mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-23 21:17:54 +01:00
ggml : fix backward rope after YaRN (#3974)
* fix backward process of rope rope backward process was broken after YaRN RoPE (#2268) implementation, due to missing changes in backward functions. the code for the backward process is nearly identically to the forward process: the only difference is the sign of the sin-values. to avoid future regressions remove the near-duplicate backward functions and reuse the forward code: for this a new function argument `bool forward` was added to `ggml_compute_forward_rope_f32` and `ggml_compute_forward_rope_f16`. the sin-values will be negated when forward is false. * fix finetune rope call to use correct default attn_factor of 1.0f * remove unused `ggml_rope_xpos_back` it is better to have only one `ggml_rope_back` function that accepts all rope parameters, so that `ggml_compute_backward` can propagate all parameters without having to switch between different rope_back variants. * fix comments explaining the sinus sign in ggml_forward_rope * add missing function arguments in declaration * fix function argument type in declaration
This commit is contained in:
xaedes
GitHub
parent
54b4df8886
commit
e9c1cecb9d
@ -643,7 +643,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
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return ggml_rope_custom(ctx,
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t, KQ_pos, n_rot, rope_mode, n_ctx, 0,
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rope_freq_base, rope_freq_scale, 0.0f, 0.0f, 0.0f, 0.0f
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rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f
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);
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};
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330
ggml.c
330
ggml.c
@ -4970,8 +4970,13 @@ struct ggml_tensor * ggml_rope_back(
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int n_dims,
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int mode,
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int n_ctx,
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int n_orig_ctx,
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float freq_base,
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float freq_scale,
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float ext_factor,
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float attn_factor,
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float beta_fast,
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float beta_slow,
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float xpos_base,
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bool xpos_down) {
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GGML_ASSERT(ggml_is_vector(b));
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@ -4988,11 +4993,15 @@ struct ggml_tensor * ggml_rope_back(
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struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
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int32_t params[8] = { /*n_past*/ 0, n_dims, mode, n_ctx };
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memcpy(params + 4, &freq_base, sizeof(float));
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memcpy(params + 5, &freq_scale, sizeof(float));
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memcpy(params + 6, &xpos_base, sizeof(float));
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memcpy(params + 7, &xpos_down, sizeof(bool));
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int32_t params[13] = { /*n_past*/ 0, n_dims, mode, n_ctx, n_orig_ctx };
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memcpy(params + 5, &freq_base, sizeof(float));
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memcpy(params + 6, &freq_scale, sizeof(float));
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memcpy(params + 7, &ext_factor, sizeof(float));
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memcpy(params + 8, &attn_factor, sizeof(float));
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memcpy(params + 9, &beta_fast, sizeof(float));
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memcpy(params + 10, &beta_slow, sizeof(float));
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memcpy(params + 11, &xpos_base, sizeof(float));
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memcpy(params + 12, &xpos_down, sizeof(bool));
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ggml_set_op_params(result, params, sizeof(params));
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result->op = GGML_OP_ROPE_BACK;
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@ -10974,7 +10983,8 @@ static void ggml_compute_forward_rope_f32(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst) {
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struct ggml_tensor * dst,
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const bool forward) {
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if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
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return;
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}
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@ -11033,6 +11043,11 @@ static void ggml_compute_forward_rope_f32(
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const bool is_neox = mode & 2;
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const bool is_glm = mode & 4;
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// backward process uses inverse rotation by cos and sin.
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// cos and sin build a rotation matrix, where the inverse is the transpose.
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// this essentially just switches the sign of sin.
