vbatts/llama.cpp
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Merge remote-tracking branch 'origin/master' into webchat-escape-html

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Henri Vasserman 2023-07-24 18:12:52 +03:00
commit 27d0fcc344
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GPG key ID: 2995FC0F58B1A986
10 changed files with 1514 additions and 906 deletions
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2
Makefile
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@ -360,7 +360,7 @@ embedding: examples/embedding/embedding.cpp build-info.h ggml.
save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp build-info.h ggml.o llama.o common.o $(OBJS)
server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2)
$(LIB_PRE)embdinput$(DSO_EXT): examples/embd-input/embd-input.h examples/embd-input/embd-input-lib.cpp build-info.h ggml.o llama.o common.o $(OBJS)

850
examples/server/index.html.hpp
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@ -111,103 +111,176 @@ unsigned char index_html[] = {
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@ -902,4 +1150,4 @@ unsigned char index_html[] = {
0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x0a, 0x3c, 0x2f, 0x68, 0x74, 0x6d, 0x6c,
0x3e, 0x0a
};
unsigned int index_html_len = 10814;
unsigned int index_html_len = 13790;

124
examples/server/public/index.html
View file

@ -73,6 +73,37 @@
margin: 0;
}
fieldset.two {
display: grid;
grid-template: "a a";
gap: 1em;
}
fieldset.three {
display: grid;
grid-template: "a a a";
gap: 1em;
}
details {
border: 1px solid #aaa;
border-radius: 4px;
padding: 0.5em 0.5em 0;
margin-top: 0.5em;
}
summary {
font-weight: bold;
margin: -0.5em -0.5em 0;
padding: 0.5em;
cursor: pointer;
}
details[open] {
padding: 0.5em;
}
textarea {
padding: 5px;
flex-grow: 1;
@ -125,10 +156,17 @@
const params = signal({
n_predict: 400,
temperature: 0.7,
repeat_last_n: 256,
repeat_penalty: 1.18,
top_k: 40,
top_p: 0.5,
repeat_last_n: 256, // 0 = disable penalty, -1 = context size
repeat_penalty: 1.18, // 1.0 = disabled
top_k: 40, // <= 0 to use vocab size
top_p: 0.5, // 1.0 = disabled
tfs_z: 1.0, // 1.0 = disabled
typical_p: 1.0, // 1.0 = disabled
presence_penalty: 0.0, // 0.0 = disabled
frequency_penalty: 0.0, // 0.0 = disabled
mirostat: 0, // 0/1/2
mirostat_tau: 5, // target entropy
mirostat_eta: 0.1, // learning rate
})
const llamaStats = signal(null)
@ -264,6 +302,27 @@
const updateSession = (el) => session.value = { ...session.value, [el.target.name]: el.target.value }
const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value }
const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) }
const updateParamsInt = (el) => params.value = { ...params.value, [el.target.name]: Math.floor(parseFloat(el.target.value)) }
const FloatField = ({label, max, min, name, step, value}) => {
return html`
<div>
<label for="${name}">${label}</label>
<input type="range" id="${name}" min="${min}" max="${max}" step="${step}" name="${name}" value="${value}" oninput=${updateParamsFloat} />
<span>${value}</span>
</div>
`
};
const IntField = ({label, max, min, name, value}) => {
return html`
<div>
<label for="${name}">${label}</label>
<input type="range" id="${name}" min="${min}" max="${max}" name="${name}" value="${value}" oninput=${updateParamsInt} />
<span>${value}</span>
</div>
`
};
return html`
<form>
@ -272,7 +331,9 @@
<label for="prompt">Prompt</label>
<textarea type="text" name="prompt" value="${session.value.prompt}" rows=4 oninput=${updateSession}/>
</div>
</fieldset>
<fieldset class="two">
<div>
<label for="user">User name</label>
<input type="text" name="user" value="${session.value.user}" oninput=${updateSession} />
@ -282,7 +343,9 @@
<label for="bot">Bot name</label>
<input type="text" name="char" value="${session.value.char}" oninput=${updateSession} />
</div>
</fieldset>
<fieldset>
<div>
<label for="template">Prompt template</label>
<textarea id="template" name="template" value="${session.value.template}" rows=4 oninput=${updateSession}/>
@ -292,32 +355,35 @@
<label for="template">Chat history template</label>
<textarea id="template" name="historyTemplate" value="${session.value.historyTemplate}" rows=1 oninput=${updateSession}/>
</div>
<div>
<label for="temperature">Temperature</label>
<input type="range" id="temperature" min="0.0" max="1.0" step="0.01" name="temperature" value="${params.value.temperature}" oninput=${updateParamsFloat} />
<span>${params.value.temperature}</span>
</div>
<div>
<label for="nPredict">Predictions</label>
<input type="range" id="nPredict" min="1" max="2048" step="1" name="n_predict" value="${params.value.n_predict}" oninput=${updateParamsFloat} />
<span>${params.value.n_predict}</span>
</div>
<div>
<label for="repeat_penalty">Penalize repeat sequence</label>
<input type="range" id="repeat_penalty" min="0.0" max="2.0" step="0.01" name="repeat_penalty" value="${params.value.repeat_penalty}" oninput=${updateParamsFloat} />
<span>${params.value.repeat_penalty}</span>
</div>
<div>
<label for="repeat_last_n">Consider N tokens for penalize</label>
<input type="range" id="repeat_last_n" min="0.0" max="2048" name="repeat_last_n" value="${params.value.repeat_last_n}" oninput=${updateParamsFloat} />
<span>${params.value.repeat_last_n}</span>
</div>
</fieldset>
<fieldset class="two">
${IntField({label: "Predictions", max: 2048, min: -1, name: "n_predict", value: params.value.n_predict})}
${FloatField({label: "Temperature", max: 1.5, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature})}
${FloatField({label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty})}
${IntField({label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n})}
${IntField({label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k})}
${FloatField({label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p})}
</fieldset>
<details>
<summary>More options</summary>
<fieldset class="two">
${FloatField({label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z})}
${FloatField({label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p})}
${FloatField({label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty})}
${FloatField({label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty})}
</fieldset>
<hr />
<fieldset class="three">
<div>
<label><input type="radio" name="mirostat" value="0" checked=${params.value.mirostat == 0} oninput=${updateParamsInt} /> no Mirostat</label>
<label><input type="radio" name="mirostat" value="1" checked=${params.value.mirostat == 1} oninput=${updateParamsInt} /> Mirostat v1</label>
<label><input type="radio" name="mirostat" value="2" checked=${params.value.mirostat == 2} oninput=${updateParamsInt} /> Mirostat v2</label>
</div>
${FloatField({label: "Mirostat tau", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau})}
${FloatField({label: "Mirostat eta", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta})}
</fieldset>
</details>
</form>
`
}

