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https://github.com/ggerganov/whisper.cpp.git
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llama : add qwen2moe (llama/6074)
* support qwen2moe * fix-review * metal : support unary ops for nelements % 4 != 0 * metal : require contiguousness for float4 unary kernels * metal : require contiguousness for float4 unary kernels (cont) * fix-review * names : for brevity "SHARED_EXP" -> "SHEXP" * llama : reuse build_moe_ffn() * llama : add model type name --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Shijie
Georgi Gerganov
parent
98c0b77e0c
commit
fdb2c87350
57
ggml-metal.m
57
ggml-metal.m
@ -42,8 +42,11 @@ enum ggml_metal_kernel_type {
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GGML_METAL_KERNEL_TYPE_RELU,
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GGML_METAL_KERNEL_TYPE_SIGMOID,
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GGML_METAL_KERNEL_TYPE_GELU,
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GGML_METAL_KERNEL_TYPE_GELU_4,
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GGML_METAL_KERNEL_TYPE_GELU_QUICK,
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GGML_METAL_KERNEL_TYPE_GELU_QUICK_4,
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GGML_METAL_KERNEL_TYPE_SILU,
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GGML_METAL_KERNEL_TYPE_SILU_4,
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GGML_METAL_KERNEL_TYPE_SOFT_MAX,
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GGML_METAL_KERNEL_TYPE_SOFT_MAX_4,
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GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF,
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@ -475,8 +478,11 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
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GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU, relu, true);
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GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIGMOID, sigmoid, true);
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GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU, gelu, true);
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GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_4, gelu_4, true);
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GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK, gelu_quick, true);
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GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, gelu_quick_4, true);
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GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU, silu, true);
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GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4, silu_4, true);
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GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX, soft_max, ctx->support_simdgroup_reduction);
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GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_4, soft_max_4, ctx->support_simdgroup_reduction);
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GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, diag_mask_inf, true);
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@ -1181,6 +1187,9 @@ static enum ggml_status ggml_metal_graph_compute(
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} break;
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case GGML_OP_UNARY:
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switch (ggml_get_unary_op(gf->nodes[i])) {
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// we are not taking into account the strides, so for now require contiguous tensors
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GGML_ASSERT(ggml_is_contiguous(src0));
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case GGML_UNARY_OP_TANH:
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{
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id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TANH].pipeline;
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@ -1219,42 +1228,60 @@ static enum ggml_status ggml_metal_graph_compute(
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} break;
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case GGML_UNARY_OP_GELU:
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{
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id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline;
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int64_t n = ggml_nelements(dst);
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id<MTLComputePipelineState> pipeline = nil;
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if (n % 4 == 0) {
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pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_4].pipeline;
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n /= 4;
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} else {
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pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU].pipeline;
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}
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[encoder setComputePipelineState:pipeline];
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[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
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[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
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const int64_t n = ggml_nelements(dst);
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GGML_ASSERT(n % 4 == 0);
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[encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
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[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
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} break;
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case GGML_UNARY_OP_GELU_QUICK:
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{
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id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline;
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int64_t n = ggml_nelements(dst);
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id<MTLComputePipelineState> pipeline = nil;
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if (n % 4 == 0) {
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pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK_4].pipeline;
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n /= 4;
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} else {
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pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GELU_QUICK].pipeline;
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}
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[encoder setComputePipelineState:pipeline];
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[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
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[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
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const int64_t n = ggml_nelements(dst);
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GGML_ASSERT(n % 4 == 0);
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[encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
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[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
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} break;
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case GGML_UNARY_OP_SILU:
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{
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id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline;
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int64_t n = ggml_nelements(dst);
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id<MTLComputePipelineState> pipeline = nil;
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if (n % 4 == 0) {
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pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU_4].pipeline;
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n /= 4;
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} else {
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pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SILU].pipeline;
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}
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[encoder setComputePipelineState:pipeline];
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[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
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[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
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const int64_t n = ggml_nelements(dst);
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GGML_ASSERT(n % 4 == 0);
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[encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
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[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
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} break;
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default:
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{
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@ -249,6 +249,15 @@ constant float GELU_QUICK_COEF = -1.702f;
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constant float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
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kernel void kernel_gelu(
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device const float * src0,
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device float * dst,
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uint tpig[[thread_position_in_grid]]) {
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device const float & x = src0[tpig];
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dst[tpig] = 0.5f*x*(1.0f + precise::tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
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}
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kernel void kernel_gelu_4(
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device const float4 * src0,
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device float4 * dst,
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uint tpig[[thread_position_in_grid]]) {
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@ -262,6 +271,15 @@ kernel void kernel_gelu(
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}
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kernel void kernel_gelu_quick(
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device const float * src0,
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device float * dst,
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uint tpig[[thread_position_in_grid]]) {
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device const float & x = src0[tpig];
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dst[tpig] = x*(1.0f/(1.0f+exp(GELU_QUICK_COEF*x)));
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}
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kernel void kernel_gelu_quick_4(
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device const float4 * src0,
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device float4 * dst,
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uint tpig[[thread_position_in_grid]]) {
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@ -271,6 +289,14 @@ kernel void kernel_gelu_quick(
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}
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kernel void kernel_silu(
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device const float * src0,
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device float * dst,
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uint tpig[[thread_position_in_grid]]) {
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device const float & x = src0[tpig];
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dst[tpig] = x / (1.0f + exp(-x));
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}
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+kernel void kernel_silu_4(
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device const float4 * src0,
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device float4 * dst,
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uint tpig[[thread_position_in_grid]]) {
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