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ggml : avoid multiply by D in GGML_OP_SSM_SCAN

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This makes the weight buft detection in src/llama.cpp simpler.

* convert : transpose Mamba-2 A, D and reshape SSM_NORM

This breaks existing conversions of Mamba-2 models
to avoid some reshapes.

Not sure if it's a good idea,
but it makes the graph slightly cleaner.

* llama : more appropriate SSM_SCAN and SSM_CONV buft support checks
This commit is contained in:
Francis Couture-Harpin 2024-11-04 11:36:37 -05:00
parent 7d16e1bc8c
commit 3bc7103d2e
7 changed files with 98 additions and 95 deletions
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26
convert_hf_to_gguf.py
View File

@ -264,6 +264,12 @@ class Model:
return [(self.map_tensor_name(name), data_torch)]
# TODO: merge into modify_tensors? (need to check tensor shapes for all arches before doing that)
def reshape_tensors(self, data_torch: Tensor, new_name: str, bid: int | None) -> Tensor:
del new_name, bid # unused
return data_torch.squeeze()
def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
del name, new_name, bid, n_dims # unused
@ -295,7 +301,7 @@ class Model:
break
for new_name, data_torch in (self.modify_tensors(data_torch, name, bid)):
data = data_torch.squeeze().numpy()
data = self.reshape_tensors(data_torch, new_name, bid).numpy()
# if data ends up empty, it means data_torch was a scalar tensor -> restore
if len(data.shape) == 0:
@ -3063,6 +3069,24 @@ class Mamba2Model(Model):
yield (new_name, data_torch)
def reshape_tensors(self, data_torch: Tensor, new_name: str, bid: int | None) -> Tensor:
if any(self.match_model_tensor_name(new_name, t, bid, suffix="") for t in [
gguf.MODEL_TENSOR.SSM_A,
gguf.MODEL_TENSOR.SSM_D,
]):
# unsqueeze A to use similar shape semantics as Mamba-1
# (D is also unsqueezed, but for more straightforward broadcast internally)
return data_torch.reshape((*data_torch.shape, 1))
elif self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_NORM, bid):
d_model = self.find_hparam(["hidden_size", "d_model", "dim"])
d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model
n_group = self.hparams.get("n_groups", 1)
return data_torch.reshape((n_group, d_inner // n_group))
return data_torch.squeeze()
@Model.register("CohereForCausalLM")
class CommandR2Model(Model):

1
ggml/include/ggml.h
View File

@ -1828,7 +1828,6 @@ extern "C" {
struct ggml_tensor * A,
struct ggml_tensor * B,
struct ggml_tensor * C,
struct ggml_tensor * D,
struct ggml_tensor * ids);
// partition into non-overlapping windows with padding if needed