29
ggml-cuda.cu
View file

@ -3962,18 +3962,23 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
}
func = ggml_cuda_mul;
break;
case GGML_OP_GELU:
if (!any_on_device) {
return false;
}
func = ggml_cuda_gelu;
break;
case GGML_OP_SILU:
if (!any_on_device) {
return false;
}
func = ggml_cuda_silu;
break;
case GGML_OP_UNARY:
switch (ggml_get_unary_op(tensor)) {
case GGML_UNARY_OP_GELU:
if (!any_on_device) {
return false;
}
func = ggml_cuda_gelu;
break;
case GGML_UNARY_OP_SILU:
if (!any_on_device) {
return false;
}
func = ggml_cuda_silu;
break;
default:
return false;
} break;
case GGML_OP_NORM:
if (!any_on_device) {
return false;

90
ggml-metal.m
View file

@ -519,48 +519,56 @@ void ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_SILU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
case GGML_OP_UNARY:
switch (ggml_get_unary_op(gf->nodes[i])) {
case GGML_UNARY_OP_SILU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
[encoder setComputePipelineState:ctx->pipeline_silu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setComputePipelineState:ctx->pipeline_silu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_UNARY_OP_RELU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
[encoder setComputePipelineState:ctx->pipeline_relu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_UNARY_OP_GELU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
[encoder setComputePipelineState:ctx->pipeline_gelu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
default:
{
fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
GGML_ASSERT(false);
}
} break;
case GGML_OP_RELU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
[encoder setComputePipelineState:ctx->pipeline_relu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_GELU:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
[encoder setComputePipelineState:ctx->pipeline_gelu];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
const int64_t n = ggml_nelements(dst);
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
case GGML_OP_SOFT_MAX:
{
if (encoder == nil) {
@ -979,8 +987,10 @@ void ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
default:
fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
GGML_ASSERT(false);
{
fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
GGML_ASSERT(false);
}
}
}

