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ggml : mul_mat_id use the same tensor for all the experts (ggerganov#…
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…6387)

* ggml : update mul_mat_id to use the same tensor for all the experts

* update cuda

* minor

* update metal

* update test-backend-ops

* fix cuda

* Update ggml-metal.m

Co-authored-by: Georgi Gerganov <[email protected]>

* update convert.py

* update convert-hf-to-gguf.py

* update convert.py for mixtral hf models

* Update convert-hf-to-gguf.py

Co-authored-by: Georgi Gerganov <[email protected]>

* cuda : support non-pow-2 number of experts

* allow quantize to work for split and merged experts models in the same way

* cleanup + disable mmap automatically with split tensors models

* update imatrix

* test-backend-ops : test qwen argsort

* update grok model loading

* llama : add merged experts tensors to the grok tensor map

* minor

* gguf : bump version

* fix quantizing of merged experts

* convert-hf-to-gguf.py : update grok (untested)

* make linter happy

* cuda/argsort : use shared memory instead of pool memory

* convert : fix grok tensor names

* metal : add support for non-pow-2 argsort

* llama : more loader cleanup, better error checking

* cuda : fix warning

* llama : still use mmap for loading old models, but copy the data to a host buffer

* add review note

* llama : remove ffn tensor counting + add sanity check

ggml-ci

* convert : fix handling of n_experts == None

ggml-ci

* imatrix : fix ncall counters

* llama : produce error if imatrix size does not match

* quantize : terminate on errors + trace logs

ggml-ci

* metal : pad shared memory to 16 bytes

---------

Co-authored-by: Georgi Gerganov <[email protected]>
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@slaren @ggerganov @tybalex
2 people authored and tybalex committed Apr 17, 2024
1 parent d9b0606 commit f188ecd
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Showing 15 changed files with 744 additions and 876 deletions.
136 changes: 135 additions & 1 deletion convert-hf-to-gguf.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1216,6 +1216,8 @@ def write_tensors(self):
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
n_head = self.hparams.get("num_attention_heads")
n_kv_head = self.hparams.get("num_key_value_heads")
n_experts = self.hparams.get("num_local_experts")
experts = dict()
for name, data_torch in self.get_tensors():
# we don't need these
if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
Expand All @@ -1236,6 +1238,49 @@ def write_tensors(self):

data = data.squeeze()

# process the experts separately
if name.find("block_sparse_moe.experts") != -1:
experts[name] = data
if len(experts) >= n_experts:
# merge the experts into a single 3d tensor
for bid in range(block_count):
for wid in range(1, 4):
full = True
for xid in range(n_experts):
ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.w{wid}.weight"
if ename not in experts:
full = False
break
if not full:
continue

datas = []
for xid in range(n_experts):
ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.w{wid}.weight"
datas.append(experts[ename])
del experts[ename]

data = np.stack(datas, axis=0)
data_dtype = data.dtype

if self.ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)

if self.ftype == 1 and data_dtype == np.float32:
data = data.astype(np.float16)

merged_name = f"layers.{bid}.feed_forward.experts.w{wid}.weight"

new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias"))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()

print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}")

self.gguf_writer.add_tensor(new_name, data)
continue

# map tensor names
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
if new_name is None:
Expand All @@ -1249,7 +1294,7 @@ def write_tensors(self):
if self.ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)

# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
# 1d tensors need to be converted to float32
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
data = data.astype(np.float32)

Expand All @@ -1261,6 +1306,9 @@ def write_tensors(self):

self.gguf_writer.add_tensor(new_name, data)

if len(experts) > 0:
raise ValueError(f"Unprocessed experts: {experts.keys()}")


@Model.register("GrokForCausalLM")
class GrokModel(Model):
Expand All @@ -1276,6 +1324,92 @@ def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_name("Grok")

def write_tensors(self):
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
n_experts = self.hparams.get("num_local_experts")
experts = dict()
for name, data_torch in self.get_tensors():
# we don't need these
if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
continue

old_dtype = data_torch.dtype

# convert any unsupported data types to float32
if data_torch.dtype not in (torch.float16, torch.float32):
data_torch = data_torch.to(torch.float32)

data = data_torch.squeeze().numpy()

