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metal_kernels_large.py
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metal_kernels_large.py
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import numpy as np
import time
import math
import Metal
import transformer
test = False
ls = 256
ls3 = 256 #threadExecutionWidth correct value to use
def create_metal_buffer(a,device):
return transformer.create_buffer(a,"Metal",{"device":device})
def create_metal_buffer_empty(size,device):
return transformer.create_buffer_empty(size,"Metal",{"device":device})
class Metal_Kernels:
def __init__(self,dim,n_heads,max_context):
self.prg_cache = {}
self.dim = dim
self.n_heads = n_heads
self.max_context = max_context
self.device = Metal.MTLCreateSystemDefaultDevice()
self.mtl_queue = self.device.newCommandQueue()
def run_metal(self,fxn,g,ls,args,device=None):
command_buffer = self.mtl_queue.commandBuffer()
encoder = command_buffer.computeCommandEncoder()
pipeline_state, err = self.device.newComputePipelineStateWithFunction_error_(fxn, None)
encoder.setComputePipelineState_(pipeline_state)
i = 0
for arg in args:
encoder.setBuffer_offset_atIndex_(arg.data, 0, i)
i+=1
threadsPerGrid = Metal.MTLSizeMake(g,1,1)
threadsPerThreadGroup = Metal.MTLSizeMake(ls,1,1)
encoder.dispatchThreadgroups_threadsPerThreadgroup_(threadsPerGrid, threadsPerThreadGroup)
encoder.endEncoding()
command_buffer.commit()
command_buffer.waitUntilCompleted()
return
def run_metal_test(self,fxn,g,ls,args,device=None):
args_copy = []
n = 5
f = 0
for i in range(len(args)): args_copy.append(args[i].np())
excepted_output = []
for x in range(n):
ff = False
args_buffers = []
for j in range(len(args_copy)): args_buffers.append(create_metal_buffer(args_copy[j],self.device))
command_buffer = self.mtl_queue.commandBuffer()
encoder = command_buffer.computeCommandEncoder()
pipeline_state, err = self.device.newComputePipelineStateWithFunction_error_(fxn, None)
encoder.setComputePipelineState_(pipeline_state)
i = 0
for arg in args_buffers:
encoder.setBuffer_offset_atIndex_(arg.data, 0, i)
i+=1
threadsPerGrid = Metal.MTLSizeMake(gs,1,1)
threadsPerThreadGroup = Metal.MTLSizeMake(ls,1,1)
encoder.dispatchThreadgroups_threadsPerThreadgroup_(threadsPerGrid, threadsPerThreadGroup)
encoder.endEncoding()
t = time.perf_counter()
command_buffer.commit()
command_buffer.waitUntilCompleted()
t = time.perf_counter() - t
if len(excepted_output) == 0:
for j in range(len(args_buffers)):
excepted_output.append(np.copy(args_buffers[j].np()))
else:
for j in range(len(excepted_output)):
np.testing.assert_allclose(excepted_output[j],args_buffers[j].np(),rtol=1e-6)
#ff = True
excepted_output[j] = np.copy(args_buffers[j].np())
if ff: f+=1
#np.testing.assert_allclose(excepted_output[j],args_buffers[j].np(),rtol=1e-6)
print("flakiness =",f/n)
args_copy = None
excepted_output = None
args_buffers = None
self.run_metal(fxn,g,ls,args,device)
return
def run_metal_test2(self,fxn,g,ls,args,device=None):
ls = 1
while ls < 2048:
print("ls =",ls)
self.run_metal_test(fxn,g(ls),ls,args,device)
ls*=2
def add(self,a_g,b_g,b_s=0,a_s=0):
if hasattr(self, 'add_res_g') == False:
self.add_res_g = create_metal_buffer_empty(self.dim*4,self.device)
prg_str = f"""
#include <metal_stdlib>
#include <metal_simdgroup_matrix>
using namespace metal;
kernel void add(
device const float *a, device const float *b, device float *res, uint3 gid [[thread_position_in_grid]])
{{
int gidx0 = gid.x;
if(gidx0 < {self.dim}) {{
res[gidx0] = a[{int(a_s)*self.dim} + gidx0] + b[gidx0 + {b_s*self.dim}];
}}
}}
"""
prg = transformer.compile(prg_str,"Metal",{"device":self.device})
