}, {1, 1, 1, 1});
+        add_test_bin_bcast(type, {1, 1, 1920, 1}, {16, 16, 1, 1});
+        add_test_bin_bcast(type, {1, 1, 2560, 1}, {16, 16, 1, 1});
+        add_test_bin_bcast(type, {1, 1, 1280, 1}, {32, 32, 1, 1});
+        add_test_bin_bcast(type, {1, 1, 1920, 1}, {32, 32, 1, 1});
+        add_test_bin_bcast(type, {1, 1, 640, 1}, {32, 32, 1, 1});
+        add_test_bin_bcast(type, {5120, 1, 1, 1}, {1, 256, 1, 1});
+        add_test_bin_bcast(type, {640, 1, 1, 1}, {1, 1, 1, 1});
+        //add_test_bin_bcast(type, {3, 3, 2560, 1280}, {1, 1, 1, 1});
+        //add_test_bin_bcast(type, {3, 3, 2560, 1280}, {2, 1, 1, 1});
+    }
 
     test_cases.emplace_back(new test_add1());
     test_cases.emplace_back(new test_scale());