53
ggml/src/ggml-metal.m
View File

@ -1649,25 +1649,21 @@ static void ggml_metal_encode_node(
struct ggml_tensor * src4 = node->src[4];
struct ggml_tensor * src5 = node->src[5];
struct ggml_tensor * src6 = node->src[6];
struct ggml_tensor * src7 = node->src[7];
GGML_ASSERT(src3);
GGML_ASSERT(src4);
GGML_ASSERT(src5);
GGML_ASSERT(src6);
GGML_ASSERT(src7);
size_t offs_src3 = 0;
size_t offs_src4 = 0;
size_t offs_src5 = 0;
size_t offs_src6 = 0;
size_t offs_src7 = 0;
id<MTLBuffer> id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil;
id<MTLBuffer> id_src4 = src4 ? ggml_metal_get_buffer(src4, &offs_src4) : nil;
id<MTLBuffer> id_src5 = src5 ? ggml_metal_get_buffer(src5, &offs_src5) : nil;
id<MTLBuffer> id_src6 = src6 ? ggml_metal_get_buffer(src6, &offs_src6) : nil;
id<MTLBuffer> id_src7 = src7 ? ggml_metal_get_buffer(src7, &offs_src7) : nil;
const int64_t ne30 = src3->ne[0];
const int64_t ne31 = src3->ne[1]; GGML_UNUSED(ne31);
@ -1699,10 +1695,6 @@ static void ggml_metal_encode_node(
const uint64_t nb60 = src6->nb[0]; GGML_UNUSED(nb60);
const int64_t ne70 = src7->ne[0]; GGML_UNUSED(ne70);
const uint64_t nb70 = src7->nb[0]; GGML_UNUSED(nb70);
const int64_t d_state = ne00;
const int64_t d_inner = ne01;
const int64_t n_head = ne02;
@ -1727,31 +1719,30 @@ static void ggml_metal_encode_node(
[encoder setBuffer:id_src4 offset:offs_src4 atIndex:4];
[encoder setBuffer:id_src5 offset:offs_src5 atIndex:5];
[encoder setBuffer:id_src6 offset:offs_src6 atIndex:6];
[encoder setBuffer:id_src7 offset:offs_src7 atIndex:7];
[encoder setBuffer:id_dst offset:offs_dst atIndex:8];
[encoder setBuffer:id_dst offset:offs_dst atIndex:7];
[encoder setBytes:&d_state length:sizeof(d_state) atIndex:9];
[encoder setBytes:&d_inner length:sizeof(d_inner) atIndex:10];
[encoder setBytes:&n_head length:sizeof(n_head) atIndex:11];
[encoder setBytes:&n_group length:sizeof(n_group) atIndex:12];
[encoder setBytes:&n_seq_tokens length:sizeof(n_seq_tokens) atIndex:13];
[encoder setBytes:&n_seqs length:sizeof(n_seqs) atIndex:14];
[encoder setBytes:&d_state length:sizeof(d_state) atIndex:8];
[encoder setBytes:&d_inner length:sizeof(d_inner) atIndex:9];
[encoder setBytes:&n_head length:sizeof(n_head) atIndex:10];
[encoder setBytes:&n_group length:sizeof(n_group) atIndex:11];
[encoder setBytes:&n_seq_tokens length:sizeof(n_seq_tokens) atIndex:12];
[encoder setBytes:&n_seqs length:sizeof(n_seqs) atIndex:13];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:15];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:16];
[encoder setBytes:&nb03 length:sizeof(nb03) atIndex:17];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:18];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:19];
[encoder setBytes:&nb13 length:sizeof(nb13) atIndex:20];
[encoder setBytes:&nb21 length:sizeof(nb21) atIndex:21];
[encoder setBytes:&nb22 length:sizeof(nb22) atIndex:22];
[encoder setBytes:&nb31 length:sizeof(nb31) atIndex:23];
[encoder setBytes:&nb41 length:sizeof(nb41) atIndex:24];
[encoder setBytes:&nb42 length:sizeof(nb42) atIndex:25];
[encoder setBytes:&nb43 length:sizeof(nb43) atIndex:26];
[encoder setBytes:&nb51 length:sizeof(nb51) atIndex:27];
[encoder setBytes:&nb52 length:sizeof(nb52) atIndex:28];
[encoder setBytes:&nb53 length:sizeof(nb53) atIndex:29];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:14];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:15];
[encoder setBytes:&nb03 length:sizeof(nb03) atIndex:16];
[encoder setBytes:&nb11 length:sizeof(nb11) atIndex:17];
[encoder setBytes:&nb12 length:sizeof(nb12) atIndex:18];
[encoder setBytes:&nb13 length:sizeof(nb13) atIndex:19];
[encoder setBytes:&nb21 length:sizeof(nb21) atIndex:20];
[encoder setBytes:&nb22 length:sizeof(nb22) atIndex:21];
[encoder setBytes:&nb31 length:sizeof(nb31) atIndex:22];
[encoder setBytes:&nb41 length:sizeof(nb41) atIndex:23];
[encoder setBytes:&nb42 length:sizeof(nb42) atIndex:24];
[encoder setBytes:&nb43 length:sizeof(nb43) atIndex:25];
[encoder setBytes:&nb51 length:sizeof(nb51) atIndex:26];
[encoder setBytes:&nb52 length:sizeof(nb52) atIndex:27];
[encoder setBytes:&nb53 length:sizeof(nb53) atIndex:28];
// NOTE: max index is 31
if (ne30 == 1) {