fxn = prg.newFunctionWithName_("add")
g = math.ceil(self.dim / ls)
self.run_metal(fxn,g,ls,[a_g, b_g,self.add_res_g],self.device)
return self.add_res_g
def tok_emb(self,tokens,weight_g,weight_2_g,no_tokens):
tokens_g = create_metal_buffer(tokens.astype(np.int32),self.device)
size = no_tokens*self.dim
tok_emb_g = create_metal_buffer_empty(no_tokens*self.dim*4,self.device)
prg_str = f"""
#include <metal_stdlib>
#include <metal_simdgroup_matrix>
using namespace metal;
kernel void mm(
device int *tokens, device const float *weight, device const float *weight2, device float *tok_emb, uint3 gid [[thread_position_in_grid]])
{{
int gidx0 = gid.x;
int i = gidx0 / {self.dim};
int j = gidx0 % {self.dim};
tok_emb[i*{self.dim} + j] = weight[tokens[i]*{self.dim} + j] + weight2[i*{self.dim} + j];
}}
"""
options = Metal.MTLCompileOptions.alloc().init()
library = transformer.compile(prg_str,"Metal",{"device":self.device})
fxn = library.newFunctionWithName_("mm")
g = math.ceil(size / ls)
self.run_metal(fxn,g,ls3,[tokens_g,weight_g,weight_2_g,tok_emb_g])
return tok_emb_g
def kernel_1(self,h_g,weight_g,bias_g,weight2_g,temperature,random_num):
ls = 256
seg = int(self.dim / ls3)
rows = self.dim
cols = 50257
if hasattr(self, 'logits_g') == False:
self.logits_g = create_metal_buffer_empty(50257*4,self.device)
if hasattr(self, 'res') == False:
self.res = np.zeros(1).astype(np.float32)
if hasattr(self, 'res_g') == False:
self.res_g = create_metal_buffer_empty(1*4,self.device)
seg2 = math.ceil(50257 / ls)
prg_str = f"""
#include <metal_stdlib>
#include <metal_simdgroup_matrix>
using namespace metal;
kernel void mm4(
device float *h, device const float *weight, device const float *bias, uint3 gid [[thread_position_in_grid]])
{{
threadgroup float temp[{ls3}];
threadgroup float mean;
int lidx0 = gid.x;
float total = 0;
for(int i = 0; i < {seg}; i++) {{
total += h[lidx0*{seg} + i];
}}
temp[lidx0] = total;
threadgroup_barrier(mem_flags::mem_threadgroup);
if(lidx0==0) {{
total = 0;
for(int i = 0; i < {ls3}; i++) {{
total += temp[i];
}}
mean = total / {self.dim};
}}
threadgroup_barrier(mem_flags::mem_threadgroup);
for(int i = 0; i < {seg}; i++) {{
h[i + lidx0*{seg}] -= mean;
}}
threadgroup_barrier(mem_flags::mem_threadgroup);
total = 0;
for(int i = 0; i < {seg}; i++) {{
total += pow(h[lidx0*{seg} + i],2);
}}
temp[lidx0] = total;
threadgroup_barrier(mem_flags::mem_threadgroup);
if(lidx0==0) {{
total = 0;
for(int i = 0; i < {ls3}; i++) {{
total += temp[i];
}}
mean = pow(total / {self.dim} + 1e-5,0.5);
}}
threadgroup_barrier(mem_flags::mem_threadgroup);
for(int i = 0; i < {seg}; i++) {{
h[i + lidx0*{seg}] = (h[i + lidx0*{seg}] * weight[i + lidx0*{seg}]) / mean + bias[i + lidx0*{seg}];
}}
}}
kernel void matvec(
device const float *h, device const float *weight2 , device float *res, uint3 gid [[thread_position_in_grid]])
{{
int gidx0 = gid.x;
res[gidx0] = 0;
for(int j = 0; j < {rows}; j++) {{
res[gidx0] += h[j] * weight2[gidx0 + j*{cols}];
}}
res[gidx0] /= {temperature};
}}
kernel void mm5(
device const float *a, device float *res, uint3 gid [[thread_position_in_grid]])
{{
res[0] = a[0]; //todo why is this needed?, used to be a MAX
}}
kernel void mm6(
device float *a, device float *res, uint3 gid [[thread_position_in_grid]])
{{
int gidx0 = gid.x;
a[gidx0] = exp(a[gidx0] - res[0]);
}}
kernel void mm8(
device float *a, device const float *res, uint3 gid [[thread_position_in_grid]])
{{
int gidx0 = gid.x;
a[gidx0] = a[gidx0] / res[0];
}}
kernel void mm9(
device const float *a, device float *res, uint3 gid [[thread_position_in_grid]])
{{
threadgroup float temp[{ls3}];
int lidx0 = gid.x;
temp[lidx0] = 0;
