ggml : refactor rope norm/neox (#7634)
* ggml : unify rope norm/neox (CPU) * ggml : fix compile warning * ggml : remove GLM rope mode ggml-ci * metal : better rope implementation ggml-ci * cuda : better rope implementation ggml-ci * naming : n_orig_ctx -> n_ctx_orig ggml-ci * dev : add reminders to update backends ggml-ci * vulkan : fix ggml_rope_ext() usage * cuda : fix array size + indents ggml-ci
This commit is contained in:
Georgi Gerganov
GitHub
parent
9973e81c5c
commit
2b3389677a
19 changed files with 485 additions and 732 deletions
334
ggml.c
334
ggml.c
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@ -6250,16 +6250,13 @@ static struct ggml_tensor * ggml_rope_impl(
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struct ggml_tensor * c,
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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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int n_ctx_orig,
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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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bool inplace) {
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GGML_ASSERT((mode & 1) == 0 && "mode & 1 == 1 is no longer supported");
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@ -6280,15 +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*/ 0, n_ctx_orig };
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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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@ -6305,10 +6300,9 @@ struct ggml_tensor * ggml_rope(
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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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int mode,
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int n_ctx) {
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int mode) {
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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, 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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@ -6317,10 +6311,9 @@ struct ggml_tensor * ggml_rope_inplace(
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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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int mode,
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int n_ctx) {
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int mode) {
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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, 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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@ -6331,8 +6324,7 @@ struct ggml_tensor * ggml_rope_ext(
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struct ggml_tensor * c,
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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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int n_ctx_orig,
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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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@ -6340,8 +6332,8 @@ struct ggml_tensor * ggml_rope_ext(
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float beta_fast,
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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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ctx, a, b, c, n_dims, mode, n_ctx_orig, freq_base, freq_scale,
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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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@ -6352,8 +6344,7 @@ struct ggml_tensor * ggml_rope_ext_inplace(
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struct ggml_tensor * c,
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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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int n_ctx_orig,
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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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@ -6361,8 +6352,8 @@ struct ggml_tensor * ggml_rope_ext_inplace(
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float beta_fast,
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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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ctx, a, b, c, n_dims, mode, n_ctx_orig, freq_base, freq_scale,
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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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@ -6372,8 +6363,7 @@ struct ggml_tensor * ggml_rope_custom(
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struct ggml_tensor * b,
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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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int n_ctx_orig,
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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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@ -6381,8 +6371,8 @@ struct ggml_tensor * ggml_rope_custom(
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float beta_fast,
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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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ctx, a, b, NULL, n_dims, mode, n_ctx_orig, freq_base, freq_scale,
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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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@ -6392,8 +6382,7 @@ struct ggml_tensor * ggml_rope_custom_inplace(
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struct ggml_tensor * b,
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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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int n_ctx_orig,
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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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@ -6401,21 +6390,11 @@ struct ggml_tensor * ggml_rope_custom_inplace(
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float beta_fast,
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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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ctx, a, b, NULL, n_dims, mode, n_ctx_orig, freq_base, freq_scale,
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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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@ -6425,16 +6404,13 @@ struct ggml_tensor * ggml_rope_back(
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struct ggml_tensor * c,
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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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int n_ctx_orig,
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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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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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@ -6450,15 +6426,13 @@ 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*/ 0, n_ctx_orig };
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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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@ -14227,8 +14201,7 @@ static float rope_yarn_ramp(const float low, const float high, const int i0) {
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// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
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static void rope_yarn(
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float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale,
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float * cos_theta, float * sin_theta
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) {
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float * cos_theta, float * sin_theta) {
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// Get n-d rotational scaling corrected for extrapolation
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float theta_interp = freq_scale * theta_extrap;
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float theta = theta_interp;
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@ -14245,18 +14218,19 @@ static void rope_yarn(
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// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get
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// `corr_dim(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))`
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static float ggml_rope_yarn_corr_dim(int n_dims, int n_orig_ctx, float n_rot, float base) {
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return n_dims * logf(n_orig_ctx / (n_rot * 2 * (float)M_PI)) / (2 * logf(base));
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static float ggml_rope_yarn_corr_dim(int n_dims, int n_ctx_orig, float n_rot, float base) {
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return n_dims * logf(n_ctx_orig / (n_rot * 2 * (float)M_PI)) / (2 * logf(base));
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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 * cache, float sin_sign, float theta_scale
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) {
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float theta_base, float freq_scale, const 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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// 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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@ -14265,11 +14239,11 @@ static void ggml_rope_cache_init(
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}
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GGML_CALL void ggml_rope_yarn_corr_dims(
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int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]
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int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2]
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) {
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// start and end correction dims
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float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base));
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float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base));
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float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_fast, freq_base));
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float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_slow, freq_base));
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dims[0] = MAX(0, start);
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dims[1] = MIN(n_dims - 1, end);
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}
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@ -14289,15 +14263,11 @@ 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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const int n_ctx = ((int32_t *) dst->op_params)[3];
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const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
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//const int n_ctx = ((int32_t *) dst->op_params)[3];
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const int n_ctx_orig = ((int32_t *) dst->op_params)[4];
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memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float));
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memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float));
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@ -14305,8 +14275,6 @@ 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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@ -14336,20 +14304,15 @@ static void ggml_compute_forward_rope_f32(
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const float theta_scale = powf(freq_base, -2.0f/n_dims);
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float corr_dims[2];
