mirror of
https://github.com/ggerganov/whisper.cpp.git
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wip : rpi4 support
This commit is contained in:
Georgi Gerganov
parent
ce1fe95902
commit
167324584b
23
Makefile
23
Makefile
@ -15,10 +15,12 @@ CXXFLAGS += -Wall -Wextra -Wno-unused-parameter -Wno-unused-function
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# OS specific
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# TODO: support Windows
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ifeq ($(UNAME_S),Linux)
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CFLAGS += -pthread
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CFLAGS += -pthread
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CXXFLAGS += -pthread
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endif
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ifeq ($(UNAME_S),Darwin)
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CFLAGS += -pthread
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CFLAGS += -pthread
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CXXFLAGS += -pthread
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endif
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# Architecture specific
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@ -26,14 +28,21 @@ ifeq ($(UNAME_P),x86_64)
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CFLAGS += -mavx -mavx2 -mfma -mf16c
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endif
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ifneq ($(filter arm%,$(UNAME_P)),)
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CFLAGS += -mfpu=neon
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# Mac M1
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endif
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ifneq ($(filter aarch64%,$(UNAME_M)),)
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CFLAGS += -mfpu=neon
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ifneq ($(filter aarch64%,$(UNAME_P)),)
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endif
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ifneq ($(filter armv6%,$(UNAME_M)),)
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# Raspberry Pi 1, 2, 3
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CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
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endif
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ifneq ($(filter armv%,$(UNAME_M)),)
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ifneq ($(filter armv7%,$(UNAME_M)),)
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# Raspberry Pi 4
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CFLAGS += -mcpu=cortex-a72 -mfloat-abi=hard -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
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CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
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endif
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ifneq ($(filter armv8%,$(UNAME_M)),)
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# Raspberry Pi 4
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CFLAGS += -mfp16-format=ieee -mno-unaligned-access
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endif
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#
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130
ggml.c
130
ggml.c
@ -1,5 +1,6 @@
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#include "ggml.h"
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#include <alloca.h>
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#include <assert.h>
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#include <time.h>
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#include <math.h>
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@ -12,7 +13,12 @@
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#include <pthread.h>
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#define GGML_DEBUG 0
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#define GGML_MEM_ALIGN 16
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#if UINTPTR_MAX == 0xFFFFFFFF
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#define GGML_MEM_ALIGN 4
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#else
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#define GGML_MEM_ALIGN 16
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#endif
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#define MAX(a, b) ((a) > (b) ? (a) : (b))
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#define MIN(a, b) ((a) < (b) ? (a) : (b))
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@ -305,6 +311,7 @@ inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t
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#ifdef __ARM_NEON
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const int n32 = (n & ~31);
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#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
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float16x8_t sum0 = vdupq_n_f16(0);
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float16x8_t sum1 = vdupq_n_f16(0);
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float16x8_t sum2 = vdupq_n_f16(0);
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@ -344,6 +351,61 @@ inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t
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float32x2_t sumf32 = vadd_f32(vget_low_f32(sum0f32), vget_high_f32(sum0f32));
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sumf = vget_lane_f32(sumf32, 0) + vget_lane_f32(sumf32, 1);
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#else
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float32x4_t sum0 = vdupq_n_f32(0);
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float32x4_t sum1 = vdupq_n_f32(0);
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float32x4_t sum2 = vdupq_n_f32(0);
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float32x4_t sum3 = vdupq_n_f32(0);
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float32x4_t sum4 = vdupq_n_f32(0);
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float32x4_t sum5 = vdupq_n_f32(0);
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float32x4_t sum6 = vdupq_n_f32(0);
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float32x4_t sum7 = vdupq_n_f32(0);
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float32x4_t x0, x1, x2, x3, x4, x5, x6, x7;
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float32x4_t y0, y1, y2, y3, y4, y5, y6, y7;
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for (int i = 0; i < n32; i += 32) {
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x0 = vcvt_f32_f16(vld1_f16(x + i + 0 ));
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x1 = vcvt_f32_f16(vld1_f16(x + i + 4 ));
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x2 = vcvt_f32_f16(vld1_f16(x + i + 8 ));
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x3 = vcvt_f32_f16(vld1_f16(x + i + 12));
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x4 = vcvt_f32_f16(vld1_f16(x + i + 16));
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x5 = vcvt_f32_f16(vld1_f16(x + i + 20));
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x6 = vcvt_f32_f16(vld1_f16(x + i + 24));
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x7 = vcvt_f32_f16(vld1_f16(x + i + 28));
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y0 = vcvt_f32_f16(vld1_f16(y + i + 0 ));
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y1 = vcvt_f32_f16(vld1_f16(y + i + 4 ));
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y2 = vcvt_f32_f16(vld1_f16(y + i + 8 ));
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y3 = vcvt_f32_f16(vld1_f16(y + i + 12));
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y4 = vcvt_f32_f16(vld1_f16(y + i + 16));
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y5 = vcvt_f32_f16(vld1_f16(y + i + 20));
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y6 = vcvt_f32_f16(vld1_f16(y + i + 24));
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y7 = vcvt_f32_f16(vld1_f16(y + i + 28));
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sum0 = vfmaq_f32(sum0, x0, y0);
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sum1 = vfmaq_f32(sum1, x1, y1);
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sum2 = vfmaq_f32(sum2, x2, y2);
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sum3 = vfmaq_f32(sum3, x3, y3);
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sum4 = vfmaq_f32(sum4, x4, y4);
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sum5 = vfmaq_f32(sum5, x5, y5);
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sum6 = vfmaq_f32(sum6, x6, y6);
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sum7 = vfmaq_f32(sum7, x7, y7);
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}
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// reduce sum0..sum7 to sum0
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sum0 = vaddq_f32(sum0, sum1);
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sum2 = vaddq_f32(sum2, sum3);
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sum4 = vaddq_f32(sum4, sum5);
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sum6 = vaddq_f32(sum6, sum7);
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sum0 = vaddq_f32(sum0, sum2);
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sum4 = vaddq_f32(sum4, sum6);
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sum0 = vaddq_f32(sum0, sum4);
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// reduce sum0 to sumf
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float32x2_t sumf32 = vadd_f32(vget_low_f32(sum0), vget_high_f32(sum0));
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sumf = vget_lane_f32(sumf32, 0) + vget_lane_f32(sumf32, 1);
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#endif
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// leftovers
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for (int i = n32; i < n; ++i) {
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@ -486,6 +548,7 @@ inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, ggml_
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// NEON 128-bit
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const int n32 = (n & ~31);
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#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
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const float16x8_t v8 = vdupq_n_f16(v);
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float16x8_t x0, x1, x2, x3;
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@ -512,6 +575,51 @@ inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, ggml_
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vst1q_f16(y + i + 16, y2);
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vst1q_f16(y + i + 24, y3);
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}
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#else
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const float32x4_t v40 = vdupq_n_f32(v);
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const float32x4_t v41 = vdupq_n_f32(v);
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float32x4_t x0, x1, x2, x3, x4, x5, x6, x7;
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float32x4_t y0, y1, y2, y3, y4, y5, y6, y7;
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for (int i = 0; i < n32; i += 32) {
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y0 = vcvt_f32_f16(vld1_f16(y + i + 0 ));
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y1 = vcvt_f32_f16(vld1_f16(y + i + 4 ));
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y2 = vcvt_f32_f16(vld1_f16(y + i + 8 ));
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y3 = vcvt_f32_f16(vld1_f16(y + i + 12));
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y4 = vcvt_f32_f16(vld1_f16(y + i + 16));
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y5 = vcvt_f32_f16(vld1_f16(y + i + 20));
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y6 = vcvt_f32_f16(vld1_f16(y + i + 24));
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y7 = vcvt_f32_f16(vld1_f16(y + i + 28));
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x0 = vcvt_f32_f16(vld1_f16(x + i + 0 ));
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x1 = vcvt_f32_f16(vld1_f16(x + i + 4 ));
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x2 = vcvt_f32_f16(vld1_f16(x + i + 8 ));
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x3 = vcvt_f32_f16(vld1_f16(x + i + 12));
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x4 = vcvt_f32_f16(vld1_f16(x + i + 16));
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x5 = vcvt_f32_f16(vld1_f16(x + i + 20));
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x6 = vcvt_f32_f16(vld1_f16(x + i + 24));
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x7 = vcvt_f32_f16(vld1_f16(x + i + 28));
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y0 = vfmaq_f32(y0, x0, v40);
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y1 = vfmaq_f32(y1, x1, v40);
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y2 = vfmaq_f32(y2, x2, v40);
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y3 = vfmaq_f32(y3, x3, v40);
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y4 = vfmaq_f32(y4, x4, v41);
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y5 = vfmaq_f32(y5, x5, v41);
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y6 = vfmaq_f32(y6, x6, v41);
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y7 = vfmaq_f32(y7, x7, v41);
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vst1_f16(y + i + 0 , vcvt_f16_f32(y0));
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vst1_f16(y + i + 4 , vcvt_f16_f32(y1));
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vst1_f16(y + i + 8 , vcvt_f16_f32(y2));
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vst1_f16(y + i + 12, vcvt_f16_f32(y3));
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vst1_f16(y + i + 16, vcvt_f16_f32(y4));
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vst1_f16(y + i + 20, vcvt_f16_f32(y5));
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vst1_f16(y + i + 24, vcvt_f16_f32(y6));
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vst1_f16(y + i + 28, vcvt_f16_f32(y7));
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}
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#endif
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// leftovers
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for (int i = n32; i < n; ++i) {
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@ -911,16 +1019,18 @@ struct ggml_context * ggml_init(struct ggml_init_params params) {
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if (is_first_call) {
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const uint64_t t_start = ggml_time_us(); UNUSED(t_start);
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ggml_fp16_t ii;
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for (int i = 0; i < (1 << 16); ++i) {
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uint16_t ii = (uint16_t) i;
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const float f = ggml_fp16_to_fp32(*(ggml_fp16_t *)(&ii));
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uint16_t ui = i;
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memcpy(&ii, &ui, sizeof(ii));
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const float f = ggml_fp16_to_fp32(ii);
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table_gelu_f16[i] = ggml_fp32_to_fp16(ggml_gelu_f32(f));
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table_exp_f16[i] = ggml_fp32_to_fp16(exp(f));
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}
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const uint64_t t_end = ggml_time_us(); UNUSED(t_end);
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GGML_PRINT_DEBUG("%s: GELU table initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f);
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GGML_PRINT_DEBUG("%s: GELU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f);
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is_first_call = false;
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}