14
ggml/src/ggml-metal.metal
View File

@ -805,7 +805,6 @@ kernel void kernel_ssm_scan_f32(
device const void * src4,
device const void * src5,
device const void * src6,
device const void * src7,
device float * dst,
constant int64_t & d_state,
constant int64_t & d_inner,
@ -838,7 +837,6 @@ kernel void kernel_ssm_scan_f32(
const uint64_t nb00 = sizeof(float);
const uint64_t nb10 = sizeof(float);
const uint64_t nb20 = sizeof(float);
const uint64_t nb60 = sizeof(float);
const int64_t nc = d_state;
const int64_t nr = d_inner;
@ -848,7 +846,7 @@ kernel void kernel_ssm_scan_f32(
const int64_t s_off = d_inner * n_head * n_seq_tokens * n_seqs * sizeof(float);
device const int32_t * ids = (device const int32_t *) src7;
device const int32_t * ids = (device const int32_t *) src6;
device const float * s0 = (device const float *) ((device const char *) src0 + ir*nb02 + ids[i3]*nb03);
device float * s = (device float *) ((device char *) dst + ir*nb02 + i3*nb03 + s_off);
@ -859,7 +857,6 @@ kernel void kernel_ssm_scan_f32(
device const float * A = (device const float *) ((device const char *) src3 + ir*nb31); // {d_state, nh}
device const float * B = (device const float *) ((device const char *) src4 + (ir & (ng - 1))*nb41 + i2*nb42 + i3*nb43); // {d_state, ng, nt, ns}
device const float * C = (device const float *) ((device const char *) src5 + (ir & (ng - 1))*nb51 + i2*nb52 + i3*nb53); // {d_state, ng, nt, ns}
device const float * D = (device const float *) ((device const char *) src6 + ir*nb60); // {nh}
device float * y = (device float *) ((device char *) dst + (i1 + ir*(nr) + i2*(nh*nr) + i3*(n_t*nh*nr))*nb00); // {dim, nh, nt, ns}
const float dt_soft_plus = dt[0] <= 20.0f ? log(1.0f + exp(dt[0])) : dt[0];
@ -873,7 +870,7 @@ kernel void kernel_ssm_scan_f32(
s[i] = state;
}
y[0] = sumf + x[0] * D[0];
y[0] = sumf;
// recurse
s0 = s;
@ -890,7 +887,6 @@ kernel void kernel_ssm_scan_f32_group(
device const void * src4,
device const void * src5,
device const void * src6,
device const void * src7,
device float * dst,
constant int64_t & d_state,
constant int64_t & d_inner,
@ -923,7 +919,6 @@ kernel void kernel_ssm_scan_f32_group(
const uint64_t nb00 = sizeof(float);
const uint64_t nb10 = sizeof(float);
const uint64_t nb20 = sizeof(float);
const uint64_t nb60 = sizeof(float);
const int64_t nc = d_state;
const int64_t nr = d_inner;
@ -933,7 +928,7 @@ kernel void kernel_ssm_scan_f32_group(
const int64_t s_off = d_inner * n_head * n_seq_tokens * n_seqs * sizeof(float);
device const int32_t * ids = (device const int32_t *) src7;
device const int32_t * ids = (device const int32_t *) src6;
device const float * s0 = (device const float *) ((device const char *) src0 + ir*nb02 + ids[i3]*nb03);
device float * s = (device float *) ((device char *) dst + ir*nb02 + i3*nb03 + s_off);
@ -944,7 +939,6 @@ kernel void kernel_ssm_scan_f32_group(
device const float * A = (device const float *) ((device const char *) src3 + ir*nb31); // {1, nh}
device const float * B = (device const float *) ((device const char *) src4 + (ir & (ng - 1))*nb41 + i2*nb42 + i3*nb43); // {d_state, ng, nt, ns}
device const float * C = (device const float *) ((device const char *) src5 + (ir & (ng - 1))*nb51 + i2*nb52 + i3*nb53); // {d_state, ng, nt, ns}
device const float * D = (device const float *) ((device const char *) src6 + ir*nb60); // {nh}
device float * y = (device float *) ((device char *) dst + (i1 + ir*(nr) + i2*(nh*nr) + i3*(n_t*nh*nr))*nb00); // {dim, nh, nt, ns}
const float dt_soft_plus = dt[0] <= 20.0f ? log(1.0f + exp(dt[0])) : dt[0];
@ -959,7 +953,7 @@ kernel void kernel_ssm_scan_f32_group(
s[i] = state;
}
y[0] = sumf + x[0] * D[0];
y[0] = sumf;
// recurse
s0 = s;

