ggml : unify rope norm/neox (CPU)
This commit is contained in:
Georgi Gerganov
parent
9422c5e34b
commit
2fd31fe188
2 changed files with 81 additions and 143 deletions
212
ggml.c
212
ggml.c
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@ -6257,8 +6257,6 @@ static struct ggml_tensor * ggml_rope_impl(
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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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bool inplace) {
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GGML_ASSERT((mode & 1) == 0 && "mode & 1 == 1 is no longer supported");
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@ -6279,15 +6277,13 @@ static struct ggml_tensor * ggml_rope_impl(
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struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
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int32_t params[13] = { /*n_past*/ 0, n_dims, mode, n_ctx, n_orig_ctx };
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int32_t params[11] = { /*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;
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@ -6307,7 +6303,7 @@ struct ggml_tensor * ggml_rope(
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int mode,
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int n_ctx) {
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return ggml_rope_impl(
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ctx, a, b, NULL, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, false
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ctx, a, b, NULL, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, false
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);
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}
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@ -6319,7 +6315,7 @@ struct ggml_tensor * ggml_rope_inplace(
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int mode,
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int n_ctx) {
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return ggml_rope_impl(
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ctx, a, b, NULL, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, true
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ctx, a, b, NULL, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, true
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);
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}
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@ -6340,7 +6336,7 @@ struct ggml_tensor * ggml_rope_ext(
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float beta_slow) {
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return ggml_rope_impl(
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ctx, a, b, c, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, false
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ext_factor, attn_factor, beta_fast, beta_slow, false
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);
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}
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@ -6361,7 +6357,7 @@ struct ggml_tensor * ggml_rope_ext_inplace(
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float beta_slow) {
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return ggml_rope_impl(
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ctx, a, b, c, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, true
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ext_factor, attn_factor, beta_fast, beta_slow, true
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);
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}
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@ -6381,7 +6377,7 @@ struct ggml_tensor * ggml_rope_custom(
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float beta_slow) {
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return ggml_rope_impl(
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ctx, a, b, NULL, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, false
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ext_factor, attn_factor, beta_fast, beta_slow, false
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);
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}
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@ -6401,20 +6397,10 @@ struct ggml_tensor * ggml_rope_custom_inplace(
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float beta_slow) {
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return ggml_rope_impl(
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ctx, a, b, NULL, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, true
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ext_factor, attn_factor, beta_fast, beta_slow, true
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);
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}
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struct ggml_tensor * ggml_rope_xpos_inplace(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b,
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int n_dims,
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float base,
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bool down) {
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return ggml_rope_impl(ctx, a, b, NULL, n_dims, 0, 0, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, base, down, true);
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}
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// ggml_rope_back
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struct ggml_tensor * ggml_rope_back(
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@ -6431,9 +6417,7 @@ struct ggml_tensor * ggml_rope_back(
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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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float beta_slow) {
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GGML_ASSERT(ggml_is_vector(b));
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GGML_ASSERT(b->type == GGML_TYPE_I32);
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GGML_ASSERT(a->ne[2] == b->ne[0]);
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@ -6449,15 +6433,13 @@ 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[13] = { /*n_past*/ 0, n_dims, mode, n_ctx, n_orig_ctx };
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int32_t params[11] = { /*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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@ -14282,13 +14264,15 @@ static float ggml_rope_yarn_corr_dim(int n_dims, int n_orig_ctx, float n_rot, fl
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}
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static void ggml_rope_cache_init(
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float theta_base, float freq_scale, float corr_dims[2], int64_t ne0, float ext_factor, float mscale,
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float theta_base, float freq_scale, float * freq_factors, float corr_dims[2], int64_t ne0, float ext_factor, float mscale,
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float * cache, float sin_sign, float theta_scale
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) {
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// ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py
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float theta = theta_base;
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for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
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const float ff = freq_factors ? freq_factors[i0/2] : 1.0f;
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rope_yarn(
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theta, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1]
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theta/ff, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1]
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);
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cache[i0 + 1] *= sin_sign;
