Skip to content

llama : fix buffer checks for mamba and rwk #10111

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 4 commits into from
Oct 31, 2024
Merged

llama : fix buffer checks for mamba and rwk #10111

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
dec6ce2
llama : fix buffer checks for mamba and rwk
slaren Oct 31, 2024
b135927
llama : fix missing worst case flag during reserve
slaren Oct 31, 2024
d1faeca
cuda : fix supports_op for norm
slaren Oct 31, 2024
899351e
disable sched SET_CAUSE
slaren Oct 31, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion ggml/src/ggml-backend.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1508,7 +1508,7 @@ static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, co
return -1;
}

#if 1
#if 0
#define GGML_SCHED_MAX_SPLITS_DEBUG 4096
static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_SCHED_MAX_SPLITS_DEBUG*GGML_SCHED_MAX_SPLIT_INPUTS][128]; // debug only
#define SET_CAUSE(node, ...) sprintf(causes[hash_id(node)], __VA_ARGS__)
Expand Down
6 changes: 4 additions & 2 deletions ggml/src/ggml-cuda.cu
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3107,18 +3107,20 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
}
return false;
} break;
case GGML_OP_NORM:
case GGML_OP_RMS_NORM:
return ggml_is_contiguous(op->src[0]) && op->ne[0] % WARP_SIZE == 0;
break;
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
case GGML_OP_NORM:
case GGML_OP_ADD:
case GGML_OP_ADD1:
case GGML_OP_SUB:
case GGML_OP_MUL:
case GGML_OP_DIV:
case GGML_OP_RMS_NORM:
case GGML_OP_SCALE:
case GGML_OP_SQR:
case GGML_OP_SQRT:
Expand Down
1 change: 1 addition & 0 deletions ggml/src/ggml.c
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7272,6 +7272,7 @@ struct ggml_tensor * ggml_ssm_conv(
const int64_t n_s = sx->ne[2];

// TODO: maybe support other strides than 1?
// FIXME: this is always true?
GGML_ASSERT(sx->ne[0] == d_conv - 1 + n_t);
GGML_ASSERT(sx->ne[1] == d_inner);
GGML_ASSERT(n_t >= 0);
Expand Down
38 changes: 29 additions & 9 deletions src/llama.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7122,7 +7122,7 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w
} break;
case GGML_OP_MUL_MAT:
{
ggml_tensor * b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, w->ne[0], 512);
ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], 512, w->ne[2], w->ne[3]);
op_tensor = ggml_mul_mat(ctx, w, b);
} break;
case GGML_OP_MUL_MAT_ID:
Expand Down Expand Up @@ -7162,18 +7162,38 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w
} break;
case GGML_OP_SSM_CONV:
{
// TODO: ggml_ssm_conv(ctx, conv_x, model.layers[il].ssm_conv1d);
op_tensor = ggml_ssm_conv(ctx, nullptr, w);
// FIXME
ggml_tensor * conv_x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 12345, w->ne[1], 6789);
op_tensor = ggml_ssm_conv(ctx, conv_x, w);
Copy link
Collaborator

@danbev danbev Oct 31, 2024 •
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Should this be the other way around, the convolution/filter as the third argument:

op_tensor = ggml_ssm_conv(ctx, w, conv_x);

Copy link
Member Author

@slaren slaren Oct 31, 2024 •
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

No, it is important that the weight w is in the same position as when used during inference so that the backend supports_op can check it. This function is called as ggml_ssm_conv(ctx, conv_x, model.layers[il].ssm_conv1d) during inference, so the weight is the third argument.

Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Ah I did not realize that, thanks for clarifying!

} break;
case GGML_OP_SSM_SCAN:
{
// TODO: ggml_ssm_scan(ctx, ssm, x, dt, model.layers[il].ssm_a, B, C);
op_tensor = ggml_ssm_scan(ctx, nullptr, nullptr, nullptr, w, nullptr, nullptr);
// FIXME
const int64_t d_state = w->ne[0];
const int64_t d_inner = w->ne[1];
const int64_t n_seq_tokens = 512;
const int64_t n_seqs = 1;
ggml_tensor * s = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, d_inner, n_seqs);
ggml_tensor * x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs);
ggml_tensor * dt = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs);
ggml_tensor * B = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs);
ggml_tensor * C = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs);
op_tensor = ggml_ssm_scan(ctx, s, x, dt, w, B, C);
} break;
case GGML_OP_RWKV_WKV:
{
// TODO: ggml_rwkv_wkv(ctx, k, v, r, layer->time_mix_first, w, *wkv_state);
op_tensor = ggml_rwkv_wkv(ctx, nullptr, nullptr, nullptr, w, nullptr, nullptr);
// FIXME
const int64_t S = 123;
const int64_t H = 123;
const int64_t n_tokens = 123;
const int64_t n_seqs = 123;
ggml_tensor * k = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, 1, H, n_tokens);
ggml_tensor * v = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens);
ggml_tensor * r = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens);
ggml_tensor * tf = w;
ggml_tensor * td = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, 1, S, H, n_tokens);
ggml_tensor * state = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, n_seqs, S, H);
op_tensor = ggml_rwkv_wkv(ctx, k, v, r, tf, td, state);
} break;
default:
GGML_ABORT("%s: missing test for op %s for tensor %s", __func__, ggml_op_name(op), w->name);
Expand Down Expand Up @@ -7448,7 +7468,7 @@ static bool llm_load_tensors(

// tensors with "bias" suffix are always used with GGML_OP_ADD
ggml_op op;
bool bias = strcmp(tn.suffix, "bias") == 0;
bool bias = tn.suffix != nullptr && strcmp(tn.suffix, "bias") == 0;
if (bias) {
op = GGML_OP_ADD;
} else {
Expand Down Expand Up @@ -19674,7 +19694,7 @@ struct llama_context * llama_new_context_with_model(
int n_nodes_tg = ggml_graph_n_nodes(gf_tg);

// reserve again with pp graph to avoid ggml-alloc reallocations during inference
gf_pp = llama_build_graph(*ctx, ubatch_pp, false);
gf_pp = llama_build_graph(*ctx, ubatch_pp, true);
if (!ggml_backend_sched_reserve(ctx->sched, gf_pp)) {
LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
llama_free(ctx);
Expand Down
Loading