20
ggml/src/ggml.c
View File

@ -7181,7 +7181,6 @@ 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);
@ -7205,7 +7204,6 @@ struct ggml_tensor * ggml_ssm_scan(
struct ggml_tensor * A,
struct ggml_tensor * B,
struct ggml_tensor * C,
struct ggml_tensor * D,
struct ggml_tensor * ids) {
GGML_ASSERT(ggml_is_contiguous(s));
GGML_ASSERT(ggml_is_contiguous(dt));
@ -7235,8 +7233,6 @@ struct ggml_tensor * ggml_ssm_scan(
GGML_ASSERT(B->ne[0] == d_state);
GGML_ASSERT(B->ne[2] == n_seq_tokens);
GGML_ASSERT(B->ne[3] == n_seqs);
GGML_ASSERT(D->ne[0] == n_head);
GGML_ASSERT(ggml_is_vector(D));
GGML_ASSERT(ids->ne[0] == n_seqs);
GGML_ASSERT(ggml_is_vector(ids));
GGML_ASSERT(A->ne[1] == n_head);
@ -7258,8 +7254,7 @@ struct ggml_tensor * ggml_ssm_scan(
result->src[3] = A;
result->src[4] = B;
result->src[5] = C;
result->src[6] = D;
result->src[7] = ids;
result->src[6] = ids;
return result;
}
@ -16217,8 +16212,7 @@ static void ggml_compute_forward_ssm_scan_f32(
const struct ggml_tensor * src3 = dst->src[3]; // A {d_state, n_head} or {1, n_head}
const struct ggml_tensor * src4 = dst->src[4]; // B {d_state, n_group, n_seq_tokens, n_seqs}
const struct ggml_tensor * src5 = dst->src[5]; // C {d_state, n_group, n_seq_tokens, n_seqs}
const struct ggml_tensor * src6 = dst->src[6]; // D {n_head}
const struct ggml_tensor * src7 = dst->src[7]; // ids {n_seqs}
const struct ggml_tensor * src6 = dst->src[6]; // ids {n_seqs}
const int ith = params->ith;
const int nth = params->nth;
@ -16240,8 +16234,7 @@ static void ggml_compute_forward_ssm_scan_f32(
GGML_ASSERT(src3->nb[0] == sizeof(float));
GGML_ASSERT(src4->nb[0] == sizeof(float));
GGML_ASSERT(src5->nb[0] == sizeof(float));
GGML_ASSERT(src6->nb[0] == sizeof(float));
GGML_ASSERT(src7->nb[0] == sizeof(int32_t));
GGML_ASSERT(src6->nb[0] == sizeof(int32_t));
// allows optimizing the modulo since n_group should be a power of 2
GGML_ASSERT((ng & -ng) == ng);
@ -16252,7 +16245,7 @@ static void ggml_compute_forward_ssm_scan_f32(
const int ih0 = dh*ith;
const int ih1 = MIN(ih0 + dh, nh);
const int32_t * ids = (const int32_t *) src7->data;
const int32_t * ids = (const int32_t *) src6->data;
for (int i3 = 0; i3 < ns; ++i3) {
const float * s0 = (const float *) ((const char *) src0->data + ids[i3]*(src0->nb[3])); // {d_state, dim, nh, ns}
@ -16264,7 +16257,6 @@ static void ggml_compute_forward_ssm_scan_f32(
const float * A = (const float *) ((const char *) src3->data); // {d_state, nh} or {1, nh}
const float * B = (const float *) ((const char *) src4->data + i2*(src4->nb[2]) + i3*(src4->nb[3])); // {d_state, ng, nt, ns}
const float * C = (const float *) ((const char *) src5->data + i2*(src5->nb[2]) + i3*(src5->nb[3])); // {d_state, ng, nt, ns}
const float * D = (const float *) ((const char *) src6->data); // {nh}
float * y = ( float *) (( char *) dst->data + i2*(nh*nr*sizeof(float)) + i3*(nt*nh*nr*sizeof(float))); // {dim, nh, nt, ns}
if (src3->ne[0] == 1) {
@ -16325,7 +16317,7 @@ static void ggml_compute_forward_ssm_scan_f32(
sumf += state * C[ig];
s[i] = state;
}
y[ii] = sumf + x[ii] * D[h];
y[ii] = sumf;
}
}
} else {
@ -16353,7 +16345,7 @@ static void ggml_compute_forward_ssm_scan_f32(
sumf += state * C[ig];
s[i] = state;
}
y[ii] = sumf + x[ii] * D[h];
y[ii] = sumf;
}
}
}

