ggml : full ALiBi support
This commit is contained in:
Georgi Gerganov
parent
d11afd6652
commit
7fdca3348c
10 changed files with 82 additions and 680 deletions
275
ggml.c
275
ggml.c
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@ -2185,7 +2185,6 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
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"SOFT_MAX_BACK",
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"ROPE",
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"ROPE_BACK",
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"ALIBI",
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"CLAMP",
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"CONV_TRANSPOSE_1D",
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"IM2COL",
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@ -2227,7 +2226,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
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"CROSS_ENTROPY_LOSS_BACK",
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};
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static_assert(GGML_OP_COUNT == 77, "GGML_OP_COUNT != 77");
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static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 77");
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static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"none",
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@ -2276,7 +2275,6 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"soft_max_back(x)",
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"rope(x)",
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"rope_back(x)",
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"alibi(x)",
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"clamp(x)",
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"conv_transpose_1d(x)",
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"im2col(x)",
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@ -2318,7 +2316,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"cross_entropy_loss_back(x,y)",
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};
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static_assert(GGML_OP_COUNT == 77, "GGML_OP_COUNT != 77");
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static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 77");
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static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
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@ -5646,7 +5644,6 @@ static struct ggml_tensor * ggml_soft_max_impl(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * mask,
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struct ggml_tensor * pos,
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float scale,
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float max_bias,
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bool inplace) {
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@ -5660,20 +5657,6 @@ static struct ggml_tensor * ggml_soft_max_impl(
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GGML_ASSERT(mask->ne[1] >= a->ne[1]);
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}
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if (pos) {
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GGML_ASSERT(ggml_is_vector(pos));
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GGML_ASSERT(pos->type == GGML_TYPE_F16 || pos->type == GGML_TYPE_F32);
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GGML_ASSERT(pos->ne[0] == a->ne[0]);
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}
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if (pos && mask) {
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GGML_ASSERT(pos->type == mask->type);
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}
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if (max_bias > 0.0f) {
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GGML_ASSERT(pos);
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}
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bool is_node = false;
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if (a->grad) {
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@ -5689,7 +5672,6 @@ static struct ggml_tensor * ggml_soft_max_impl(
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result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
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result->src[0] = a;
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result->src[1] = mask;
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result->src[2] = pos;
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return result;
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}
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@ -5697,23 +5679,22 @@ static struct ggml_tensor * ggml_soft_max_impl(
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struct ggml_tensor * ggml_soft_max(
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struct ggml_context * ctx,
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struct ggml_tensor * a) {
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return ggml_soft_max_impl(ctx, a, NULL, NULL, 1.0f, 0.0f, false);
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return ggml_soft_max_impl(ctx, a, NULL, 1.0f, 0.0f, false);
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}
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struct ggml_tensor * ggml_soft_max_inplace(
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struct ggml_context * ctx,
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struct ggml_tensor * a) {
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return ggml_soft_max_impl(ctx, a, NULL, NULL, 1.0f, 0.0f, true);
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return ggml_soft_max_impl(ctx, a, NULL, 1.0f, 0.0f, true);
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}
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struct ggml_tensor * ggml_soft_max_ext(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * mask,
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struct ggml_tensor * pos,
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float scale,
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float max_bias) {
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return ggml_soft_max_impl(ctx, a, mask, pos, scale, max_bias, false);
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return ggml_soft_max_impl(ctx, a, mask, scale, max_bias, false);
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}
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// ggml_soft_max_back
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@ -5928,37 +5909,6 @@ struct ggml_tensor * ggml_rope_back(
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return result;
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}
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// ggml_alibi
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struct ggml_tensor * ggml_alibi(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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int n_past,
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int n_head,
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float bias_max) {
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GGML_ASSERT(n_past >= 0);
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bool is_node = false;
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if (a->grad) {
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GGML_ASSERT(false); // TODO: implement backward
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is_node = true;
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}
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// TODO: when implement backward, fix this:
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//struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
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struct ggml_tensor * result = ggml_view_tensor(ctx, a);
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int32_t op_params[3] = { n_past, n_head };
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memcpy(op_params + 2, &bias_max, sizeof(float));
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ggml_set_op_params(result, op_params, sizeof(op_params));
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result->op = GGML_OP_ALIBI;
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result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
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result->src[0] = a;
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return result;
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}
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// ggml_clamp
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struct ggml_tensor * ggml_clamp(