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const float sin_sign = forward ? 1.0f : -1.0f;
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const int32_t * pos = (const int32_t *) src1->data;
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for (int64_t i3 = 0; i3 < ne3; i3++) {
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@ -11049,9 +11064,9 @@ static void ggml_compute_forward_rope_f32(
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float block_theta = MAX(p - (n_ctx - 2), 0);
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for (int64_t i0 = 0; i0 < ne0 / 4; i0++) {
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const float cos_theta = cosf(theta_base);
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const float sin_theta = sinf(theta_base);
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const float sin_theta = sinf(theta_base) * sin_sign;
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const float cos_block_theta = cosf(block_theta);
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const float sin_block_theta = sinf(block_theta);
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const float sin_block_theta = sinf(block_theta) * sin_sign;
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theta_base *= theta_scale;
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block_theta *= theta_scale;
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@ -11075,6 +11090,7 @@ static void ggml_compute_forward_rope_f32(
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rope_yarn(
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theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta
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);
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sin_theta *= sin_sign;
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// zeta scaling for xPos only:
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float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f;
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@ -11105,6 +11121,7 @@ static void ggml_compute_forward_rope_f32(
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theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor,
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&cos_theta, &sin_theta
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);
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sin_theta *= sin_sign;
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theta_base *= theta_scale;
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@ -11130,7 +11147,8 @@ static void ggml_compute_forward_rope_f16(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst) {
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struct ggml_tensor * dst,
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const bool forward) {
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if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
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return;
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}
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@ -11182,6 +11200,11 @@ static void ggml_compute_forward_rope_f16(
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const bool is_neox = mode & 2;
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const bool is_glm = mode & 4;
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// backward process uses inverse rotation by cos and sin.
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// cos and sin build a rotation matrix, where the inverse is the transpose.
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// this essentially just switches the sign of sin.
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const float sin_sign = forward ? 1.0f : -1.0f;
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const int32_t * pos = (const int32_t *) src1->data;
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for (int64_t i3 = 0; i3 < ne3; i3++) {
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@ -11198,9 +11221,9 @@ static void ggml_compute_forward_rope_f16(
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float block_theta = MAX(p - (n_ctx - 2), 0);
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for (int64_t i0 = 0; i0 < ne0 / 4; i0++) {
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const float cos_theta = cosf(theta_base);
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const float sin_theta = sinf(theta_base);
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const float sin_theta = sinf(theta_base) * sin_sign;
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const float cos_block_theta = cosf(block_theta);
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const float sin_block_theta = sinf(block_theta);
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const float sin_block_theta = sinf(block_theta) * sin_sign;
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theta_base *= theta_scale;
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block_theta *= theta_scale;
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@ -11224,6 +11247,7 @@ static void ggml_compute_forward_rope_f16(
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rope_yarn(
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theta_base, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta
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);
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sin_theta *= sin_sign;
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theta_base *= theta_scale;
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@ -11250,6 +11274,7 @@ static void ggml_compute_forward_rope_f16(
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theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor,
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&cos_theta, &sin_theta
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);
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sin_theta *= sin_sign;
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theta_base *= theta_scale;
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@ -11279,11 +11304,11 @@ static void ggml_compute_forward_rope(
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switch (src0->type) {
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case GGML_TYPE_F16:
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{
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ggml_compute_forward_rope_f16(params, src0, src1, dst);
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ggml_compute_forward_rope_f16(params, src0, src1, dst, true);
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} break;
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case GGML_TYPE_F32:
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{
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ggml_compute_forward_rope_f32(params, src0, src1, dst);
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ggml_compute_forward_rope_f32(params, src0, src1, dst, true);
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} break;
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default:
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{
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@ -11294,216 +11319,6 @@ static void ggml_compute_forward_rope(
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// ggml_compute_forward_rope_back
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static void ggml_compute_forward_rope_back_f32(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst) {
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if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
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return;
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}
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// y = rope(x, src1)
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// dx = rope_back(dy, src1)
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// src0 is dy, src1 contains options
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float freq_base;
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float freq_scale;
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// these two only relevant for xPos RoPE:
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float xpos_base;
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bool xpos_down;
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//const int n_past = ((int32_t *) dst->op_params)[0];
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const int n_dims = ((int32_t *) dst->op_params)[1];
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const int mode = ((int32_t *) dst->op_params)[2];
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const int n_ctx = ((int32_t *) dst->op_params)[3]; UNUSED(n_ctx);
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memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float));
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memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
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memcpy(&xpos_base, (int32_t *) dst->op_params + 6, sizeof(float));
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memcpy(&xpos_down, (int32_t *) dst->op_params + 7, sizeof(bool));
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GGML_TENSOR_UNARY_OP_LOCALS
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//printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
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//printf("n_past = %d, ne2 = %d\n", n_past, ne2);
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assert(nb0 == sizeof(float));
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const int ith = params->ith;
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const int nth = params->nth;
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const int nr = ggml_nrows(dst);
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// rows per thread
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const int dr = (nr + nth - 1)/nth;
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// row range for this thread
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const int ir0 = dr*ith;
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const int ir1 = MIN(ir0 + dr, nr);
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// row index used to determine which thread to use
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int ir = 0;
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const float theta_scale = powf(freq_base, -2.0f/n_dims);
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const bool is_neox = mode & 2;
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const int32_t * pos = (const int32_t *) src1->data;
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for (int64_t i3 = 0; i3 < ne3; i3++) {
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for (int64_t i2 = 0; i2 < ne2; i2++) {
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const int64_t p = pos[i2];
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for (int64_t i1 = 0; i1 < ne1; i1++) {
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if (ir++ < ir0) continue;
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if (ir > ir1) break;
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float theta_base = freq_scale * (float)p;
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if (!is_neox) {
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for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
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const float cos_theta = cosf(theta_base);
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const float sin_theta = sinf(theta_base);
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// zeta scaling for xPos only:
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float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f;
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if (xpos_down) zeta = 1.0f / zeta;
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theta_base *= theta_scale;
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const float * const dy = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
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float * dx = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
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const float dy0 = dy[0];
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const float dy1 = dy[1];
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dx[0] = dy0*cos_theta*zeta + dy1*sin_theta*zeta;
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dx[1] = - dy0*sin_theta*zeta + dy1*cos_theta*zeta;
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}
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} else {
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for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
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for (int64_t ic = 0; ic < n_dims; ic += 2) {
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const float cos_theta = cosf(theta_base);
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const float sin_theta = sinf(theta_base);
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theta_base *= theta_scale;
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const int64_t i0 = ib*n_dims + ic/2;
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const float * const dy = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
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float * dx = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
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const float dy0 = dy[0];
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const float dy1 = dy[n_dims/2];
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dx[0] = dy0*cos_theta + dy1*sin_theta;
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dx[n_dims/2] = - dy0*sin_theta + dy1*cos_theta;
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}
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}
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}
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}
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}
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}
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}
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static void ggml_compute_forward_rope_back_f16(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst) {
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if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
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return;
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}
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// y = rope(x, src1)
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// dx = rope_back(dy, src1)
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// src0 is dy, src1 contains options
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//const int n_past = ((int32_t *) dst->op_params)[0];
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const int n_dims = ((int32_t *) dst->op_params)[1];
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const int mode = ((int32_t *) dst->op_params)[2];
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GGML_TENSOR_UNARY_OP_LOCALS
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//printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3);
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//printf("n_past = %d, ne2 = %d\n", n_past, ne2);
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assert(nb0 == sizeof(ggml_fp16_t));
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const int ith = params->ith;
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const int nth = params->nth;
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const int nr = ggml_nrows(dst);
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// rows per thread
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const int dr = (nr + nth - 1)/nth;
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// row range for this thread
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const int ir0 = dr*ith;
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const int ir1 = MIN(ir0 + dr, nr);
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// row index used to determine which thread to use
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int ir = 0;
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const float theta_scale = powf(10000.0, -2.0f/n_dims);
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const bool is_neox = mode & 2;
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const int32_t * pos = (const int32_t *) src1->data;
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for (int64_t i3 = 0; i3 < ne3; i3++) {
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for (int64_t i2 = 0; i2 < ne2; i2++) {
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const int64_t p = pos[i2];
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for (int64_t i1 = 0; i1 < ne1; i1++) {
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if (ir++ < ir0) continue;
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if (ir > ir1) break;
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float theta_base = (float)p;
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if (!is_neox) {
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for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
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const float cos_theta = cosf(theta_base);
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const float sin_theta = sinf(theta_base);
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theta_base *= theta_scale;
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const ggml_fp16_t * const dy = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
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ggml_fp16_t * dx = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
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const float dy0 = GGML_FP16_TO_FP32(dy[0]);
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const float dy1 = GGML_FP16_TO_FP32(dy[1]);
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dx[0] = GGML_FP32_TO_FP16( dy0*cos_theta + dy1*sin_theta);
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dx[1] = GGML_FP32_TO_FP16(-dy0*sin_theta + dy1*cos_theta);
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}
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} else {
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for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
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for (int64_t ic = 0; ic < n_dims; ic += 2) {
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const float cos_theta = cosf(theta_base);
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const float sin_theta = sinf(theta_base);
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theta_base *= theta_scale;
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const int64_t i0 = ib*n_dims + ic/2;
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const ggml_fp16_t * const dy = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
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ggml_fp16_t * dx = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
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const float dy0 = GGML_FP16_TO_FP32(dy[0]);
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const float dy1 = GGML_FP16_TO_FP32(dy[n_dims/2]);
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dx[0] = GGML_FP32_TO_FP16( dy0*cos_theta + dy1*sin_theta);
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dx[n_dims/2] = GGML_FP32_TO_FP16(-dy0*sin_theta + dy1*cos_theta);
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}
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}
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}
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}
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}
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}
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}
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static void ggml_compute_forward_rope_back(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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@ -11512,11 +11327,11 @@ static void ggml_compute_forward_rope_back(
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switch (src0->type) {
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case GGML_TYPE_F16:
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{
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ggml_compute_forward_rope_back_f16(params, src0, src1, dst);
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ggml_compute_forward_rope_f16(params, src0, src1, dst, false);
|
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} break;
|
||||
case GGML_TYPE_F32:
|
||||
{
|
||||
ggml_compute_forward_rope_back_f32(params, src0, src1, dst);
|
||||
ggml_compute_forward_rope_f32(params, src0, src1, dst, false);
|
||||
} break;
|
||||
default:
|
||||
{
|
||||
@ -15559,17 +15374,20 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
||||
// necessary for llama
|
||||
if (src0->grad) {
|
||||
//const int n_past = ((int32_t *) tensor->op_params)[0];
|
||||
const int n_dims = ((int32_t *) tensor->op_params)[1];
|
||||
const int mode = ((int32_t *) tensor->op_params)[2];
|
||||
const int n_ctx = ((int32_t *) tensor->op_params)[3];
|
||||
float freq_base;
|
||||
float freq_scale;
|
||||
float xpos_base;
|
||||
bool xpos_down;
|
||||
memcpy(&freq_base, (int32_t *) tensor->op_params + 4, sizeof(float));
|
||||
memcpy(&freq_scale, (int32_t *) tensor->op_params + 5, sizeof(float));
|
||||
memcpy(&xpos_base, (int32_t *) tensor->op_params + 6, sizeof(float));
|
||||
memcpy(&xpos_down, (int32_t *) tensor->op_params + 7, sizeof(bool));
|
||||
const int n_dims = ((int32_t *) tensor->op_params)[1];
|
||||
const int mode = ((int32_t *) tensor->op_params)[2];
|
||||
const int n_ctx = ((int32_t *) tensor->op_params)[3];
|
||||
const int n_orig_ctx = ((int32_t *) tensor->op_params)[4];
|
||||
float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, xpos_base, xpos_down;
|
||||
|
||||
memcpy(&freq_base, (int32_t *) tensor->op_params + 5, sizeof(float));
|
||||
memcpy(&freq_scale, (int32_t *) tensor->op_params + 6, sizeof(float));
|
||||
memcpy(&ext_factor, (int32_t *) tensor->op_params + 7, sizeof(float));
|
||||
memcpy(&attn_factor, (int32_t *) tensor->op_params + 8, sizeof(float));
|
||||
memcpy(&beta_fast, (int32_t *) tensor->op_params + 9, sizeof(float));
|
||||
memcpy(&beta_slow, (int32_t *) tensor->op_params + 10, sizeof(float));
|
||||
memcpy(&xpos_base, (int32_t *) tensor->op_params + 11, sizeof(float));
|
||||
memcpy(&xpos_down, (int32_t *) tensor->op_params + 12, sizeof(bool));
|
||||
|
||||
src0->grad = ggml_add_or_set(ctx,
|
||||
src0->grad,
|
||||
@ -15579,8 +15397,13 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
||||
n_dims,
|
||||
mode,
|
||||
n_ctx,
|
||||
n_orig_ctx,
|
||||
freq_base,
|
||||
freq_scale,
|
||||
ext_factor,
|
||||
attn_factor,
|
||||
beta_fast,
|
||||
beta_slow,
|
||||
xpos_base,
|
||||
xpos_down),
|
||||
zero_table);
|
||||
@ -15590,17 +15413,20 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
||||
{
|
||||
if (src0->grad) {
|
||||
//const int n_past = ((int32_t *) tensor->op_params)[0];
|
||||
const int n_dims = ((int32_t *) tensor->op_params)[1];
|
||||
const int mode = ((int32_t *) tensor->op_params)[2];
|
||||
const int n_ctx = ((int32_t *) tensor->op_params)[3];
|
||||
float freq_base;
|
||||
float freq_scale;
|
||||
float xpos_base;
|
||||
bool xpos_down;
|
||||
memcpy(&freq_base, (int32_t *) tensor->op_params + 4, sizeof(float));
|
||||
memcpy(&freq_scale, (int32_t *) tensor->op_params + 5, sizeof(float));
|
||||
memcpy(&xpos_base, (int32_t *) tensor->op_params + 6, sizeof(float));
|
||||
memcpy(&xpos_down, (int32_t *) tensor->op_params + 7, sizeof(bool));
|
||||
const int n_dims = ((int32_t *) tensor->op_params)[1];
|
||||
const int mode = ((int32_t *) tensor->op_params)[2];
|
||||
const int n_ctx = ((int32_t *) tensor->op_params)[3];
|
||||
const int n_orig_ctx = ((int32_t *) tensor->op_params)[4];
|
||||
float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, xpos_base, xpos_down;
|
||||
|
||||
memcpy(&freq_base, (int32_t *) tensor->op_params + 5, sizeof(float));
|
||||
memcpy(&freq_scale, (int32_t *) tensor->op_params + 6, sizeof(float));
|
||||
memcpy(&ext_factor, (int32_t *) tensor->op_params + 7, sizeof(float));
|
||||
memcpy(&attn_factor, (int32_t *) tensor->op_params + 8, sizeof(float));
|
||||
memcpy(&beta_fast, (int32_t *) tensor->op_params + 9, sizeof(float));
|
||||
memcpy(&beta_slow, (int32_t *) tensor->op_params + 10, sizeof(float));
|
||||
memcpy(&xpos_base, (int32_t *) tensor->op_params + 11, sizeof(float));
|
||||
memcpy(&xpos_down, (int32_t *) tensor->op_params + 12, sizeof(bool));
|
||||
|
||||
src0->grad = ggml_add_or_set(ctx,
|
||||
src0->grad,
|
||||
@ -15609,14 +15435,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
||||
src1,
|
||||
n_dims,
|
||||
mode,
|
||||
0,
|
||||
n_ctx,
|
||||
n_orig_ctx,
|
||||
freq_base,
|
||||
freq_scale,
|
||||
0.0f,
|
||||
1.0f,
|
||||
0.0f,
|
||||
0.0f,
|
||||
ext_factor,
|
||||
attn_factor,
|
||||
beta_fast,
|
||||
beta_slow,
|
||||
xpos_base,
|
||||
xpos_down,
|
||||
false),
|
||||
|
||||
Loading…
Reference in New Issue
Block a user