775
ggml.c
View file

File diff suppressed because it is too large Load diff

60
ggml.h
View file

@ -330,16 +330,6 @@ extern "C" {
GGML_OP_ARGMAX,
GGML_OP_REPEAT,
GGML_OP_REPEAT_BACK,
GGML_OP_ABS,
GGML_OP_SGN,
GGML_OP_NEG,
GGML_OP_STEP,
GGML_OP_TANH,
GGML_OP_ELU,
GGML_OP_RELU,
GGML_OP_GELU,
GGML_OP_GELU_QUICK,
GGML_OP_SILU,
GGML_OP_SILU_BACK,
GGML_OP_NORM, // normalize
GGML_OP_RMS_NORM,
@ -378,6 +368,8 @@ extern "C" {
GGML_OP_WIN_PART,
GGML_OP_WIN_UNPART,
GGML_OP_UNARY,
GGML_OP_MAP_UNARY,
GGML_OP_MAP_BINARY,
@ -391,6 +383,18 @@ extern "C" {
GGML_OP_COUNT,
};
enum ggml_unary_op {
GGML_UNARY_OP_ABS,
GGML_UNARY_OP_SGN,
GGML_UNARY_OP_NEG,
GGML_UNARY_OP_STEP,
GGML_UNARY_OP_TANH,
GGML_UNARY_OP_ELU,
GGML_UNARY_OP_RELU,
GGML_UNARY_OP_GELU,
GGML_UNARY_OP_GELU_QUICK,
GGML_UNARY_OP_SILU,
};
// ggml object
struct ggml_object {
@ -535,6 +539,7 @@ extern "C" {
GGML_API const char * ggml_type_name(enum ggml_type type);
GGML_API const char * ggml_op_name (enum ggml_op op);
GGML_API const char * ggml_op_symbol(enum ggml_op op);
GGML_API size_t ggml_element_size(const struct ggml_tensor * tensor);
@ -558,6 +563,7 @@ extern "C" {
GGML_API size_t ggml_used_mem(const struct ggml_context * ctx);
GGML_API size_t ggml_set_scratch (struct ggml_context * ctx, struct ggml_scratch scratch);
GGML_API bool ggml_get_no_alloc(struct ggml_context * ctx);
GGML_API void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc);
GGML_API void * ggml_get_mem_buffer (const struct ggml_context * ctx);
@ -617,9 +623,11 @@ extern "C" {
GGML_API void * ggml_get_data (const struct ggml_tensor * tensor);
GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor);
GGML_API const char * ggml_get_name(const struct ggml_tensor * tensor);
GGML_API struct ggml_tensor * ggml_set_name(struct ggml_tensor * tensor, const char * name);
GGML_API struct ggml_tensor * ggml_format_name(struct ggml_tensor * tensor, const char * fmt, ...);
GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor);
GGML_API const char * ggml_get_name (const struct ggml_tensor * tensor);
GGML_API struct ggml_tensor * ggml_set_name ( struct ggml_tensor * tensor, const char * name);
GGML_API struct ggml_tensor * ggml_format_name( struct ggml_tensor * tensor, const char * fmt, ...);
//
// operations on tensors with backpropagation
@ -629,6 +637,11 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * a);
// in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_dup_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_add(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -952,11 +965,22 @@ extern "C" {
struct ggml_tensor * a,
struct ggml_tensor * b);
// a -> b, in-place, return view(b)
GGML_API struct ggml_tensor * ggml_cpy_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
// make contiguous
GGML_API struct ggml_tensor * ggml_cont(
struct ggml_context * ctx,
struct ggml_tensor * a);
// make contiguous, in-place
GGML_API struct ggml_tensor * ggml_cont_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
// return view(a), b specifies the new shape
// TODO: when we start computing gradient, make a copy instead of view
GGML_API struct ggml_tensor * ggml_reshape(
@ -1268,6 +1292,16 @@ extern "C" {
typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
GGML_API struct ggml_tensor * ggml_unary(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_unary_op op);
GGML_API struct ggml_tensor * ggml_unary_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_unary_op op);
GGML_API struct ggml_tensor * ggml_map_unary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,

5
k_quants.c
View file

@ -3297,8 +3297,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
#else
uint8_t aux8[QK_K];
int8_t aux8[QK_K];
int16_t aux16[16];
float sums [8];
memset(sums, 0, 8*sizeof(float));
@ -3308,7 +3307,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
const uint8_t * restrict q4 = x[i].qs;
const uint8_t * restrict hm = x[i].qh;
const int8_t * restrict q8 = y[i].qs;
uint8_t * restrict a = aux8;
int8_t * restrict a = aux8;
for (int l = 0; l < 32; ++l) {
a[l+ 0] = q4[l] & 0xF;
a[l+32] = q4[l] >> 4;

479
tests/test-grad0.c
View file

@ -64,7 +64,7 @@ void get_random_dims(int64_t * dims, int ndims) {
}
}
struct ggml_tensor * get_random_tensor(
struct ggml_tensor * get_random_tensor_f32(
struct ggml_context * ctx0,
int ndims,
int64_t ne[],
@ -112,7 +112,55 @@ struct ggml_tensor * get_random_tensor(
return result;
}
struct ggml_tensor * get_random_tensor_int(
struct ggml_tensor * get_random_tensor_f16(
struct ggml_context * ctx0,
int ndims,
int64_t ne[],
float fmin,
float fmax) {
struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_F16, ndims, ne);
switch (ndims) {
case 1:
for (int i0 = 0; i0 < ne[0]; i0++) {
((ggml_fp16_t *)result->data)[i0] = ggml_fp32_to_fp16(frand()*(fmax - fmin) + fmin);
}
break;
case 2:
for (int i1 = 0; i1 < ne[1]; i1++) {
for (int i0 = 0; i0 < ne[0]; i0++) {
((ggml_fp16_t *)result->data)[i1*ne[0] + i0] = ggml_fp32_to_fp16(frand()*(fmax - fmin) + fmin);
}
}
break;
case 3:
for (int i2 = 0; i2 < ne[2]; i2++) {
for (int i1 = 0; i1 < ne[1]; i1++) {
for (int i0 = 0; i0 < ne[0]; i0++) {
((ggml_fp16_t *)result->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = ggml_fp32_to_fp16(frand()*(fmax - fmin) + fmin);
}
}
}
break;
case 4:
for (int i3 = 0; i3 < ne[3]; i3++) {
for (int i2 = 0; i2 < ne[2]; i2++) {
for (int i1 = 0; i1 < ne[1]; i1++) {
for (int i0 = 0; i0 < ne[0]; i0++) {
((ggml_fp16_t *)result->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = ggml_fp32_to_fp16(frand()*(fmax - fmin) + fmin);
}
}
}
}
break;
default:
assert(false);
};
return result;
}
struct ggml_tensor * get_random_tensor_i32(
struct ggml_context * ctx0,
int ndims,
int64_t ne[],
@ -160,23 +208,6 @@ struct ggml_tensor * get_random_tensor_int(
return result;
}
float get_element(const struct ggml_tensor * t, int idx) {
if (t->type == GGML_TYPE_F32) {
return ((float *)t->data)[idx];
}
if (t->type == GGML_TYPE_I32) {
return ((int32_t *)t->data)[idx];
}
assert(false);
return INFINITY;
}
void set_element(struct ggml_tensor * t, int idx, float value) {
((float *)t->data)[idx] = value;
}
void print_elements(const char* label, const struct ggml_tensor * t) {
if (!t) {
printf("%s: %s = null\n", __func__, label);
@ -186,7 +217,7 @@ void print_elements(const char* label, const struct ggml_tensor * t) {
printf("%s: %s = [", __func__, label);
for (int k = 0; k < nelements; ++k) {
if (k > 0) { printf(", "); }
printf("%.5f", get_element(t, k));
printf("%.5f", ggml_get_f32_1d(t, k));
}
printf("] shape: [");
for (int k = 0; k < t->n_dims; ++k) {
@ -237,23 +268,23 @@ bool check_gradient(
const int nelements = ggml_nelements(x[i]);
for (int k = 0; k < nelements; ++k) {
// compute gradient using finite differences
const float x0 = get_element(x[i], k);
const float x0 = ggml_get_f32_1d(x[i], k);
const float xm = x0 - eps;
const float xp = x0 + eps;
set_element(x[i], k, xp);
ggml_set_f32_1d(x[i], k, xp);
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
const float f0 = ggml_get_f32_1d(f, 0);
set_element(x[i], k, xm);
ggml_set_f32_1d(x[i], k, xm);
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
const float f1 = ggml_get_f32_1d(f, 0);
const float g0 = (f0 - f1)/(2.0f*eps);
set_element(x[i], k, x0);
ggml_set_f32_1d(x[i], k, x0);
// compute gradient using backward graph
ggml_graph_reset (&gf);
@ -261,7 +292,7 @@ bool check_gradient(
ggml_graph_compute_with_ctx(ctx0, &gb, n_threads);
const float g1 = get_element(x[i]->grad, k);
const float g1 = ggml_get_f32_1d(x[i]->grad, k);
const float error_abs = fabsf(g0 - g1);
const float error_rel = g0 != 0 ? fabsf(g0 - g1)/fabsf(g0) : 0;
@ -392,19 +423,35 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * x[MAX_NARGS];
// add
// add f32
{
const int nargs = 2;
for (int ndims = 1; ndims <= 4; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
struct ggml_tensor * f = ggml_sum(ctx0, ggml_add(ctx0, x[0], x[1]));
check_gradient("add", ctx0, x, f, ndims, nargs, 1e-3f, 2e-3f, 2e-3f);
check_gradient("add f32", ctx0, x, f, ndims, nargs, 1e-3f, 2e-3f, 2e-3f);
}
}
// add f16
{
const int nargs = 2;
for (int ndims = 1; ndims <= 4; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor_f16(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
struct ggml_tensor * f = ggml_sum(ctx0, ggml_add(ctx0, x[0], x[1]));
check_gradient("add f16", ctx0, x, f, ndims, nargs, 1e-1f, 2e-1f, 2e-1f);
}
}
@ -414,7 +461,7 @@ int main(int argc, const char ** argv) {
for (int ndims = 1; ndims <= 4; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