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@ -13333,7 +13283,6 @@ static void ggml_compute_forward_soft_max_f32(
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const struct ggml_tensor * src0 = dst->src[0];
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const struct ggml_tensor * src1 = dst->src[1];
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const struct ggml_tensor * src2 = dst->src[2];
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assert(ggml_is_contiguous(dst));
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assert(ggml_are_same_shape(src0, dst));
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@ -13377,13 +13326,13 @@ static void ggml_compute_forward_soft_max_f32(
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float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith;
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// when max_bias <= 0.0f, src2 is not used and we default it to src0 to avoid branching
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ggml_fp16_t * pos_f16 = src2 ? (ggml_fp16_t *) src2->data : src0->data;
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float * pos_f32 = src2 ? (float *) src2->data : src0->data;
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const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16) || (src2 && src2->type == GGML_TYPE_F16);
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const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16);
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for (int i1 = ir0; i1 < ir1; i1++) {
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// ALiBi
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const uint32_t h = (i1/ne01)%ne02; // head
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const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f;
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float * sp = (float *)((char *) src0->data + i1*src0->nb[1]);
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float * dp = (float *)((char *) dst->data + i1*dst->nb[1]);
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@ -13396,27 +13345,11 @@ static void ggml_compute_forward_soft_max_f32(
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if (mp_f32) {
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if (use_f16) {
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for (int i = 0; i < nc; ++i) {
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wp[i] += GGML_FP16_TO_FP32(mp_f16[i]);
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wp[i] += slope*GGML_FP16_TO_FP32(mp_f16[i]);
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}
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} else {
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for (int i = 0; i < nc; ++i) {
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wp[i] += mp_f32[i];
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}
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}
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}
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// ALiBi bias
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if (max_bias > 0.0f) {
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const uint32_t h = (i1/ne01)%ne02; // head
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const float slope = h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1);
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if (use_f16) {
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for (int i = 0; i < nc; ++i) {
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wp[i] += slope*GGML_FP16_TO_FP32(pos_f16[i]);
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}
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} else {
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for (int i = 0; i < nc; ++i) {
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wp[i] += slope*pos_f32[i];
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wp[i] += slope*mp_f32[i];
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}
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}
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}
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@ -13578,178 +13511,6 @@ static void ggml_compute_forward_soft_max_back(
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}
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}
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// ggml_compute_forward_alibi
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static void ggml_compute_forward_alibi_f32(
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const struct ggml_compute_params * params,
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struct ggml_tensor * dst) {
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const struct ggml_tensor * src0 = dst->src[0];
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assert(params->ith == 0);
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if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
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return;
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}
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//const int n_past = ((int32_t *) dst->op_params)[0];
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const int n_head = ((int32_t *) dst->op_params)[1];
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float max_bias;
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memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
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const int64_t ne0 = src0->ne[0]; // all_seq_len = n_past + ne1
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const int64_t ne1 = src0->ne[1]; // seq_len_without_past
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const int64_t ne2 = src0->ne[2]; // n_head -> this is k
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//const int64_t ne3 = src0->ne[3]; // 1 -> bsz
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const int64_t n = ggml_nrows(src0);
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const int64_t ne2_ne3 = n/ne1; // ne2*ne3
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const size_t nb0 = src0->nb[0];
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const size_t nb1 = src0->nb[1];
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const size_t nb2 = src0->nb[2];
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//const int nb3 = src0->nb[3];
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GGML_ASSERT(nb0 == sizeof(float));
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GGML_ASSERT(n_head == ne2);
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// add alibi to src0 (KQ_scaled)
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const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
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const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
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const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
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for (int64_t k = 0; k < ne2_ne3; k++) {
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// TODO: k*nb2 or k*nb3
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float m_k;
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if (k < n_heads_log2_floor) {
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m_k = powf(m0, k + 1);
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} else {
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m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
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}
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for (int64_t i = 0; i < ne0; i++) {
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for (int64_t j = 0; j < ne1; j++) {
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float * const src = (float *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2);
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float * pdst = (float *)((char *) dst->data + i*nb0 + j*nb1 + k*nb2);
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pdst[0] = i * m_k + src[0];
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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_alibi_f16(
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const struct ggml_compute_params * params,
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struct ggml_tensor * dst) {
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const struct ggml_tensor * src0 = dst->src[0];
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assert(params->ith == 0);
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if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
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return;
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}
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//const int n_past = ((int32_t *) dst->op_params)[0];