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ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims);
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ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
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const bool is_neox = mode & 2;
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const bool is_glm = mode & 4;
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const float * freq_factors = NULL;
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if (is_neox) {
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if (src2 != NULL) {
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GGML_ASSERT(src2->type == GGML_TYPE_F32);
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GGML_ASSERT(src2->ne[0] >= n_dims / 2);
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freq_factors = (const float *) src2->data;
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}
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} else {
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GGML_ASSERT(src2 == NULL && "TODO: freq_factors not implemented for !is_neox");
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if (src2 != NULL) {
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GGML_ASSERT(src2->type == GGML_TYPE_F32);
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GGML_ASSERT(src2->ne[0] >= n_dims / 2);
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freq_factors = (const float *) src2->data;
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}
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// backward process uses inverse rotation by cos and sin.
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@ -14364,95 +14327,51 @@ 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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}
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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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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 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) * 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) * sin_sign;
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theta_base *= theta_scale;
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block_theta *= theta_scale;
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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[n_dims/2];
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const float x2 = src[n_dims];
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const float x3 = src[n_dims/2*3];
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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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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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||||
if (!is_neox) {
|
||||
for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
|
||||
const float cos_theta = cache[i0 + 0];
|
||||
const float sin_theta = cache[i0 + 1];
|
||||
|
||||
// zeta scaling for xPos only:
|
||||
float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f;
|
||||
if (xpos_down) zeta = 1.0f / zeta;
|
||||
|
||||
const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
|
||||
float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
|
||||
|
||||
const float x0 = src[0];
|
||||
const float x1 = src[1];
|
||||
|
||||
dst_data[0] = x0*cos_theta*zeta - x1*sin_theta*zeta;
|
||||
dst_data[1] = x0*sin_theta*zeta + x1*cos_theta*zeta;
|
||||
dst_data[0] = x0*cos_theta - x1*sin_theta;
|
||||
dst_data[1] = x0*sin_theta + x1*cos_theta;
|
||||
}
|
||||
} else {
|
||||
// ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py
|
||||
for (int64_t ic = 0; ic < ne0; ic += 2) {
|
||||
if (ic < n_dims) {
|
||||
const int64_t i0 = ic/2;
|
||||
for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
|
||||
const int64_t ic = i0/2;
|
||||
|
||||
const float freq_factor = freq_factors ? freq_factors[i0] : 1.0f;
|
||||
const float cos_theta = cache[i0 + 0];
|
||||
const float sin_theta = cache[i0 + 1];
|
||||
|
||||
float cos_theta, sin_theta;
|
||||
rope_yarn(
|
||||
theta_base/freq_factor, freq_scale, corr_dims, ic, ext_factor, attn_factor,
|
||||
&cos_theta, &sin_theta
|
||||
);
|
||||
const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
|
||||
float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0);
|
||||
|
||||
sin_theta *= sin_sign;
|
||||
theta_base *= theta_scale;
|
||||
const float x0 = src[0];
|
||||
const float x1 = src[n_dims/2];
|
||||
|
||||
const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
|
||||
float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
|
||||
|
||||
const float x0 = src[0];
|
||||
const float x1 = src[n_dims/2];
|
||||
|
||||
dst_data[0] = x0*cos_theta - x1*sin_theta;
|
||||
dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
|
||||
} else {
|
||||
const int64_t i0 = ic;
|
||||
|
||||
const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
|
||||
float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
|
||||
|
||||
dst_data[0] = src[0];
|
||||
dst_data[1] = src[1];
|
||||
}
|
||||
dst_data[0] = x0*cos_theta - x1*sin_theta;
|
||||
dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
|
||||
}
|
||||
}
|
||||
|
||||
for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) {
|
||||
const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
|
||||
float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
|
||||
|
||||
dst_data[0] = src[0];
|
||||
dst_data[1] = src[1];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -14477,8 +14396,8 @@ static void ggml_compute_forward_rope_f16(
|
|||
//const int n_past = ((int32_t *) dst->op_params)[0];
|
||||
const int n_dims = ((int32_t *) dst->op_params)[1];
|
||||
const int mode = ((int32_t *) dst->op_params)[2];
|
||||
const int n_ctx = ((int32_t *) dst->op_params)[3];
|
||||
const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
|
||||
//const int n_ctx = ((int32_t *) dst->op_params)[3];
|
||||
const int n_ctx_orig = ((int32_t *) dst->op_params)[4];
|
||||
memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float));