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@ -14321,10 +14305,6 @@ static void ggml_compute_forward_rope_f32(
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float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
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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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@ -14337,8 +14317,6 @@ static void ggml_compute_forward_rope_f32(
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memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float));
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memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float));
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memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float));
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memcpy(&xpos_base, (int32_t *) dst->op_params + 11, sizeof(float));
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memcpy(&xpos_down, (int32_t *) dst->op_params + 12, sizeof(bool));
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GGML_TENSOR_UNARY_OP_LOCALS
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@ -14396,18 +14374,16 @@ static void ggml_compute_forward_rope_f32(
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const int64_t p = pos[i2];
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float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith;
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if (!is_glm && !is_neox) { // TODO: cache sin/cos for glm, neox
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ggml_rope_cache_init(p, freq_scale, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
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if (!is_glm) { // TODO: cache sin/cos for glm
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ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
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}
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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_glm) {
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theta_base = MIN(p, n_ctx - 2);
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float theta_base = MIN(p, n_ctx - 2);
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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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@ -14431,59 +14407,48 @@ static void ggml_compute_forward_rope_f32(
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dst_data[n_dims] = x2*cos_block_theta - x3*sin_block_theta;
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dst_data[n_dims/2*3] = x2*sin_block_theta + x3*cos_block_theta;
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}
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} else if (!is_neox) {
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for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
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const float cos_theta = cache[i0 + 0];
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const float sin_theta = cache[i0 + 1];
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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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const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
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float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
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const float x0 = src[0];
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const float x1 = src[1];
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dst_data[0] = x0*cos_theta*zeta - x1*sin_theta*zeta;
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dst_data[1] = x0*sin_theta*zeta + x1*cos_theta*zeta;
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}
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} else {
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// ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py
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for (int64_t ic = 0; ic < ne0; ic += 2) {
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if (ic < n_dims) {
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const int64_t i0 = ic/2;
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const float theta_base = (float)p;
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const float freq_factor = freq_factors ? freq_factors[i0] : 1.0f;
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float cos_theta, sin_theta;
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rope_yarn(
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theta_base/freq_factor, freq_scale, corr_dims, ic, 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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if (!is_neox) {
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for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
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const float cos_theta = cache[i0 + 0];
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const float sin_theta = cache[i0 + 1];
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const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
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float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
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const float x0 = src[0];
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const float x1 = src[1];
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dst_data[0] = x0*cos_theta - x1*sin_theta;
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dst_data[1] = x0*sin_theta + x1*cos_theta;
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}
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} else {
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for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
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const int64_t ic = i0/2;
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const float cos_theta = cache[i0 + 0];
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const float sin_theta = cache[i0 + 1];
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const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
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float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0);
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const float x0 = src[0];
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const float x1 = src[n_dims/2];
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dst_data[0] = x0*cos_theta - x1*sin_theta;
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dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
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} else {
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const int64_t i0 = ic;
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const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
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float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
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dst_data[0] = src[0];
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dst_data[1] = src[1];
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}
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}
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for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) {
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const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
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float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
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dst_data[0] = src[0];
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dst_data[1] = src[1];
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}
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}
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}