54
src/llama.cpp
View File

@ -7120,6 +7120,7 @@ static const std::map<llm_tensor, llm_tensor_info> llm_tensor_info_mapping = {
{LLM_TENSOR_SSM_CONV1D, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_CONV}},
{LLM_TENSOR_SSM_A, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_SCAN}},
{LLM_TENSOR_SSM_D, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_SSM_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_TIME_MIX_LERP_X, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_TIME_MIX_LN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_CHANNEL_MIX_LERP_K, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
@ -7227,23 +7228,27 @@ static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w
} break;
case GGML_OP_SSM_CONV:
{
// FIXME
ggml_tensor * conv_x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 12345, w->ne[1], 6789);
const int64_t n_seq_tokens = 512;
const int64_t n_seqs = 3;
ggml_tensor * conv_x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, w->ne[0] - 1 + n_seq_tokens, w->ne[1], n_seqs);
op_tensor = ggml_ssm_conv(ctx, conv_x, w);
} break;
case GGML_OP_SSM_SCAN:
{
// FIXME
const int64_t d_state = w->ne[0];
const int64_t d_inner = w->ne[1];
// w is ssm_a
const int64_t d_state = w->ne[0] == 1 ? hparams.ssm_d_state : w->ne[0];
const int64_t n_head = w->ne[1];
const int64_t head_dim = hparams.ssm_d_inner / n_head;
const int64_t n_group = hparams.ssm_n_group;
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);
const int64_t n_seqs = 3;
ggml_tensor * s = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, d_state, head_dim, n_head, n_seqs);
ggml_tensor * x = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, head_dim, n_head, n_seq_tokens, n_seqs);
ggml_tensor * dt = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_head, n_seq_tokens, n_seqs);
ggml_tensor * B = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, d_state, n_group, n_seq_tokens, n_seqs);
ggml_tensor * C = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, d_state, n_group, n_seq_tokens, n_seqs);
ggml_tensor * ids = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_seqs);
op_tensor = ggml_ssm_scan(ctx, s, x, dt, w, B, C, ids);
} break;
case GGML_OP_RWKV_WKV:
{
@ -8572,10 +8577,10 @@ static bool llm_load_tensors(
layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {n_head}, 0);
// no "weight" suffix for these
layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {n_head}, 0);
layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {n_head}, 0);
layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, n_head}, 0);
layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, n_head}, 0);
layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {d_inner}, 0);
layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {d_inner / n_group, n_group}, 0);
// out_proj
layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
@ -9994,7 +9999,7 @@ static struct ggml_tensor * llm_build_rs(
return states;
}
// TODO: split
// TODO: split conv and ssm
static struct ggml_tensor * llm_build_mamba(
struct ggml_context * ctx,
struct llama_context & lctx,
@ -10102,13 +10107,14 @@ static struct ggml_tensor * llm_build_mamba(
dt = llm_build_lora_mm(lctx, ctx, model.layers[il].ssm_dt, dt);
dt = ggml_add(ctx, dt, model.layers[il].ssm_dt_b);
cur = x;
x = ggml_reshape_4d(ctx, x, head_dim, n_head, n_seq_tokens, n_seqs);
struct ggml_tensor * ssm_ids = ggml_view_1d(ctx, state_copy, n_seqs, 0);
// Custom operator to optimize the parallel associative scan
// as described in the Annex D of the Mamba paper.
// => {d_inner, n_seq_tokens, n_seqs} and {d_state, d_inner, n_seqs}
struct ggml_tensor * y_ssm = ggml_ssm_scan(ctx, ssm, x, dt, model.layers[il].ssm_a, B, C, model.layers[il].ssm_d, ssm_ids);
struct ggml_tensor * y_ssm = ggml_ssm_scan(ctx, ssm, x, dt, model.layers[il].ssm_a, B, C, ssm_ids);
// store last states
ggml_build_forward_expand(graph,
@ -10120,6 +10126,7 @@ static struct ggml_tensor * llm_build_mamba(
// TODO: skip computing output earlier for unused tokens
y = ggml_add(ctx, y, ggml_mul(ctx, cur, model.layers[il].ssm_d));
y = ggml_mul(ctx, y, ggml_silu(ctx, ggml_cont(ctx, z)));
// {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs}
@ -10184,7 +10191,7 @@ static struct ggml_tensor * llm_build_mamba2(
struct ggml_tensor * zxBCdt = llm_build_lora_mm(lctx, ctx, model.layers[il].ssm_in, cur);
// split the above in three
struct ggml_tensor * z = ggml_view_3d(ctx, zxBCdt, d_inner, n_seq_tokens, n_seqs, zxBCdt->nb[1], zxBCdt->nb[2], 0);
struct ggml_tensor * z = ggml_view_4d(ctx, zxBCdt, head_dim, n_head, n_seq_tokens, n_seqs, head_dim*zxBCdt->nb[0], zxBCdt->nb[1], zxBCdt->nb[2], 0);
struct ggml_tensor * xBC = ggml_view_3d(ctx, zxBCdt, d_inner + 2*n_group*d_state, n_seq_tokens, n_seqs, zxBCdt->nb[1], zxBCdt->nb[2], d_inner*ggml_element_size(zxBCdt));
struct ggml_tensor * dt = ggml_view_3d(ctx, zxBCdt, n_head, n_seq_tokens, n_seqs, zxBCdt->nb[1], zxBCdt->nb[2], (2*d_inner + 2*n_group*d_state)*ggml_element_size(zxBCdt));
@ -10230,11 +10237,9 @@ static struct ggml_tensor * llm_build_mamba2(
dt = ggml_add(ctx, dt, model.layers[il].ssm_dt_b);
struct ggml_tensor * ssm_ids = ggml_view_1d(ctx, state_copy, n_seqs, 0);
// Use the same shape semantics for A as Mamba-1
struct ggml_tensor * A = ggml_reshape_2d(ctx, model.layers[il].ssm_a, 1, n_head);
// TODO: use semistructured matrices to implement state-space duality
// => {d_inner, n_seq_tokens, n_seqs} and {d_state, d_inner, n_seqs}
struct ggml_tensor * y_ssm = ggml_ssm_scan(ctx, ssm, x, dt, A, B, C, model.layers[il].ssm_d, ssm_ids);
struct ggml_tensor * y_ssm = ggml_ssm_scan(ctx, ssm, x, dt, model.layers[il].ssm_a, B, C, ssm_ids);
// store last states
ggml_build_forward_expand(graph,
@ -10242,17 +10247,16 @@ static struct ggml_tensor * llm_build_mamba2(
ggml_view_1d(ctx, y_ssm, d_state*d_inner*n_seqs, ggml_nelements(x)*x->nb[0]),
ggml_view_1d(ctx, ssm_states_all, d_state*d_inner*n_seqs, kv_head*d_state*d_inner*ggml_element_size(ssm_states_all))));
struct ggml_tensor * y = ggml_view_3d(ctx, y_ssm, d_inner, n_seq_tokens, n_seqs, n_head*x->nb[1], n_seq_tokens*n_head*x->nb[1], 0);
struct ggml_tensor * y = ggml_view_4d(ctx, y_ssm, head_dim, n_head, n_seq_tokens, n_seqs, x->nb[1], n_head*x->nb[1], n_seq_tokens*n_head*x->nb[1], 0);
// TODO: skip computing output earlier for unused tokens
y = ggml_add(ctx, y, ggml_mul(ctx, x, model.layers[il].ssm_d));
y = ggml_mul(ctx, y, ggml_silu(ctx, ggml_cont(ctx, z)));
// grouped RMS norm
y = ggml_reshape_4d(ctx, y, d_inner / n_group, n_group, n_seq_tokens, n_seqs);
y = llm_build_norm(ctx, y, hparams,
ggml_reshape_2d(ctx, model.layers[il].ssm_norm, d_inner / n_group, n_group), NULL,
LLM_NORM_RMS, cb, il);
y = llm_build_norm(ctx, y, hparams, model.layers[il].ssm_norm, NULL, LLM_NORM_RMS, cb, il);
y = ggml_reshape_3d(ctx, y, d_inner, n_seq_tokens, n_seqs);
// {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs}