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@ -4427,13 +4537,15 @@ void ggml_compute_forward_soft_max_f32(
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ggml_float sum = 0.0;
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uint16_t ss;
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for (int i = 0; i < nc; i++) {
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if (p[i] == -INFINITY) {
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p[i] = 0.0;
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} else {
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//const float val = (p[i] == -INFINITY) ? 0.0 : exp(p[i] - max);
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ggml_fp16_t s = ggml_fp32_to_fp16(p[i] - max);
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const float val = ggml_fp16_to_fp32(table_exp_f16[*(uint16_t *) &s]);
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memcpy(&ss, &s, sizeof(ss));
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const float val = ggml_fp16_to_fp32(table_exp_f16[ss]);
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sum += val;
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p[i] = val;
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}
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@ -5234,13 +5346,15 @@ void ggml_compute_forward_flash_attn_f32(
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ggml_float sum = 0.0;
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uint16_t ss;
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for (int i = 0; i < M; i++) {
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if (S[i] == -INFINITY) {
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S[i] = 0.0;
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} else {
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//const float val = (S[i] == -INFINITY) ? 0.0 : exp(S[i] - max);
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ggml_fp16_t s = ggml_fp32_to_fp16(S[i] - max);
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const float val = ggml_fp16_to_fp32(table_exp_f16[*(uint16_t *) &s]);
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memcpy(&ss, &s, sizeof(ss));
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const float val = ggml_fp16_to_fp32(table_exp_f16[ss]);
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sum += val;
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S[i] = val;
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}
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@ -5413,13 +5527,15 @@ void ggml_compute_forward_flash_attn_f16(
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ggml_float sum = 0.0;
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uint16_t ss;
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for (int i = 0; i < M; i++) {
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if (S[i] == -INFINITY) {
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S[i] = 0.0;
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} else {
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//const float val = (S[i] == -INFINITY) ? 0.0 : exp(S[i] - max);
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ggml_fp16_t s = ggml_fp32_to_fp16(S[i] - max);
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const float val = ggml_fp16_to_fp32(table_exp_f16[*(uint16_t *) &s]);
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memcpy(&ss, &s, sizeof(ss));
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const float val = ggml_fp16_to_fp32(table_exp_f16[ss]);
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sum += val;
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S[i] = val;
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}
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2
ggml.h
2
ggml.h
@ -108,7 +108,7 @@ struct ggml_tensor {
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int64_t perf_time_us;
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void * data;
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char pad[8];
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char padding[8];
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};
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// computation graph
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17
whisper.cpp
17
whisper.cpp
@ -1291,7 +1291,8 @@ bool whisper_encode(
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struct ggml_tensor * inpO = ggml_add(ctxL, cur, inpFF);
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{
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struct ggml_cgraph gf = { .n_threads = n_threads };
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struct ggml_cgraph gf = {};
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gf.n_threads = n_threads;
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ggml_build_forward_expand(&gf, inpO);
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ggml_graph_compute (ctxL, &gf);
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@ -1327,7 +1328,8 @@ bool whisper_encode(
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// run the computation
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{
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struct ggml_cgraph gf = { .n_threads = n_threads };
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struct ggml_cgraph gf = {};
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gf.n_threads = n_threads;
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ggml_build_forward_expand(&gf, cur);
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ggml_graph_compute (ctx0, &gf);
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@ -1351,7 +1353,8 @@ bool whisper_encode(
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// pre-compute cross-attention memory
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{
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struct ggml_cgraph gf = { .n_threads = n_threads };
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struct ggml_cgraph gf = {};
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gf.n_threads = n_threads;
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// TODO: hack to disconnect the encoded features from the previous graph
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cur->op = GGML_OP_NONE;
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@ -1461,7 +1464,8 @@ bool whisper_decode(
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};
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struct ggml_context * ctxL = ggml_init(paramsL);
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struct ggml_cgraph gf = { .n_threads = n_threads };
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struct ggml_cgraph gf = {};
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gf.n_threads = n_threads;
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// norm
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{
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@ -1744,7 +1748,8 @@ bool whisper_decode(
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// run the computation
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{
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struct ggml_cgraph gf = { .n_threads = n_threads };
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struct ggml_cgraph gf = {};
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gf.n_threads = n_threads;
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ggml_build_forward_expand(&gf, cur);
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ggml_graph_compute (ctx0, &gf);
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@ -2334,7 +2339,7 @@ int whisper_full(
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}
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}
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if (seek >= whisper_n_len(ctx)) {
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if (seek + 100 >= whisper_n_len(ctx)) {
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break;
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}
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