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}
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@ -14574,18 +14539,16 @@ static void ggml_compute_forward_rope_f16(
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const int64_t p = pos[i2];
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float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith;
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if (!is_glm && !is_neox) { // TODO: cache sin/cos for glm, neox
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ggml_rope_cache_init(p, freq_scale, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
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if (!is_glm) { // TODO: cache sin/cos for glm
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ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
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}
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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_glm) {
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theta_base = MIN(p, n_ctx - 2);
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float theta_base = MIN(p, n_ctx - 2);
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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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@ -14609,55 +14572,48 @@ static void ggml_compute_forward_rope_f16(
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dst_data[n_dims] = GGML_FP32_TO_FP16(x2*cos_block_theta - x3*sin_block_theta);
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dst_data[n_dims/2*3] = GGML_FP32_TO_FP16(x2*sin_block_theta + x3*cos_block_theta);
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}
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} else if (!is_neox) {
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for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
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const float cos_theta = cache[i0 + 0];
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const float sin_theta = cache[i0 + 1];
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const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
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ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
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const float x0 = GGML_FP16_TO_FP32(src[0]);
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const float x1 = GGML_FP16_TO_FP32(src[1]);
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dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
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dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
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}
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} else {
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// ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py
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for (int64_t ic = 0; ic < ne0; ic += 2) {
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if (ic < n_dims) {
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const int64_t i0 = ic/2;
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const float theta_base = (float)p;
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const float freq_factor = freq_factors ? freq_factors[i0] : 1.0f;
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float cos_theta, sin_theta;
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rope_yarn(
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theta_base/freq_factor, freq_scale, corr_dims, ic, 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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if (!is_neox) {
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for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
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const float cos_theta = cache[i0 + 0];
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const float sin_theta = cache[i0 + 1];
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const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
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ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
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const float x0 = GGML_FP16_TO_FP32(src[0]);
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const float x1 = GGML_FP16_TO_FP32(src[1]);
|
||||
|
||||
dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
|
||||
dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
|
||||
}
|
||||
} else {
|
||||
for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
|
||||
const int64_t ic = i0/2;
|
||||
|
||||
const float cos_theta = cache[i0 + 0];
|
||||
const float sin_theta = cache[i0 + 1];
|
||||
|
||||
const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
|
||||
ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0);
|
||||
|
||||
const float x0 = GGML_FP16_TO_FP32(src[0]);
|
||||
const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]);
|
||||
|
||||
dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
|
||||
dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
|
||||
} else {
|
||||
const int64_t i0 = ic;
|
||||
|
||||
const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
|
||||
ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
|
||||
|
||||
dst_data[0] = src[0];
|
||||
dst_data[1] = src[1];
|
||||
}
|
||||
}
|
||||
|
||||
for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) {
|
||||
const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
|
||||
ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
|
||||
|
||||
dst_data[0] = src[0];
|
||||
dst_data[1] = src[1];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -18361,7 +18317,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
|||
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;
|
||||
float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
|
||||
|
||||
memcpy(&freq_base, (int32_t *) tensor->op_params + 5, sizeof(float));
|
||||
memcpy(&freq_scale, (int32_t *) tensor->op_params + 6, sizeof(float));
|
||||
|
|
@ -18369,8 +18325,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
|||
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,
|
||||
|
|
@ -18387,9 +18341,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
|||
ext_factor,
|
||||
attn_factor,
|
||||
beta_fast,
|
||||
beta_slow,
|
||||
xpos_base,
|
||||
xpos_down),
|
||||
beta_slow),
|
||||
zero_table);
|
||||
}
|
||||
} break;
|
||||
|
|
@ -18401,7 +18353,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
|||
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;
|
||||
float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
|
||||
|
||||
memcpy(&freq_base, (int32_t *) tensor->op_params + 5, sizeof(float));
|
||||
memcpy(&freq_scale, (int32_t *) tensor->op_params + 6, sizeof(float));
|
||||
|
|
@ -18409,8 +18361,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
|||
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,
|
||||
|
|
@ -18428,8 +18378,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
|||
attn_factor,
|
||||
beta_fast,
|
||||
beta_slow,
|
||||
xpos_base,
|
||||
xpos_down,
|
||||
false),
|
||||
zero_table);
|
||||
}
|
||||
|
|
|
|||
12
ggml.h
12
ggml.h
|
|
@ -1552,14 +1552,6 @@ extern "C" {
|
|||
float beta_slow),
|
||||
"use ggml_rope_ext_inplace instead");
|
||||
|
||||
struct ggml_tensor * ggml_rope_xpos_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
int n_dims,
|
||||
float base,
|
||||
bool down);
|
||||
|
||||
// compute correction dims for YaRN RoPE scaling
|
||||
GGML_CALL void ggml_rope_yarn_corr_dims(
|
||||
int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]);
|
||||
|
|
@ -1580,9 +1572,7 @@ extern "C" {
|
|||
float ext_factor,
|
||||
float attn_factor,
|
||||
float beta_fast,
|
||||
float beta_slow,
|
||||
float xpos_base,
|
||||
bool xpos_down);
|
||||
float beta_slow);
|
||||
|
||||
// clamp
|
||||
// in-place, returns view(a)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue