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https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 18:34:36 +00:00
ggml : fix rope args order + assert (#2054)
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3973b25a64
commit
513f861953
@ -1434,7 +1434,7 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
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gf->perf_time_us = 0;
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const auto & hparams = model->hparams;
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//const int n_ctx = hparams.n_ctx;
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const int n_ctx = hparams.n_ctx;
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const int n_vocab = hparams.n_vocab;
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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@ -1863,10 +1863,10 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
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t12->grad = expand(gb, ggml_permute(ctx0, t15->grad, 0, 2, 3, 1)); assert_shape_4d(t12->grad, N, n_batch, n_embd/n_head, n_head);
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t11->grad = expand(gb, ggml_reshape_2d(ctx0, ggml_cont(ctx0, t12->grad), N*n_batch, n_embd)); assert_shape_2d(t11->grad, N*n_batch, n_embd);
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t10->grad = expand(gb, ggml_permute(ctx0, t14->grad, 0, 2, 1, 3)); assert_shape_4d(t10->grad, n_embd/n_head, n_head, N, n_batch);
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t09->grad = expand(gb, ggml_rope_back(ctx0, t10->grad, n_past, n_rot, rope_mode)); assert_shape_4d(t09->grad, n_embd/n_head, n_head, N, n_batch);
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t09->grad = expand(gb, ggml_rope_back(ctx0, t10->grad, n_past, n_rot, rope_mode, n_ctx)); assert_shape_4d(t09->grad, n_embd/n_head, n_head, N, n_batch);
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t08->grad = expand(gb, ggml_reshape_2d(ctx0, t09->grad, n_embd, N*n_batch)); assert_shape_2d(t08->grad, n_embd, N*n_batch);
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t07->grad = expand(gb, ggml_permute(ctx0, t13->grad, 0, 2, 1, 3)); assert_shape_4d(t07->grad, n_embd/n_head, n_head, N, n_batch);
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t06->grad = expand(gb, ggml_rope_back(ctx0, t07->grad, n_past, n_rot, rope_mode)); assert_shape_4d(t06->grad, n_embd/n_head, n_head, N, n_batch);
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t06->grad = expand(gb, ggml_rope_back(ctx0, t07->grad, n_past, n_rot, rope_mode, n_ctx)); assert_shape_4d(t06->grad, n_embd/n_head, n_head, N, n_batch);
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t05->grad = expand(gb, ggml_reshape_2d(ctx0, t06->grad, n_embd, N*n_batch)); assert_shape_2d(t05->grad, n_embd, N*n_batch);
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t04->grad = expand(gb, ggml_add_inplace(ctx0,
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ggml_add_inplace(ctx0,
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24
ggml.c
24
ggml.c
@ -6956,9 +6956,9 @@ struct ggml_tensor * ggml_rope_impl(
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int n_past,
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int n_dims,
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int mode,
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int n_ctx,
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float freq_base,
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float freq_scale,
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int n_ctx,
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bool inplace) {
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GGML_ASSERT(n_past >= 0);
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bool is_node = false;
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@ -6997,7 +6997,7 @@ struct ggml_tensor * ggml_rope(
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int n_dims,
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int mode,
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int n_ctx) {
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return ggml_rope_impl(ctx, a, n_past, n_dims, mode, 10000.0f, 1.0f, n_ctx, false);
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return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, false);
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}
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struct ggml_tensor * ggml_rope_inplace(
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@ -7007,7 +7007,7 @@ struct ggml_tensor * ggml_rope_inplace(
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int n_dims,
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int mode,
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int n_ctx) {
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return ggml_rope_impl(ctx, a, n_past, n_dims, mode, 10000.0f, 1.0f, n_ctx, true);
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return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, true);
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}
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struct ggml_tensor * ggml_rope_custom_inplace(
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@ -7016,10 +7016,10 @@ struct ggml_tensor * ggml_rope_custom_inplace(
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int n_past,
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int n_dims,
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int mode,
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int n_ctx,
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float freq_base,
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float freq_scale,
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int n_ctx) {
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return ggml_rope_impl(ctx, a, n_past, n_dims, mode, freq_base, freq_scale, n_ctx, true);
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float freq_scale) {
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return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, true);
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}
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// ggml_rope_back
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@ -7029,7 +7029,8 @@ struct ggml_tensor * ggml_rope_back(
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struct ggml_tensor * a,
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int n_past,
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int n_dims,
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int mode) {
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int mode,
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int n_ctx) {
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GGML_ASSERT(n_past >= 0);
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GGML_ASSERT((mode & 4) == 0 && "ggml_rope_back() for ChatGLM not implemented yet");
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@ -7043,12 +7044,13 @@ struct ggml_tensor * ggml_rope_back(
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ggml_scratch_save(ctx);
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struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 3);
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struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 4);
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ggml_set_name(b, "n_past, n_dims, mode");
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((int32_t *) b->data)[0] = n_past;
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((int32_t *) b->data)[1] = n_dims;
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((int32_t *) b->data)[2] = mode;
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((int32_t *) b->data)[3] = n_ctx;
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ggml_scratch_load(ctx);
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@ -15740,13 +15742,15 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
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const int n_past = ((int32_t *) src1->data)[0];
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const int n_dims = ((int32_t *) src1->data)[1];
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const int mode = ((int32_t *) src1->data)[2];
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const int n_ctx = ((int32_t *) src1->data)[3];
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src0->grad = ggml_add_impl(ctx,
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src0->grad,
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ggml_rope_back(ctx,
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tensor->grad,
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n_past,
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n_dims,
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mode),
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mode,
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n_ctx),
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inplace);
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}
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if (src1->grad) {
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@ -15757,7 +15761,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
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{
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if (src0->grad) {
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assert(src1->type == GGML_TYPE_I32);
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assert(ggml_nelements(src1) == 3);
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assert(ggml_nelements(src1) == 4);
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const int n_past = ((int32_t *) src1->data)[0];
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const int n_dims = ((int32_t *) src1->data)[1];
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const int mode = ((int32_t *) src1->data)[2];
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7
ggml.h
7
ggml.h
@ -1128,9 +1128,9 @@ extern "C" {
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int n_past,
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int n_dims,
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int mode,
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int n_ctx,
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float freq_base,
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float freq_scale,
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int n_ctx);
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float freq_scale);
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// rotary position embedding backward, i.e compute dx from dy
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// a - dy
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@ -1139,7 +1139,8 @@ extern "C" {
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struct ggml_tensor * a,
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int n_past,
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int n_dims,
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int mode);
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int mode,
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int n_ctx);
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// alibi position embedding
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// in-place, returns view(a)
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@ -1452,11 +1452,11 @@ static bool llama_eval_internal(
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offload_func_kq(tmpq);
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ggml_set_name(tmpq, "tmpq");
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struct ggml_tensor * Kcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0, freq_base, freq_scale, 0);
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struct ggml_tensor * Kcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0, 0, freq_base, freq_scale);
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offload_func_kq(Kcur);
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ggml_set_name(Kcur, "Kcur");
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struct ggml_tensor * Qcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0, freq_base, freq_scale, 0);
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struct ggml_tensor * Qcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0, 0, freq_base, freq_scale);
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offload_func_kq(Qcur);
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ggml_set_name(Qcur, "Qcur");
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