17
tests/test-backend-ops.cpp
View File

@ -1589,35 +1589,34 @@ struct test_ssm_scan : public test_case {
const ggml_type type;
const int64_t d_state;
const int64_t d_inner;
const int64_t head_dim;
const int64_t n_head;
const int64_t n_group;
const int64_t n_seq_tokens;
const int64_t n_seqs;
std::string vars() override {
return VARS_TO_STR7(type, d_state, d_inner, n_head, n_group, n_seq_tokens, n_seqs);
return VARS_TO_STR7(type, d_state, head_dim, n_head, n_group, n_seq_tokens, n_seqs);
}
test_ssm_scan(ggml_type type = GGML_TYPE_F32,
int64_t d_state = 32,
int64_t d_inner = 1, // non-zero for Mamba-2
int64_t head_dim = 1, // non-zero for Mamba-2
int64_t n_head = 32,
int64_t n_group = 1,
int64_t n_seq_tokens = 32,
int64_t n_seqs = 32)
: type(type), d_state(d_state), d_inner(d_inner), n_head(n_head), n_group(n_group), n_seq_tokens(n_seq_tokens), n_seqs(n_seqs) {}
: type(type), d_state(d_state), head_dim(head_dim), n_head(n_head), n_group(n_group), n_seq_tokens(n_seq_tokens), n_seqs(n_seqs) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * s = ggml_new_tensor_4d(ctx, type, d_state, d_inner, n_head, n_seqs);
ggml_tensor * x = ggml_new_tensor_4d(ctx, type, d_inner, n_head, n_seq_tokens, n_seqs);
ggml_tensor * s = ggml_new_tensor_4d(ctx, type, d_state, head_dim, n_head, n_seqs);
ggml_tensor * x = ggml_new_tensor_4d(ctx, type, head_dim, n_head, n_seq_tokens, n_seqs);
ggml_tensor * dt = ggml_new_tensor_3d(ctx, type, n_head, n_seq_tokens, n_seqs);
ggml_tensor * A = ggml_new_tensor_2d(ctx, type, (d_inner > 1) ? 1 : d_state, n_head);
ggml_tensor * A = ggml_new_tensor_2d(ctx, type, (head_dim > 1) ? 1 : d_state, n_head);
ggml_tensor * B = ggml_new_tensor_4d(ctx, type, d_state, n_group, n_seq_tokens, n_seqs);
ggml_tensor * C = ggml_new_tensor_4d(ctx, type, d_state, n_group, n_seq_tokens, n_seqs);
ggml_tensor * D = ggml_new_tensor_1d(ctx, type, n_head);
ggml_tensor * ids = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_seqs);
ggml_tensor * out = ggml_ssm_scan(ctx, s, x, dt, A, B, C, D, ids);
ggml_tensor * out = ggml_ssm_scan(ctx, s, x, dt, A, B, C, ids);
return out;
}