# process the experts separately
if name.find(".moe.") != -1:
experts[name] = data
if len(experts) >= n_experts:
# merge the experts into a single 3d tensor
for bid in range(block_count):
for wid in ["linear", "linear_1", "linear_v"]:
full = True
for xid in range(n_experts):
ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight"
if ename not in experts:
full = False
break
if not full:
continue

datas = []
for xid in range(n_experts):
ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight"
datas.append(experts[ename])
del experts[ename]

data = np.stack(datas, axis=0)
data_dtype = data.dtype

if self.ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)

if self.ftype == 1 and data_dtype == np.float32:
data = data.astype(np.float16)

merged_name = f"transformer.decoder_layer.{bid}.moe.{wid}.weight"

new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias"))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()

print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}")

self.gguf_writer.add_tensor(new_name, data)
continue

# map tensor names
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()

n_dims = len(data.shape)
data_dtype = data.dtype

# if f32 desired, convert any float16 to float32
if self.ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)

# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
data = data.astype(np.float32)

# if f16 desired, convert any float32 2-dim weight tensors to float16
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data = data.astype(np.float16)

print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")

self.gguf_writer.add_tensor(new_name, data)


@Model.register("MiniCPMForCausalLM")
class MiniCPMModel(Model):
Expand Down
25 changes: 25 additions & 0 deletions convert.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -828,6 +828,15 @@ def load() -> Tensor:
return LazyTensor(load, s, lazy_tensor.data_type, 'part ' + lazy_tensor.description)


def pack_experts_lazy(lazy_tensors: list[LazyTensor]) -> LazyTensor:
def load() -> Tensor:
tensors = [lazy_tensor.load() for lazy_tensor in lazy_tensors]
return UnquantizedTensor(np.array([tensor.ndarray for tensor in tensors]))
s = lazy_tensors[0].shape.copy()
s.insert(0, len(lazy_tensors))
return LazyTensor(load, s, lazy_tensors[0].data_type, 'pack_experts ' + ' | '.join(lt.description for lt in lazy_tensors))


# Functionality that simulates `torch.load` but where individual tensors are
# only loaded into memory on demand, not all at once.
# PyTorch can't do this natively as of time of writing:
Expand Down Expand Up @@ -1246,6 +1255,22 @@ def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) ->

tmp = model

# merge experts into one tensor
if params.n_experts and params.n_experts > 0:
for i_l in range(params.n_layer):
for w in range(1, 4):
experts = []
for e in range(params.n_experts):
if f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight" in model:
experts.append(model[f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight"])
del tmp[f"layers.{i_l}.feed_forward.experts.{e}.w{w}.weight"]
elif f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight" in model:
experts.append(model[f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight"])
del tmp[f"model.layers.{i_l}.block_sparse_moe.experts.{e}.w{w}.weight"]
else:
raise ValueError(f"Expert tensor not found: layers.{i_l}.feed_forward.experts.{e}.w{w}.weight")
tmp[f"layers.{i_l}.feed_forward.experts.w{w}.weight"] = pack_experts_lazy(experts)

# HF models permut or pack some of the tensors, so we need to undo that
for i in itertools.count():
if f"model.layers.{i}.self_attn.q_proj.weight" in model:
Expand Down
43 changes: 23 additions & 20 deletions examples/imatrix/imatrix.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -98,35 +98,38 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *

const float * data = is_host ? (const float *) src1->data : m_src1_data.data();

// this has been adapted to the new format of storing merged experts in a single 3d tensor
// ref: https://github.com/ggerganov/llama.cpp/pull/6387
if (t->op == GGML_OP_MUL_MAT_ID) {
const int idx = ((int32_t *) t->op_params)[0];
const int n_as = ((int32_t *) t->op_params)[1];
const ggml_tensor * ids = t->src[2];
const int n_as = src0->ne[2];

// the top-k selected expert ids are stored in the src0 tensor
// for simplicity, always copy src0 to host, because it is small
// take into account that src0 is not contiguous!
GGML_ASSERT(src0->ne[1] == src1->ne[1]);
GGML_ASSERT(n_as*ggml_nrows(src0)*sizeof(int) == GGML_PAD(ggml_nbytes(src0), n_as*sizeof(int)));
m_ids.resize(ggml_nbytes(src0)/sizeof(int));
ggml_backend_tensor_get(src0, m_ids.data(), 0, ggml_nbytes(src0));
// the top-k selected expert ids are stored in the ids tensor
// for simplicity, always copy ids to host, because it is small
// take into account that ids is not contiguous!
GGML_ASSERT(ids->ne[1] == src1->ne[1]);
GGML_ASSERT(n_as*ggml_nrows(ids)*sizeof(int) == GGML_PAD(ggml_nbytes(ids), n_as*sizeof(int)));
m_ids.resize(ggml_nbytes(ids)/sizeof(int));
ggml_backend_tensor_get(ids, m_ids.data(), 0, ggml_nbytes(ids));

auto & e = m_stats[wname];

++e.ncall;
// NOTE: since we select top-k experts, the number of calls for the expert tensors will be k times larger
// using the following line, we can correct for that if needed by replacing the line above with:
//if (idx == t->src[0]->ne[0] - 1) ++e.ncall;

// loop over all possible experts, regardless if they are used or not in the batch
// this is necessary to guarantee equal number of "ncall" for each tensor
for (int ex = 0; ex < n_as; ++ex) {
src0 = t->src[2 + ex];
wname = filter_tensor_name(src0->name);
auto& e = m_stats[wname];
size_t e_start = ex*src1->ne[0];
if (e.values.empty()) {
e.values.resize(src1->ne[0], 0);
e.values.resize(src1->ne[0]*n_as, 0);
}
else if (e.values.size() != (size_t)src1->ne[0]) {
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as);
exit(1); //GGML_ASSERT(false);
}
// NOTE: since we select top-k experts, the number of calls for the expert tensors will be k times larger
// using the following line, we can correct for that if needed
//if (idx == t->src[0]->ne[0] - 1) ++e.ncall;
++e.ncall;
if (m_params.verbosity > 1) {
printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
}
Expand All @@ -136,7 +139,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
if (excur != ex) continue;
const float * x = data + row * src1->ne[0];
for (int j = 0; j < (int)src1->ne[0]; ++j) {
e.values[j] += x[j]*x[j];
e.values[e_start + j] += x[j]*x[j];
}
}
if (e.ncall > m_last_call) {
Expand Down
16 changes: 10 additions & 6 deletions examples/quantize/quantize.cpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -116,44 +116,48 @@ static void load_imatrix(const std::string & imatrix_file, std::unordered_map<st
std::ifstream in(imatrix_file.c_str(), std::ios::binary);
if (!in) {
printf("%s: failed to open %s\n",__func__, imatrix_file.c_str());
return;
exit(1);
}
int n_entries;
in.read((char *)&n_entries, sizeof(n_entries));
if (in.fail() || n_entries < 1) {
printf("%s: no data in file %s\n", __func__, imatrix_file.c_str());
return;
exit(1);
}
for (int i = 0; i < n_entries; ++i) {
int len; in.read((char *)&len, sizeof(len));
std::vector<char> name_as_vec(len+1);
in.read((char *)name_as_vec.data(), len);
if (in.fail()) {
printf("%s: failed reading name for entry %d from %s\n", __func__, i+1, imatrix_file.c_str());
return;
exit(1);
}
name_as_vec[len] = 0;
std::string name{name_as_vec.data()};
auto & e = imatrix_data[std::move(name)];
auto & e = imatrix_data[name];
int ncall;
in.read((char *)&ncall, sizeof(ncall));
int nval;
in.read((char *)&nval, sizeof(nval));
if (in.fail() || nval < 1) {
printf("%s: failed reading number of values for entry %d\n", __func__, i);
imatrix_data = {};
return;
exit(1);
}
e.resize(nval);
in.read((char *)e.data(), nval*sizeof(float));
if (in.fail()) {
printf("%s: failed reading data for entry %d\n", __func__, i);
imatrix_data = {};
return;
exit(1);
}
if (ncall > 0) {
for (auto& v : e) v /= ncall;
}

if (getenv("LLAMA_TRACE")) {
printf("%s: loaded data (size = %6d, ncall = %6d) for '%s'\n", __func__, int(e.size()), ncall, name.c_str());
}
}
printf("%s: loaded %d importance matrix entries from %s\n", __func__, int(imatrix_data.size()), imatrix_file.c_str());
}
Expand Down
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