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const int n_head = ((int32_t *) dst->op_params)[1];
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float max_bias;
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memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
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const int ne0 = src0->ne[0]; // all_seq_len = n_past + ne1
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const int ne1 = src0->ne[1]; // seq_len_without_past
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const int ne2 = src0->ne[2]; // n_head -> this is k
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//const int ne3 = src0->ne[3]; // 1 -> bsz
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const int n = ggml_nrows(src0);
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const int ne2_ne3 = n/ne1; // ne2*ne3
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const int nb0 = src0->nb[0];
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const int nb1 = src0->nb[1];
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const int nb2 = src0->nb[2];
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//const int nb3 = src0->nb[3];
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GGML_ASSERT(nb0 == sizeof(ggml_fp16_t));
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//GGML_ASSERT(ne1 + n_past == ne0); (void) n_past;
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GGML_ASSERT(n_head == ne2);
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// add alibi to src0 (KQ_scaled)
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const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
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const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
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const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
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for (int k = 0; k < ne2_ne3; k++) {
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// TODO: k*nb2 or k*nb3
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float m_k;
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if (k < n_heads_log2_floor) {
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m_k = powf(m0, k + 1);
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} else {
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m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
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}
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for (int i = 0; i < ne0; i++) {
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for (int j = 0; j < ne1; j++) {
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ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2);
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float * pdst = (float *)((char *) dst->data + i*nb0 + j*nb1 + k*nb2);
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// we return F32
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pdst[0] = i * m_k + GGML_FP16_TO_FP32(src[0]);
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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_alibi(
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const struct ggml_compute_params * params,
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struct ggml_tensor * dst) {
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const struct ggml_tensor * src0 = dst->src[0];
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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_alibi_f16(params, dst);
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} break;
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case GGML_TYPE_F32:
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{
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ggml_compute_forward_alibi_f32(params, dst);
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} break;
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case GGML_TYPE_BF16:
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case GGML_TYPE_Q4_0:
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case GGML_TYPE_Q4_1:
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case GGML_TYPE_Q5_0:
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case GGML_TYPE_Q5_1:
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case GGML_TYPE_Q8_0:
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case GGML_TYPE_Q8_1:
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case GGML_TYPE_Q2_K:
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case GGML_TYPE_Q3_K:
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case GGML_TYPE_Q4_K:
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case GGML_TYPE_Q5_K:
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case GGML_TYPE_Q6_K:
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case GGML_TYPE_IQ2_XXS:
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case GGML_TYPE_IQ2_XS:
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case GGML_TYPE_IQ3_XXS:
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case GGML_TYPE_IQ1_S:
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case GGML_TYPE_IQ1_M:
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case GGML_TYPE_IQ4_NL:
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case GGML_TYPE_IQ4_XS:
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case GGML_TYPE_IQ3_S:
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case GGML_TYPE_IQ2_S:
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case GGML_TYPE_Q8_K:
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case GGML_TYPE_I8:
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case GGML_TYPE_I16:
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case GGML_TYPE_I32:
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case GGML_TYPE_I64:
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case GGML_TYPE_F64:
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case GGML_TYPE_COUNT:
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{
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GGML_ASSERT(false);
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} break;
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}
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}
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// ggml_compute_forward_clamp
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static void ggml_compute_forward_clamp_f32(
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@ -17630,10 +17391,6 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
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{
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ggml_compute_forward_rope_back(params, tensor);
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} break;
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case GGML_OP_ALIBI:
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{
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ggml_compute_forward_alibi(params, tensor);
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} break;
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case GGML_OP_CLAMP:
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{
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ggml_compute_forward_clamp(params, tensor);
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@ -18652,10 +18409,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
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zero_table);
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}
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} break;
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case GGML_OP_ALIBI:
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{
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GGML_ASSERT(false); // TODO: not implemented
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} break;
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case GGML_OP_CLAMP:
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{
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GGML_ASSERT(false); // TODO: not implemented
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@ -19428,10 +19181,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_
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{
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n_tasks = n_threads;
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} break;
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case GGML_OP_ALIBI:
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{
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n_tasks = 1; //TODO
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} break;
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case GGML_OP_CLAMP:
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{
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n_tasks = 1; //TODO
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