@ -430,7 +477,7 @@ int main(int argc, const char ** argv) {
for (int ndims = 1; ndims <= 4; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
@ -446,7 +493,7 @@ int main(int argc, const char ** argv) {
for (int ndims = 1; ndims <= 4; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor(ctx0, ndims, ne, 0.5f, 1.0f);
x[i] = get_random_tensor_f32(ctx0, ndims, ne, 0.5f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
@ -462,7 +509,7 @@ int main(int argc, const char ** argv) {
for (int ndims = 1; ndims <= 2; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
@ -478,7 +525,7 @@ int main(int argc, const char ** argv) {
for (int ndims = 1; ndims <= 2; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor(ctx0, ndims, ne, 2.0f*1e-3f, 1.0f);
x[i] = get_random_tensor_f32(ctx0, ndims, ne, 2.0f*1e-3f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
@ -494,7 +541,7 @@ int main(int argc, const char ** argv) {
for (int ndims = 1; ndims <= 2; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor(ctx0, ndims, ne, 2.0f*1e-3f, 1.0f);
x[i] = get_random_tensor_f32(ctx0, ndims, ne, 2.0f*1e-3f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
@ -510,7 +557,7 @@ int main(int argc, const char ** argv) {
for (int ndims = 1; ndims <= 2; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
@ -527,7 +574,7 @@ int main(int argc, const char ** argv) {
for (int ndims = 1; ndims <= 4; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
@ -537,6 +584,40 @@ int main(int argc, const char ** argv) {
}
}
// mean, not yet fully implemented
if(0)
{
const int nargs = 1;
for (int ndims = 1; ndims <= 4; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
struct ggml_tensor * f = ggml_sum(ctx0, ggml_mean(ctx0, x[0]));
check_gradient("mean", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
}
}
// argmax
if (0)
{
const int nargs = 1;
for (int ndims = 1; ndims <= 4; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
struct ggml_tensor * f = ggml_sum(ctx0, ggml_argmax(ctx0, x[0]));
check_gradient("argmax", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
}
}
// repeat
{
int64_t ne2[4];
@ -549,15 +630,36 @@ int main(int argc, const char ** argv) {
const int nargs = 1;
for (int ndims = 1; ndims <= 2; ++ndims) {
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[1] = get_random_tensor(ctx0, ndims, ne2, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
x[1] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, x[1], ggml_repeat(ctx0, x[0], x[1]))));
check_gradient("repeat", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY);
}
}
// repeat back
{
int64_t ne2[4];
get_random_dims(ne2, 4);
ne2[0] = ne[0] * ne2[0];
ne2[1] = ne[1] * ne2[1];
ne2[2] = 1;
ne2[3] = 1;
const int nargs = 1;
for (int ndims = 1; ndims <= 2; ++ndims) {
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
x[1] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, x[0], ggml_repeat_back(ctx0, x[1], x[0]))));
check_gradient("repeat back", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY);
}
}
// abs (finite differences do not work)
@ -566,7 +668,7 @@ int main(int argc, const char ** argv) {
// for (int ndims = 1; ndims <= 2; ++ndims) {
// for (int i = 0; i < nargs; ++i) {
// x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
// x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
// ggml_set_param(ctx0, x[i]);
// }
@ -576,17 +678,82 @@ int main(int argc, const char ** argv) {
// }
//}
// sgn
{
const int nargs = 1;
for (int ndims = 1; ndims <= 4; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
struct ggml_tensor* f = ggml_sum(ctx0, ggml_sgn(ctx0, x[0]));
check_gradient("sgn", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
}
}
// neg
{
const int nargs = 1;
for (int ndims = 1; ndims <= 4; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
struct ggml_tensor* f = ggml_sum(ctx0, ggml_neg(ctx0, x[0]));
check_gradient("neg", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
}
}
// step
{
const int nargs = 1;
for (int ndims = 1; ndims <= 4; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
struct ggml_tensor* f = ggml_sum(ctx0, ggml_step(ctx0, x[0]));
check_gradient("step", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
}
}
// tanh, not yet fully implemented
if(0)
{
const int nargs = 1;
for (int ndims = 1; ndims <= 4; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
struct ggml_tensor* f = ggml_sum(ctx0, ggml_tanh(ctx0, x[0]));
check_gradient("tanh", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
}
}
// mul_mat
{
const int nargs = 2;
for (int ndims = 2; ndims <= 2; ++ndims) {
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
{
int64_t ne2[4];
get_random_dims(ne2, 4);
ne2[0] = ne[0];
x[1] = get_random_tensor(ctx0, ndims, ne2, -1.0f, 1.0f);
x[1] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
}
ggml_set_param(ctx0, x[0]);
@ -602,13 +769,63 @@ int main(int argc, const char ** argv) {
}
}
// elu, not yet fully implemented
if(0)
{
const int nargs = 1;
for (int ndims = 1; ndims <= 4; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
struct ggml_tensor* f = ggml_sum(ctx0, ggml_elu(ctx0, x[0]));
check_gradient("elu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
}
}
// relu
{
const int nargs = 1;
for (int ndims = 1; ndims <= 4; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
struct ggml_tensor* f = ggml_sum(ctx0, ggml_relu(ctx0, x[0]));
check_gradient("relu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
}
}
// gelu, not yet fully implemented
if(0)
{
const int nargs = 1;
for (int ndims = 1; ndims <= 4; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
struct ggml_tensor* f = ggml_sum(ctx0, ggml_gelu(ctx0, x[0]));
check_gradient("gelu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
}
}
// silu
{
const int nargs = 1;
for (int ndims = 1; ndims <= 2; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
@ -629,7 +846,7 @@ int main(int argc, const char ** argv) {
for (int ndims = 1; ndims <= 2; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
@ -647,8 +864,8 @@ int main(int argc, const char ** argv) {
ne2[0] = 1;
for (int ndims = 1; ndims <= 2; ++ndims) {
x[1] = get_random_tensor(ctx0, 1, ne2, -1.0f, 1.0f);
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[1] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
ggml_set_param(ctx0, x[1]);
@ -659,20 +876,37 @@ int main(int argc, const char ** argv) {
}
}
// cpy
// cpy f32
{
const int nargs = 2;
for (int ndims = 1; ndims <= 2; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
// x[1] is overwritten by x[0], so the gradients don't propagate to x[1]
struct ggml_tensor * f = ggml_sum(ctx0, ggml_cpy(ctx0, x[0], x[1]));
check_gradient("cpy", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
check_gradient("cpy f32", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
}
}
// cpy f16
{
const int nargs = 2;
for (int ndims = 1; ndims <= 2; ++ndims) {
for (int i = 0; i < nargs; ++i) {
x[i] = get_random_tensor_f16(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[i]);
}
// x[1] is overwritten by x[0], so the gradients don't propagate to x[1]
struct ggml_tensor * f = ggml_sum(ctx0, ggml_cpy(ctx0, x[0], x[1]));
check_gradient("cpy f16", ctx0, x, f, ndims, nargs, 1e-1f, 1e-1f, INFINITY);
}
}
@ -689,8 +923,8 @@ int main(int argc, const char ** argv) {
for (int i = 0; i < ndims; ++i) {
ne2[0] *= ne[i];
}
x[0] = get_random_tensor(ctx0, 1, ne2, -1.0f, 1.0f);
x[1] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f);
x[1] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
@ -712,8 +946,8 @@ int main(int argc, const char ** argv) {
for (int i = 0; i < ndims; ++i) {
ne2[0] *= ne[i];
}
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[1] = get_random_tensor(ctx0, 1, ne2, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
x[1] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
@ -729,7 +963,7 @@ int main(int argc, const char ** argv) {
const int nargs = 2;
for (int ndims = 1; ndims <= 4; ++ndims) {
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
get_random_dims(ne2, 1);
@ -737,7 +971,7 @@ int main(int argc, const char ** argv) {
get_random_dims(ne2, 1);
}
x[1] = get_random_tensor(ctx0, 1, ne2, -1.0f, 1.0f);
x[1] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f);
ggml_set_param(ctx0, x[1]);
const int max_offset = MAX(0, ggml_nelements(x[0]) - ggml_nelements(x[1]));
@ -758,7 +992,7 @@ int main(int argc, const char ** argv) {
const int nargs = 2;
for (int ndims = 2; ndims <= 4; ++ndims) {
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
get_random_dims(ne2, 2);
@ -766,7 +1000,7 @@ int main(int argc, const char ** argv) {
get_random_dims(ne2, 2);
}
x[1] = get_random_tensor(ctx0, 2, ne2, -1.0f, 1.0f);
x[1] = get_random_tensor_f32(ctx0, 2, ne2, -1.0f, 1.0f);
ggml_set_param(ctx0, x[1]);
max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]);
@ -790,7 +1024,7 @@ int main(int argc, const char ** argv) {
const int nargs = 2;
for (int ndims = 3; ndims <= 4; ++ndims) {
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
get_random_dims(ne2, 3);
@ -798,7 +1032,7 @@ int main(int argc, const char ** argv) {
get_random_dims(ne2, 3);
}
x[1] = get_random_tensor(ctx0, 3, ne2, -1.0f, 1.0f);
x[1] = get_random_tensor_f32(ctx0, 3, ne2, -1.0f, 1.0f);
ggml_set_param(ctx0, x[1]);
max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]);
@ -824,7 +1058,7 @@ int main(int argc, const char ** argv) {
const int nargs = 2;
for (int ndims = 4; ndims <= 4; ++ndims) {
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
get_random_dims(ne2, 4);
@ -832,7 +1066,7 @@ int main(int argc, const char ** argv) {
get_random_dims(ne2, 4);
}
x[1] = get_random_tensor(ctx0, 4, ne2, -1.0f, 1.0f);
x[1] = get_random_tensor_f32(ctx0, 4, ne2, -1.0f, 1.0f);
ggml_set_param(ctx0, x[1]);
max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]);
@ -858,7 +1092,7 @@ int main(int argc, const char ** argv) {
const int nargs = 2;
for (int ndims = 1; ndims <= 4; ++ndims) {
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
get_random_dims(ne2, 1);
@ -866,7 +1100,7 @@ int main(int argc, const char ** argv) {
get_random_dims(ne2, 1);
}
x[1] = get_random_tensor(ctx0, 1, ne2, -1.0f, 1.0f);
x[1] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f);
ggml_set_param(ctx0, x[1]);
const int max_offset = MAX(0, ggml_nelements(x[0]) - ggml_nelements(x[1]));
@ -887,7 +1121,7 @@ int main(int argc, const char ** argv) {
const int nargs = 1;
for (int ndims = 2; ndims <= 4; ++ndims) {
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
get_random_dims(ne2, 2);
@ -895,7 +1129,7 @@ int main(int argc, const char ** argv) {
get_random_dims(ne2, 2);
}
x[1] = get_random_tensor(ctx0, 2, ne2, -1.0f, 1.0f);
x[1] = get_random_tensor_f32(ctx0, 2, ne2, -1.0f, 1.0f);
ggml_set_param(ctx0, x[1]);
max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]);
@ -915,7 +1149,7 @@ int main(int argc, const char ** argv) {
const int nargs = 1;
for (int ndims = 1; ndims <= 4; ++ndims) {
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
@ -941,7 +1175,7 @@ int main(int argc, const char ** argv) {
const int nargs = 1;
for (int ndims = 1; ndims <= 4; ++ndims) {
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
get_random_dims(ne2, 2);
while (ne2[0]*ne2[1] > ggml_nelements(x[0])) {
@ -971,7 +1205,7 @@ int main(int argc, const char ** argv) {
const int nargs = 1;
for (int ndims = 1; ndims <= 4; ++ndims) {
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
get_random_dims(ne2, 3);
while (ne2[0]*ne2[1]*ne2[2] > ggml_nelements(x[0])) {
@ -1010,7 +1244,7 @@ int main(int argc, const char ** argv) {
for (int i=ndims; i<4; ++i) {
ne2[i] = 1;
}
x[0] = get_random_tensor(ctx0, 4, ne2, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, 4, ne2, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
@ -1043,7 +1277,7 @@ int main(int argc, const char ** argv) {
for (int i=ndims; i<4; ++i) {
ne2[i] = 1;
}
x[0] = get_random_tensor(ctx0, 4, ne2, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, 4, ne2, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
@ -1060,8 +1294,8 @@ int main(int argc, const char ** argv) {
int64_t ne3[4] = {1+irand(ne[1]), 1, 1, 1};
const int nargs = 1;
const int ndims = 2;
x[0] = get_random_tensor(ctx0, ndims, ne2, -1.0f, 1.0f);
x[1] = get_random_tensor_int(ctx0, 1, ne3, 0, ne2[1]);
x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
x[1] = get_random_tensor_i32(ctx0, 1, ne3, 0, ne2[1]);
ggml_set_param(ctx0, x[0]);
@ -1075,7 +1309,7 @@ int main(int argc, const char ** argv) {
const int nargs = 1;
const int ndims = 2;
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
int n_past = irand(ne[0]);
@ -1090,7 +1324,7 @@ int main(int argc, const char ** argv) {
const int nargs = 1;
const int ndims = 2;
x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
int n_past = irand(ne[0]);
@ -1108,7 +1342,7 @@ int main(int argc, const char ** argv) {
get_random_dims(ne2, 4);
for (int ndims = 1; ndims <= 3; ++ndims) {
x[0] = get_random_tensor(ctx0, ndims, ne2, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
struct ggml_tensor * f = ggml_sum(ctx0, ggml_soft_max(ctx0, x[0]));
@ -1125,8 +1359,8 @@ int main(int argc, const char ** argv) {
get_random_dims(ne2, 4);
for (int ndims = 1; ndims <= 3; ++ndims) {