|
||||
memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float));
|
||||
memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float));
|
||||
|
|
@ -14514,20 +14433,15 @@ static void ggml_compute_forward_rope_f16(
|
|||
const float theta_scale = powf(freq_base, -2.0f/n_dims);
|
||||
|
||||
float corr_dims[2];
|
||||
ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims);
|
||||
ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
|
||||
|
||||
const bool is_neox = mode & 2;
|
||||
const bool is_glm = mode & 4;
|
||||
|
||||
const float * freq_factors = NULL;
|
||||
if (is_neox) {
|
||||
if (src2 != NULL) {
|
||||
GGML_ASSERT(src2->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(src2->ne[0] >= n_dims / 2);
|
||||
freq_factors = (const float *) src2->data;
|
||||
}
|
||||
} else {
|
||||
GGML_ASSERT(src2 == NULL && "TODO: freq_factors not implemented for !is_neox");
|
||||
if (src2 != NULL) {
|
||||
GGML_ASSERT(src2->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(src2->ne[0] >= n_dims / 2);
|
||||
freq_factors = (const float *) src2->data;
|
||||
}
|
||||
|
||||
// backward process uses inverse rotation by cos and sin.
|
||||
|
|
@ -14542,43 +14456,14 @@ static void ggml_compute_forward_rope_f16(
|
|||
const int64_t p = pos[i2];
|
||||
|
||||
float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith;
|
||||
if (!is_glm && !is_neox) { // TODO: cache sin/cos for glm, neox
|
||||
ggml_rope_cache_init(p, freq_scale, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
|
||||
}
|
||||
ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale);
|
||||
|
||||
for (int64_t i1 = 0; i1 < ne1; i1++) {
|
||||
if (ir++ < ir0) continue;
|
||||
if (ir > ir1) break;
|
||||
|
||||
float theta_base = (float)p;
|
||||
|
||||
if (is_glm) {
|
||||
theta_base = MIN(p, n_ctx - 2);
|
||||
float block_theta = MAX(p - (n_ctx - 2), 0);
|
||||
for (int64_t i0 = 0; i0 < ne0 / 4; i0++) {
|
||||
const float cos_theta = cosf(theta_base);
|
||||
const float sin_theta = sinf(theta_base) * sin_sign;
|
||||
const float cos_block_theta = cosf(block_theta);
|
||||
const float sin_block_theta = sinf(block_theta) * sin_sign;
|
||||
|
||||
theta_base *= theta_scale;
|
||||
block_theta *= theta_scale;
|
||||
|
||||
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);
|
||||
|
||||
const float x0 = GGML_FP16_TO_FP32(src[0]);
|
||||
const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]);
|
||||
const float x2 = GGML_FP16_TO_FP32(src[n_dims]);
|
||||
const float x3 = GGML_FP16_TO_FP32(src[n_dims/2*3]);
|
||||
|
||||
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);
|
||||
dst_data[n_dims] = GGML_FP32_TO_FP16(x2*cos_block_theta - x3*sin_block_theta);
|
||||
dst_data[n_dims/2*3] = GGML_FP32_TO_FP16(x2*sin_block_theta + x3*cos_block_theta);
|
||||
}
|
||||
} else if (!is_neox) {
|
||||
for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
|
||||
if (!is_neox) {
|
||||
for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
|
||||
const float cos_theta = cache[i0 + 0];
|
||||
const float sin_theta = cache[i0 + 1];
|
||||
|
||||
|
|
@ -14592,41 +14477,30 @@ static void ggml_compute_forward_rope_f16(
|
|||
dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
|
||||
}
|
||||
} else {
|
||||
// ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py
|
||||
for (int64_t ic = 0; ic < ne0; ic += 2) {
|
||||
if (ic < n_dims) {
|
||||
const int64_t i0 = ic/2;
|
||||
for (int64_t i0 = 0; i0 < n_dims; i0 += 2) {
|
||||
const int64_t ic = i0/2;
|
||||
|
||||
const float freq_factor = freq_factors ? freq_factors[i0] : 1.0f;
|
||||
const float cos_theta = cache[i0 + 0];
|
||||
const float sin_theta = cache[i0 + 1];
|
||||
|
||||
float cos_theta, sin_theta;
|
||||
rope_yarn(
|
||||
theta_base/freq_factor, freq_scale, corr_dims, ic, ext_factor, attn_factor,
|
||||
&cos_theta, &sin_theta
|
||||
);
|
||||
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);
|
||||
|
||||
sin_theta *= sin_sign;
|
||||
theta_base *= theta_scale;
|
||||
const float x0 = GGML_FP16_TO_FP32(src[0]);
|
||||
const float x1 = GGML_FP16_TO_FP32(src[n_dims/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);
|
||||
|
||||
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];
|
||||
}
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
||||
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];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -18327,9 +18201,9 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
|||
//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];
|
||||
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;
|
||||
//const int n_ctx = ((int32_t *) tensor->op_params)[3];
|
||||
const int n_ctx_orig = ((int32_t *) tensor->op_params)[4];
|
||||
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));
|
||||
|
|
@ -18337,8 +18211,6 @@ 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,
|
||||
|
|
@ -18348,16 +18220,13 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
|||
src2,
|
||||
n_dims,
|
||||
mode,
|
||||
n_ctx,
|
||||
n_orig_ctx,
|
||||
n_ctx_orig,
|
||||
freq_base,
|
||||
freq_scale,
|
||||
ext_factor,
|
||||
attn_factor,
|
||||
beta_fast,
|
||||
beta_slow,
|
||||
xpos_base,
|
||||
xpos_down),
|
||||
beta_slow),
|
||||
zero_table);
|
||||
}
|
||||
} break;
|
||||
|
|
@ -18367,9 +18236,9 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
|||
//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];
|
||||
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;
|
||||
//const int n_ctx = ((int32_t *) tensor->op_params)[3];
|
||||
const int n_ctx_orig = ((int32_t *) tensor->op_params)[4];
|
||||
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));
|
||||
|
|
@ -18377,8 +18246,6 @@ 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,
|
||||
|
|
@ -18388,16 +18255,13 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
|||
src2,
|
||||
n_dims,
|
||||
mode,
|
||||
n_ctx,
|
||||
n_orig_ctx,
|
||||
n_ctx_orig,
|
||||
freq_base,
|
||||
freq_scale,
|
||||
ext_factor,
|
||||
attn_factor,
|
||||
beta_fast,
|
||||
beta_slow,
|
||||
xpos_base,
|
||||
xpos_down,
|
||||
false),
|
||||
zero_table);
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue