diff options
| -rw-r--r-- | CMakeLists.txt | 5 | ||||
| -rw-r--r-- | Makefile | 4 | ||||
| -rw-r--r-- | examples/common.cpp | 6 | ||||
| -rw-r--r-- | examples/main/main.cpp | 11 | ||||
| -rw-r--r-- | examples/perplexity/perplexity.cpp | 20 | ||||
| -rw-r--r-- | examples/quantize/quantize.cpp | 4 | ||||
| -rw-r--r-- | ggml.c | 138 | ||||
| -rw-r--r-- | llama.cpp | 52 | ||||
| -rw-r--r-- | llama.h | 8 | ||||
| -rw-r--r-- | tests/CMakeLists.txt | 1 | ||||
| -rw-r--r-- | tests/test-double-float.c | 53 |
11 files changed, 185 insertions, 117 deletions
diff --git a/CMakeLists.txt b/CMakeLists.txt index 27a222a..241be4c 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -124,17 +124,18 @@ if (LLAMA_ALL_WARNINGS) -Wall -Wextra -Wpedantic - -Wshadow -Wcast-qual + -Wdouble-promotion + -Wshadow -Wstrict-prototypes -Wpointer-arith - -Wno-unused-function ) set(cxx_flags -Wall -Wextra -Wpedantic -Wcast-qual + -Wdouble-promotion ) else() # todo : msvc @@ -35,6 +35,10 @@ CFLAGS = -I. -O3 -DNDEBUG -std=c11 -fPIC CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC LDFLAGS = +# warnings +CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith -Wno-unused-function +CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function + # OS specific # TODO: support Windows ifeq ($(UNAME_S),Linux) diff --git a/examples/common.cpp b/examples/common.cpp index 880ebe9..af3ad9e 100644 --- a/examples/common.cpp +++ b/examples/common.cpp @@ -215,13 +215,13 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { fprintf(stderr, " prompt file to start generation.\n"); fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d, -1 = infinity)\n", params.n_predict); fprintf(stderr, " --top_k N top-k sampling (default: %d)\n", params.top_k); - fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", params.top_p); + fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", (double)params.top_p); fprintf(stderr, " --repeat_last_n N last n tokens to consider for penalize (default: %d)\n", params.repeat_last_n); - fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", params.repeat_penalty); + fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", (double)params.repeat_penalty); fprintf(stderr, " -c N, --ctx_size N size of the prompt context (default: %d)\n", params.n_ctx); fprintf(stderr, " --ignore-eos ignore end of stream token and continue generating\n"); fprintf(stderr, " --memory_f32 use f32 instead of f16 for memory key+value\n"); - fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp); + fprintf(stderr, " --temp N temperature (default: %.1f)\n", (double)params.temp); fprintf(stderr, " --n_parts N number of model parts (default: -1 = determine from dimensions)\n"); fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch); fprintf(stderr, " --perplexity compute perplexity over the prompt\n"); diff --git a/examples/main/main.cpp b/examples/main/main.cpp index d5ab2cf..3130aef 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -209,7 +209,8 @@ int main(int argc, char ** argv) { fprintf(stderr, "Input prefix: '%s'\n", params.input_prefix.c_str()); } } - fprintf(stderr, "sampling: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty); + fprintf(stderr, "sampling: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", + params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty); fprintf(stderr, "generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep); fprintf(stderr, "\n\n"); @@ -274,10 +275,10 @@ int main(int argc, char ** argv) { if ((int) embd_inp.size() <= n_consumed && !is_interacting) { // out of user input, sample next token - const float top_k = params.top_k; - const float top_p = params.top_p; - const float temp = params.temp; - const float repeat_penalty = params.repeat_penalty; + const int32_t top_k = params.top_k; + const float top_p = params.top_p; + const float temp = params.temp; + const float repeat_penalty = params.repeat_penalty; llama_token id = 0; diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 75d526d..07ed0a8 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -1,15 +1,17 @@ #include "common.h" #include "llama.h" -std::vector<double> softmax(const std::vector<float>& logits) { - std::vector<double> probs(logits.size()); +#include <cmath> + +std::vector<float> softmax(const std::vector<float>& logits) { + std::vector<float> probs(logits.size()); float max_logit = logits[0]; for (float v : logits) max_logit = std::max(max_logit, v); double sum_exp = 0.0; for (size_t i = 0; i < logits.size(); i++) { // Subtract the maximum logit value from the current logit value for numerical stability - float logit = logits[i] - max_logit; - double exp_logit = std::exp(logit); + const float logit = logits[i] - max_logit; + const float exp_logit = expf(logit); sum_exp += exp_logit; probs[i] = exp_logit; } @@ -24,14 +26,16 @@ void perplexity(llama_context * ctx, const gpt_params & params) { auto tokens = ::llama_tokenize(ctx, params.prompt, true); int count = 0; - double nll = 0.0; int seq_count = tokens.size() / params.n_ctx; + double nll = 0.0; + fprintf(stderr, "%s : calculating perplexity over %d chunks\n", __func__, seq_count); for (int i = 0; i < seq_count; ++i) { int start = i * params.n_ctx; - int end = start + params.n_ctx - 1; + int end = start + params.n_ctx - 1; // TODO: this is not optimal, e.g. it makes the batch 511 instead of 512 + // it is better to always be power of 2 for better performance std::vector<llama_token> embd(tokens.begin() + start, tokens.begin() + end); auto start_t = std::chrono::high_resolution_clock::now(); if (llama_eval(ctx, embd.data(), embd.size(), 0, params.n_threads)) { @@ -40,7 +44,7 @@ void perplexity(llama_context * ctx, const gpt_params & params) { } auto end_t = std::chrono::high_resolution_clock::now(); if (i == 0) { - double seconds = std::chrono::duration<double>(end_t - start_t).count(); + const float seconds = std::chrono::duration<float>(end_t - start_t).count(); printf("%.2f seconds per pass - ETA %.2f hours\n", seconds, (seconds * seq_count) / (60.0*60.0)); } // We get the logits for all the tokens in the context window (params.n_ctx) @@ -63,7 +67,7 @@ void perplexity(llama_context * ctx, const gpt_params & params) { std::vector<float> tok_logits( logits + j * n_vocab, logits + (j + 1) * n_vocab); - double prob = softmax(tok_logits)[tokens[start + j + 1]]; + const float prob = softmax(tok_logits)[tokens[start + j + 1]]; nll += -std::log(prob); ++count; } diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 3888ff5..b444328 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -50,8 +50,8 @@ int main(int argc, char ** argv) { const int64_t t_main_end_us = ggml_time_us(); printf("\n"); - printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us/1000.0f); - printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f); + printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us/1000.0); + printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0); } return 0; @@ -150,10 +150,10 @@ typedef double ggml_float; // #include <arm_neon.h> -#define GGML_COMPUTE_FP16_TO_FP32(x) (x) +#define GGML_COMPUTE_FP16_TO_FP32(x) ((float) (x)) #define GGML_COMPUTE_FP32_TO_FP16(x) (x) -#define GGML_FP16_TO_FP32(x) (x) +#define GGML_FP16_TO_FP32(x) ((float) (x)) #define GGML_FP32_TO_FP16(x) (x) #else @@ -322,7 +322,7 @@ inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { // note: do not use these inside ggml.c // these are meant to be used via the ggml.h API float ggml_fp16_to_fp32(ggml_fp16_t x) { - return GGML_FP16_TO_FP32(x); + return (float) GGML_FP16_TO_FP32(x); } ggml_fp16_t ggml_fp32_to_fp16(float x) { @@ -488,8 +488,8 @@ static void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * r const float v0 = x[i*QK + l + 0]*id; const float v1 = x[i*QK + l + 1]*id; - const uint8_t vi0 = ((int8_t) (round(v0))) + 8; - const uint8_t vi1 = ((int8_t) (round(v1))) + 8; + const uint8_t vi0 = (int8_t)roundf(v0) + 8; + const uint8_t vi1 = (int8_t)roundf(v1) + 8; assert(vi0 >= 0 && vi0 < 16); assert(vi1 >= 0 && vi1 < 16); @@ -566,7 +566,7 @@ static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int MAX(vgetq_lane_f32(amaxv[0], 2), vgetq_lane_f32(amaxv[0], 3))); const float d = amax / ((1 << 3) - 1); - const float id = d ? 1.0/d : 0.0; + const float id = d ? 1.0f/d : 0.0f; y[i].d = d; @@ -716,8 +716,8 @@ static void quantize_row_q4_1(const float * restrict x, void * restrict vy, int const float v0 = (x[i*QK + l + 0] - min)*id; const float v1 = (x[i*QK + l + 1] - min)*id; - const uint8_t vi0 = round(v0); - const uint8_t vi1 = round(v1); + const uint8_t vi0 = roundf(v0); + const uint8_t vi1 = roundf(v1); assert(vi0 >= 0 && vi0 < 16); assert(vi1 >= 0 && vi1 < 16); @@ -1001,7 +1001,7 @@ static void dequantize_row_q4_1(const void * restrict vx, float * restrict y, in } \ const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 (x[0])); \ const float32x4_t t1 = vcvt_f32_f16(vget_high_f16(x[0])); \ - res = vaddvq_f32(vaddq_f32(t0, t1)); \ + res = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1)); \ } #define GGML_F16_VEC GGML_F16x8 @@ -1437,9 +1437,8 @@ inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, co inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]/y[i]; } inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) { - ggml_float sumf = 0.0; - #ifdef GGML_SIMD + float sumf = 0.0f; const int np = (n & ~(GGML_F32_STEP - 1)); GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO }; @@ -1465,8 +1464,9 @@ inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float } #else // scalar + ggml_float sumf = 0.0; for (int i = 0; i < n; ++i) { - sumf += x[i]*y[i]; + sumf += (ggml_float)(x[i]*y[i]); } #endif @@ -1529,11 +1529,11 @@ inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t // leftovers for (int i = np; i < n; ++i) { - sumf += GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]); + sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); } #else for (int i = 0; i < n; ++i) { - sumf += GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i]); + sumf += (ggml_float)(GGML_FP16_TO_FP32(x[i])*GGML_FP16_TO_FP32(y[i])); } #endif @@ -1549,7 +1549,7 @@ inline static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void const block_q4_0 * restrict x = vx; const block_q4_0 * restrict y = vy; - float sumf = 0.0; + ggml_float sumf = 0.0; #if defined(__ARM_NEON) float sum0 = 0.0f; @@ -1644,7 +1644,7 @@ inline static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void #endif } - sumf = sum0 + sum1; + sumf = (ggml_float)(sum0 + sum1); #elif defined(__AVX512F__) // Initialize accumulator with zeros __m512 acc0 = _mm512_setzero_ps(); @@ -1972,13 +1972,13 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * re // leftovers for (int i = np; i < n; ++i) { for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { - sumf[j] += GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i]); + sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); } } #else for (int i = 0; i < n; ++i) { for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) { - sumf[j] += GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i]); + sumf[j] += (ggml_float)(GGML_FP16_TO_FP32(x[j][i])*GGML_FP16_TO_FP32(y[i])); } } #endif @@ -2049,19 +2049,19 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { #endif } -inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, x, x); *s = sqrt(*s); } +inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, x, x); *s = sqrtf(*s); } inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; } -inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrt(x[i]); } +inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); } inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); } inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); } inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; } inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } -static const ggml_float GELU_COEF_A = 0.044715; -static const ggml_float SQRT_2_OVER_PI = 0.79788456080286535587989211986876; +static const float GELU_COEF_A = 0.044715f; +static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; inline static float ggml_gelu_f32(float x) { - return 0.5*x*(1.0 + tanh(SQRT_2_OVER_PI*x*(1.0 + GELU_COEF_A*x*x))); + return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))); } inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { @@ -2090,7 +2090,7 @@ inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { // Sigmoid Linear Unit (SiLU) function inline static float ggml_silu_f32(float x) { - return x/(1.0 + exp(-x)); + return x/(1.0f + expf(-x)); } inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { @@ -2121,7 +2121,7 @@ inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) { #ifndef GGML_USE_ACCELERATE ggml_float sum = 0.0; for (int i = 0; i < n; ++i) { - sum += x[i]; + sum += (ggml_float)x[i]; } *s = sum; #else @@ -2131,7 +2131,7 @@ inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) { inline static void ggml_vec_max_f32(const int n, float * s, const float * x) { #ifndef GGML_USE_ACCELERATE - ggml_float max = -INFINITY; + float max = -INFINITY; for (int i = 0; i < n; ++i) { max = MAX(max, x[i]); } @@ -2141,7 +2141,10 @@ inline static void ggml_vec_max_f32(const int n, float * s, const float * x) { #endif } -inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x) { ggml_vec_norm_f32(n, s, x); *s = 1./(*s); } +inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x) { + ggml_vec_norm_f32(n, s, x); + *s = 1.f/(*s); +} // // logging @@ -2540,7 +2543,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { const float f = table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(ii); table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f)); table_silu_f16[i] = GGML_FP32_TO_FP16(ggml_silu_f32(f)); - table_exp_f16[i] = GGML_FP32_TO_FP16(exp(f)); + table_exp_f16[i] = GGML_FP32_TO_FP16(expf(f)); } const uint64_t t_end = ggml_time_us(); UNUSED(t_end); @@ -5583,7 +5586,7 @@ static void ggml_compute_forward_norm_f32( const size_t nb2 = dst->nb[2]; const size_t nb3 = dst->nb[3]; - const ggml_float eps = 1e-5f; // TODO: make this a parameter + const float eps = 1e-5f; // TODO: make this a parameter // TODO: optimize for (int i03 = 0; i03 < ne03; i03++) { @@ -5591,23 +5594,24 @@ static void ggml_compute_forward_norm_f32( for (int i01 = ith; i01 < ne01; i01 += nth) { const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - ggml_float mean = 0.0; + ggml_float sum = 0.0; for (int i00 = 0; i00 < ne00; i00++) { - mean += x[i00]; + sum += (ggml_float)x[i00]; } - mean /= ne00; + float mean = sum/ne00; float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); ggml_float sum2 = 0.0; for (int i00 = 0; i00 < ne00; i00++) { - ggml_float v = x[i00] - mean; + float v = x[i00] - mean; y[i00] = v; - sum2 += v*v; + sum2 += (ggml_float)(v*v); } - const float scale = 1.0/sqrt(sum2/ne00 + eps); + float variance = sum2/ne00; + const float scale = 1.0f/sqrtf(variance + eps); ggml_vec_scale_f32(ne00, y, scale); } @@ -5665,7 +5669,7 @@ static void ggml_compute_forward_rms_norm_f32( const size_t nb2 = dst->nb[2]; const size_t nb3 = dst->nb[3]; - const ggml_float eps = 1e-6f; // TODO: make this a parameter + const float eps = 1e-6f; // TODO: make this a parameter // TODO: optimize for (int i03 = 0; i03 < ne03; i03++) { @@ -5673,12 +5677,12 @@ static void ggml_compute_forward_rms_norm_f32( for (int i01 = ith; i01 < ne01; i01 += nth) { const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); - ggml_float mean = 0.0; + ggml_float sum = 0.0; for (int i00 = 0; i00 < ne00; i00++) { - mean += x[i00] * x[i00]; + sum += (ggml_float)(x[i00] * x[i00]); } - mean /= ne00; + float mean = sum/ne00; float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3); @@ -5687,7 +5691,7 @@ static void ggml_compute_forward_rms_norm_f32( // y[i00] = x[i00]; // } - const float scale = 1.0/sqrt(mean + eps); + const float scale = 1.0f/sqrtf(mean + eps); ggml_vec_scale_f32(ne00, y, scale); } @@ -6913,12 +6917,12 @@ static void ggml_compute_forward_soft_max_f32( ggml_fp16_t s = GGML_FP32_TO_FP16(p[i] - max); memcpy(&scvt, &s, sizeof(scvt)); const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]); - sum += val; + sum += (ggml_float)val; p[i] = val; } } - assert(sum > 0.0f); + assert(sum > 0.0); sum = 1.0/sum; ggml_vec_scale_f32(nc, p, sum); @@ -6994,16 +6998,16 @@ static void ggml_compute_forward_rope_f32( const int p = (mode == 0 ? n_past + i2 : i2); for (int i1 = 0; i1 < ne1; i1++) { for (int i0 = 0; i0 < n_dims; i0 += 2) { - const double theta = pow(10000.0, ((double)-i0)/n_dims); + const float theta = powf(10000.0, ((float)-i0)/n_dims); - const double cos_theta = cos(p*theta); - const double sin_theta = sin(p*theta); + const float cos_theta = cosf(p*theta); + const float sin_theta = sinf(p*theta); const float * const src = (float *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - double x0 = src[0]; - double x1 = src[1]; + const float x0 = src[0]; + const float x1 = src[1]; dst_data[0] = x0*cos_theta - x1*sin_theta; dst_data[1] = x0*sin_theta + x1*cos_theta; @@ -7050,16 +7054,16 @@ static void ggml_compute_forward_rope_f16( const int p = (mode == 0 ? n_past + i2 : i2); for (int i1 = 0; i1 < ne1; i1++) { for (int i0 = 0; i0 < n_dims; i0 += 2) { - const double theta = pow(10000.0, ((double)-i0)/n_dims); + const float theta = powf(10000.0, ((float)-i0)/n_dims); - const double cos_theta = cos(p*theta); - const double sin_theta = sin(p*theta); + const float cos_theta = cosf(p*theta); + const float sin_theta = sinf(p*theta); const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - double x0 = ggml_fp16_to_fp32(src[0]); - double x1 = ggml_fp16_to_fp32(src[1]); + const float x0 = ggml_fp16_to_fp32(src[0]); + const float x1 = ggml_fp16_to_fp32(src[1]); dst_data[0] = ggml_fp32_to_fp16(x0*cos_theta - x1*sin_theta); dst_data[1] = ggml_fp32_to_fp16(x0*sin_theta + x1*cos_theta); @@ -7735,7 +7739,7 @@ static void ggml_compute_forward_flash_attn_f32( const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); - const float scale = 1.0/sqrt((double) D); + const float scale = 1.0f/sqrtf(D); //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); @@ -7782,7 +7786,7 @@ static void ggml_compute_forward_flash_attn_f32( float max = -INFINITY; ggml_vec_max_f32(M, &max, S); - float sum = 0.0f; + ggml_float sum = 0.0; { #ifdef GGML_SOFT_MAX_ACCELERATE max = -max; @@ -7803,7 +7807,7 @@ static void ggml_compute_forward_flash_attn_f32( ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max); memcpy(&scvt[j], &s, sizeof(uint16_t)); const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]); - sump[j] += val; + sump[j] += (ggml_float)val; SS[j] = val; } } @@ -7815,7 +7819,7 @@ static void ggml_compute_forward_flash_attn_f32( #endif } - assert(sum > 0.0f); + assert(sum > 0.0); sum = 1.0/sum; ggml_vec_scale_f32(M, S, sum); @@ -7944,7 +7948,7 @@ static void ggml_compute_forward_flash_attn_f16( const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); - const float scale = 1.0/sqrt((double) D); + const float scale = 1.0f/sqrtf(D); //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); @@ -8008,7 +8012,7 @@ static void ggml_compute_forward_flash_attn_f16( float max = -INFINITY; ggml_vec_max_f32(M, &max, S); - float sum = 0.0f; + ggml_float sum = 0.0; { #ifdef GGML_SOFT_MAX_ACCELERATE max = -max; @@ -8029,7 +8033,7 @@ static void ggml_compute_forward_flash_attn_f16( ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max); memcpy(&scvt[j], &s, sizeof(uint16_t)); const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]); - sump[j] += val; + sump[j] += (ggml_float)val; SS[j] = val; } } @@ -8041,7 +8045,7 @@ static void ggml_compute_forward_flash_attn_f16( #endif } - assert(sum > 0.0f); + assert(sum > 0.0); sum = 1.0/sum; ggml_vec_scale_f32(M, S, sum); @@ -9566,7 +9570,7 @@ label=\"%d [%d, %d] | <x>%s", fprintf(fp, " \"%p\" [ \ style = filled; fillcolor = %s; shape = record; \ label=\"<x>%.1e\"; ]\n", - (void *) node, color, ggml_get_f32_1d(node, 0)); + (void *) node, color, (double)ggml_get_f32_1d(node, 0)); } else { fprintf(fp, " \"%p\" [ \ style = filled; fillcolor = %s; shape = record; \ @@ -9804,7 +9808,7 @@ static enum ggml_opt_result ggml_opt_adam( if (params.past <= t) { const float rate = (pf[t%params.past] - fx)/fx; - if (fabs(rate) < params.delta) { + if (fabsf(rate) < params.delta) { return GGML_OPT_OK; } } @@ -9883,7 +9887,7 @@ static enum ggml_opt_result linesearch_backtracking( const float dec = 0.5f; const float inc = 2.1f; - if (*step <= 0.) { + if (*step <= 0.f) { return GGML_LINESEARCH_INVALID_PARAMETERS; } @@ -9971,7 +9975,7 @@ static enum ggml_opt_result ggml_opt_lbfgs( struct ggml_cgraph * gb) { if (params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE || params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) { - if (params.lbfgs.wolfe <= params.lbfgs.ftol || 1. <= params.lbfgs.wolfe) { + if (params.lbfgs.wolfe <= params.lbfgs.ftol || 1.f <= params.lbfgs.wolfe) { return GGML_OPT_INVALID_WOLFE; } } @@ -10092,8 +10096,8 @@ static enum ggml_opt_result ggml_opt_lbfgs( GGML_PRINT_DEBUG("f = %10.6f\n", ggml_get_f32_1d(f, 0)); - if (xnorm < 1.0) { - xnorm = 1.0; + if (xnorm < 1.0f) { + xnorm = 1.0f; } if (gnorm/xnorm <= params.lbfgs.eps) { // converged @@ -10106,7 +10110,7 @@ static enum ggml_opt_result ggml_opt_lbfgs( if (params.past <= k) { const float rate = (pf[k%params.past] - fx)/fx; - if (fabs(rate) < params.delta) { + if (fabsf(rate) < params.delta) { return GGML_OPT_OK; } } @@ -779,8 +779,8 @@ static bool llama_model_load( // progress if (progress_callback) { - double current_file_progress = double(size_t(fin.tellg()) - file_offset) / double(file_size - file_offset); - double current_progress = (double(i) + current_file_progress) / double(n_parts); + float current_file_progress = float(size_t(fin.tellg()) - file_offset) / float(file_size - file_offset); + float current_progress = (float(i) + current_file_progress) / float(n_parts); progress_callback(current_progress, progress_callback_user_data); } if (model.n_loaded % 8 == 0) { @@ -922,7 +922,7 @@ static bool llama_eval_internal( struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, - ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))); + ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head))); // KQ_masked = mask_past(KQ_scaled) struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past); @@ -1240,12 +1240,12 @@ static std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, co // sampling // -static void sample_top_k(std::vector<std::pair<double, llama_vocab::id>> & logits_id, int top_k) { +static void