x[0] = get_random_tensor(ctx0, ndims, ne2, -1.0f, 1.0f);
x[1] = get_random_tensor(ctx0, ndims, ne2, 0.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
x[1] = get_random_tensor_f32(ctx0, ndims, ne2, 0.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
struct ggml_tensor * f = ggml_sum(ctx0, ggml_cross_entropy_loss(ctx0, x[0], x[1]));
@ -1136,7 +1370,7 @@ int main(int argc, const char ** argv) {
}
}
// rope
// rope f32
{
const int nargs = 1;
@ -1148,7 +1382,7 @@ int main(int argc, const char ** argv) {
for (int ndims = 3; ndims <= 4; ++ndims) {
for (int mode = 0; mode < 4; ++mode) {
for (int n_past = 1; n_past < ne2[2]; ++n_past) {
x[0] = get_random_tensor(ctx0, ndims, ne2, -1.0f, 1.0f);
x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
@ -1163,14 +1397,48 @@ int main(int argc, const char ** argv) {
struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode, 0));
GGML_PRINT_DEBUG("rope: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode);
check_gradient("rope", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY);
GGML_PRINT_DEBUG("rope f32: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode);
check_gradient("rope f32", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY);
}
}
}
}
// flash_attn
// rope f16
{
const int nargs = 1;
int64_t ne2[4];
get_random_dims(ne2, 4);
ne2[0] += ne2[0] % 2;
int n_rot = ne2[0];
for (int ndims = 3; ndims <= 4; ++ndims) {
for (int mode = 0; mode < 4; ++mode) {
for (int n_past = 1; n_past < ne2[2]; ++n_past) {
x[0] = get_random_tensor_f16(ctx0, ndims, ne2, -1.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
const bool skip_past = (mode & 1);
if (skip_past) {
// we have no past, so this would have to work on uninitialized memory.
// we only test the gradients here;
// skip_past should have no influence on gradient computation.
// so when other modes work, we assume that this does as well.
continue;
}
struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode, 0));
GGML_PRINT_DEBUG("rope f16: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode);
check_gradient("rope f16", ctx0, x, f, ndims, nargs, 1e-1f, 1e-1f, INFINITY);
}
}
}
}
// flash_attn f32
{
const int nargs = 3;
@ -1196,16 +1464,57 @@ int main(int argc, const char ** argv) {
nek[3] = 1;
nev[3] = 1;
}
x[0] = get_random_tensor(ctx0, ndims, neq, -0.1250f, 0.1250f);
x[1] = get_random_tensor(ctx0, ndims, nek, -0.1250f, 0.1250f);
x[2] = get_random_tensor(ctx0, ndims, nev, -0.1250f, 0.1250f);
x[0] = get_random_tensor_f32(ctx0, ndims, neq, -0.1250f, 0.1250f);
x[1] = get_random_tensor_f32(ctx0, ndims, nek, -0.1250f, 0.1250f);
x[2] = get_random_tensor_f32(ctx0, ndims, nev, -0.1250f, 0.1250f);
ggml_set_param(ctx0, x[0]);
ggml_set_param(ctx0, x[1]);
ggml_set_param(ctx0, x[2]);
struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0)));
check_gradient("flash_attn", ctx0, x, f, ndims, nargs, 1.5e-4f, INFINITY, 3.5f);
check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, INFINITY, 3.5f);
}
}
}
// flash_attn f16, not yet fully implemented
if(0)
{
const int nargs = 3;
int64_t ne2[4];
get_random_dims(ne2, 4);
int64_t D = ne2[0];
int64_t N = ne2[1];
int64_t M = ne2[2] + N;
int64_t B = ne2[3];
for (int masked = 0; masked <= 1; ++masked) {
for (int ndims = 2; ndims <= 4; ++ndims) {
int64_t neq[4] = { D, N, B, ne[3] };
int64_t nek[4] = { D, M, B, ne[3] };
int64_t nev[4] = { M, D, B, ne[3] };
if (ndims == 2) {
neq[2] = 1; neq[3] = 1;
nek[2] = 1; nek[3] = 1;
nev[2] = 1; nev[3] = 1;
} else if (ndims == 3) {
neq[3] = 1;
nek[3] = 1;
nev[3] = 1;
}
x[0] = get_random_tensor_f16(ctx0, ndims, neq, -0.1250f, 0.1250f);
x[1] = get_random_tensor_f16(ctx0, ndims, nek, -0.1250f, 0.1250f);
x[2] = get_random_tensor_f16(ctx0, ndims, nev, -0.1250f, 0.1250f);
ggml_set_param(ctx0, x[0]);
ggml_set_param(ctx0, x[1]);
ggml_set_param(ctx0, x[2]);
struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0)));
check_gradient("flash_attn f16", ctx0, x, f, ndims, nargs, 1.5e-4f, INFINITY, 3.5f);
}
}
}

6
tests/test-opt.c
View file

@ -125,9 +125,9 @@ int main(void) {
};
struct ggml_context * ctx = ggml_init(params);
int64_t ne1[4] = {4, 1024, 1, 1};
int64_t ne2[4] = {4, 2048, 1, 1};;
int64_t ne3[4] = {1024, 2048, 1, 1};
int64_t ne1[4] = {4, 128, 1, 1};
int64_t ne2[4] = {4, 256, 1, 1};;
int64_t ne3[4] = {128, 256, 1, 1};
struct ggml_tensor * a = get_random_tensor(ctx, 2, ne1, -1, +1);
struct ggml_tensor * b = get_random_tensor(ctx, 2, ne2, -1, +1);