sample_top_k(std::vector<std::pair<float, llama_vocab::id>> & logits_id, int top_k) { // find the top k tokens std::partial_sort( logits_id.begin(), logits_id.begin() + top_k, logits_id.end(), - [](const std::pair<double, llama_vocab::id> & a, const std::pair<double, llama_vocab::id> & b) { + [](const std::pair<float, llama_vocab::id> & a, const std::pair<float, llama_vocab::id> & b) { return a.first > b.first; }); @@ -1256,9 +1256,9 @@ static llama_vocab::id llama_sample_top_p_top_k( llama_context & lctx, const std::vector<llama_vocab::id> & last_n_tokens, int top_k, - double top_p, - double temp, - double repeat_penalty) { + float top_p, + float temp, + float repeat_penalty) { auto & rng = lctx.rng; const int n_logits = lctx.model.hparams.n_vocab; @@ -1266,17 +1266,17 @@ static llama_vocab::id llama_sample_top_p_top_k( const auto & logits = lctx.logits; const auto * plogits = logits.data() + logits.size() - n_logits; - std::vector<std::pair<double, llama_vocab::id>> logits_id; + std::vector<std::pair<float, llama_vocab::id>> logits_id; logits_id.reserve(n_logits); { - const double scale = 1.0/temp; + const float scale = 1.0f/temp; for (int i = 0; i < n_logits; ++i) { // repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858) // credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) { // if score < 0 then repetition penalty has to multiplied to reduce the previous token probability - if (plogits[i] < 0.0) { + if (plogits[i] < 0.0f) { logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i)); } else { logits_id.push_back(std::make_pair(plogits[i]*scale/repeat_penalty, i)); @@ -1289,18 +1289,18 @@ static llama_vocab::id llama_sample_top_p_top_k( sample_top_k(logits_id, top_k); - double maxl = -std::numeric_limits<double>::infinity(); + float maxl = -std::numeric_limits<float>::infinity(); for (const auto & kv : logits_id) { maxl = std::max(maxl, kv.first); } // compute probs for the top k tokens - std::vector<double> probs; + std::vector<float> probs; probs.reserve(logits_id.size()); double sum = 0.0; for (const auto & kv : logits_id) { - double p = exp(kv.first - maxl); + const float p = expf(kv.first - maxl); probs.push_back(p); sum += p; } @@ -1310,8 +1310,8 @@ static llama_vocab::id llama_sample_top_p_top_k( p /= sum; } - if (top_p < 1.0f) { - double cumsum = 0.0f; + if (top_p < 1.0) { + double cumsum = 0.0; for (int i = 0; i < (int) probs.size(); i++) { cumsum += probs[i]; if (cumsum >= top_p) { @@ -1590,7 +1590,7 @@ static bool llama_model_quantize_internal(const std::string & fname_inp, const s } for (int i = 0; i < (int) hist_cur.size(); ++i) { - printf("%5.3f ", hist_cur[i] / (float)nelements); + printf("%5.3f ", hist_cur[i] / float(nelements)); } printf("\n"); } else { @@ -1613,7 +1613,7 @@ static bool llama_model_quantize_internal(const std::string & fname_inp, const s printf("%s: hist: ", __func__); for (int i = 0; i < (int) hist_all.size(); ++i) { - printf("%5.3f ", hist_all[i] / (float)sum_all); + printf("%5.3f ", hist_all[i] / float(sum_all)); } printf("\n"); } @@ -1795,9 +1795,9 @@ llama_token llama_sample_top_p_top_k( const llama_token * last_n_tokens_data, int last_n_tokens_size, int top_k, - double top_p, - double temp, - double repeat_penalty) { + float top_p, + float temp, + float repeat_penalty) { const int64_t t_start_sample_us = ggml_time_us(); llama_token result = 0; @@ -1828,11 +1828,11 @@ void llama_print_timings(struct llama_context * ctx) { const int32_t n_p_eval = std::max(1, ctx->n_p_eval); fprintf(stderr, "\n"); - fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0f); - fprintf(stderr, "%s: sample time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->t_sample_us, n_sample, 1e-3f * ctx->t_sample_us / n_sample); - fprintf(stderr, "%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token)\n", __func__, 1e-3f * ctx->t_p_eval_us, n_p_eval, 1e-3f * ctx->t_p_eval_us / n_p_eval); - fprintf(stderr, "%s: eval time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->t_eval_us, n_eval, 1e-3f * ctx->t_eval_us / n_eval); - fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0f); + fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0); + fprintf(stderr, "%s: sample time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3 * ctx->t_sample_us, n_sample, 1e-3 * ctx->t_sample_us / n_sample); + fprintf(stderr, "%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token)\n", __func__, 1e-3 * ctx->t_p_eval_us, n_p_eval, 1e-3 * ctx->t_p_eval_us / n_p_eval); + fprintf(stderr, "%s: eval time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3 * ctx->t_eval_us, n_eval, 1e-3 * ctx->t_eval_us / n_eval); + fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0); } void llama_reset_timings(struct llama_context * ctx) { @@ -45,7 +45,7 @@ extern "C" { } llama_token_data; - typedef void (*llama_progress_callback)(double progress, void *ctx); + typedef void (*llama_progress_callback)(float progress, void *ctx); struct llama_context_params { int n_ctx; // text context @@ -134,9 +134,9 @@ extern "C" { const llama_token * last_n_tokens_data, int last_n_tokens_size, int top_k, - double top_p, - double temp, - double repeat_penalty); + float top_p, + float temp, + float repeat_penalty); // Performance information LLAMA_API void llama_print_timings(struct llama_context * ctx); diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index b44d7fe..157d733 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -5,5 +5,6 @@ function(llama_add_test source) add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}> ${ARGN}) endfunction() +# llama_add_test(test-double-float.c) # SLOW llama_add_test(test-quantize.c) llama_add_test(test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab.bin) diff --git a/tests/test-double-float.c b/tests/test-double-float.c new file mode 100644 index 0000000..89dafc9 --- /dev/null +++ b/tests/test-double-float.c @@ -0,0 +1,53 @@ +// These tests may take a long time! +// They are to prove that conversion from double to float of various functions in ggml.c doesn't affect the result. +// This is done by checking all finite (non-NaN, non-infinite) floats. + +#undef NDEBUG +#include <assert.h> +#include <immintrin.h> +#include <math.h> +#include <stdint.h> + +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wdouble-promotion" + +// ggml.c::quantize_row_q4_0_reference +inline static uint8_t round_orig(float v0) { return ((int8_t) (round(v0))) + 8; } + +// ggml.c::ggml_silu_f32 +inline static float silu_orig(float x) { + return x/(1.0 + exp(-x)); +} + +#pragma GCC diagnostic pop + +// ggml.c::quantize_row_q4_0_reference +inline static uint8_t round_float(float v0) { return (int8_t)roundf(v0) + 8; } + +// ggml.c::ggml_silu_f32 +inline static float silu_float(float x) { + return x/(1.0f + expf(-x)); +} + +int main(void) { + uint32_t x = UINT32_MAX; + do { + float f = *(float *)&x; + assert(!isfinite(f) || (round_orig(f) == round_float(f))); + } while (x--); + +#ifdef __F16C__ + // GELU and SILU implementations are used with a FP16 lookup table. + // The original and float-only results are not equal for all inputs after converting to FP16. + // GELU is an approximation anyway (tanh), not tested here. + // For SILU, verify that the results are at least the closest floating point numbers, if the FP16 values don't match. + for (x = 0; x <= UINT16_MAX; x++) { + float f = _cvtsh_ss(x); + const float so = silu_orig(f); + const float sf = silu_float(f); + assert( (_cvtss_sh(so, 0) == _cvtss_sh(sf, 0)) + || (nextafterf(so, sf) == sf) + || (nextafterf(sf, so) == so)); + } +#endif +} |