for(int i = 0; i < {math.ceil(50257 / ls3)}; i++) {{
if(lidx0*{math.ceil(50257 / ls3)} + i < 50257){{
temp[lidx0] += a[lidx0*{math.ceil(50257 / ls3)} + i];
}}
}}
threadgroup_barrier(mem_flags::mem_threadgroup);
float t = 0;
if(lidx0 == 0) {{
for(int i = 0; i < {ls3}; i++) {{
t+=temp[i];
}}
res[0] = t;
}}
}}
kernel void mm10(
device float *a, uint3 gid [[thread_position_in_grid]])
{{
for(int i = 1; i < 50257; i++) {{
a[i] += a[i-1];
}}
}}
"""
if prg_str not in self.prg_cache:
library = transformer.compile(prg_str,"Metal",{"device":self.device})
self.prg_cache[prg_str] = library
prg = self.prg_cache[prg_str]
prg_str = f"""
kernel void mm11(
device float *a, uint3 gid [[thread_position_in_grid]])
{{
int gidx0 = gid.x;
if(a[gidx0] < {random_num}) {{ //TODO, used to be (a[gidx0] / a[50256])/{random_num}
a[gidx0] = 1;
}} else {{
a[gidx0] = 0;
}}
}}
"""
prg2 = transformer.compile(prg_str,"Metal",{"device":self.device})
fxn = prg.newFunctionWithName_("mm4")
self.run_metal(fxn,1,ls3,[h_g, weight_g, bias_g])
fxn = prg.newFunctionWithName_("matvec")
g = math.ceil(50257 / ls)
self.run_metal(fxn,g,ls3,[h_g, weight2_g,self.logits_g])
fxn = prg.newFunctionWithName_("mm5")
self.run_metal(fxn,1,1,[self.logits_g,self.res_g])
fxn = prg.newFunctionWithName_("mm6")
g = math.ceil(50257 / ls)
self.run_metal(fxn,g,ls3,[self.logits_g,self.res_g])
fxn = prg.newFunctionWithName_("mm5")
self.run_metal(fxn,1,1,[self.logits_g,self.res_g])
fxn = prg.newFunctionWithName_("mm8")
self.run_metal(fxn,g,ls3,[self.logits_g,self.res_g])
fxn = prg.newFunctionWithName_("mm9")
self.run_metal(fxn,1,ls3,[self.logits_g,self.res_g])
fxn = prg.newFunctionWithName_("mm8")
self.run_metal(fxn,g,ls3,[self.logits_g,self.res_g])
fxn = prg.newFunctionWithName_("mm10")
self.run_metal(fxn,1,1,[self.logits_g])
fxn = prg2.newFunctionWithName_("mm11")
self.run_metal(fxn,g,ls3,[self.logits_g])
fxn = prg.newFunctionWithName_("mm9")
self.run_metal(fxn,1,ls3,[self.logits_g,self.res_g])
res = np.asarray(self.res_g.data.contents().as_buffer(self.res_g.size))
res = np.frombuffer(res, dtype=np.float32)
return res
def kernel_3(self,x_g,weight_g,bias_g,attn_weight_g,attn_bias_g,new_cache_g\
,ln_f_weight_g,ln_f_bias_g,n_tokens,max_content,lm_head_weight_g,temperature,random_num):
ls = 256
size = self.dim
b_cols2 = 50257
b_rows2 = self.dim
seg2 = math.ceil(50257 / ls)
b_cols = self.dim*3 #todo
b_rows = self.dim
seg = int(size / ls) #todo
x0_g = create_metal_buffer_empty(n_tokens*self.dim*4,self.device)
logits_g = create_metal_buffer_empty(50257*4,self.device)
c_g = create_metal_buffer_empty(n_tokens*b_cols*4,self.device)
res = np.zeros(1).astype(np.float32)
res_g = create_metal_buffer_empty(1*4,self.device)
prg_str = f"""
#include <metal_stdlib>
#include <metal_simdgroup_matrix>
using namespace metal;
kernel void mm(device const float *x_in,
device float *x, device const float *weight, device const float *bias, uint3 gid [[thread_position_in_grid]])
{{
threadgroup float temp[{ls}];
threadgroup float temp2[{n_tokens}];
int gidx0 = gid.x;
int lidx0 = gidx0 % {ls};
int r = gidx0 / {ls};
temp2[r] = 0;
for(int i = 0; i < {seg}; i++) {{
x[{self.dim}*r + lidx0*{seg} + i] = x_in[{self.dim}*r + lidx0*{seg} + i];
temp2[r] += x[{self.dim}*r + lidx0*{seg} + i];
}}
temp[lidx0] = temp2[r];
threadgroup_barrier(mem_flags::mem_threadgroup);
if(lidx0<{n_tokens}) {{
temp2[lidx0] = 0;
for(int i = 0; i < {ls}; i++) {{
temp2[lidx0] += temp[i];
}}
temp2[lidx0] = temp2[lidx0] / {size};
}}
threadgroup_barrier(mem_flags::mem_threadgroup);
for(int i = 0; i < {seg}; i++) {{
x[{self.dim}*r + i + lidx0*{seg}] -= temp2[r];
}}
threadgroup_barrier(mem_flags::mem_threadgroup);
temp2[r] = 0;
for(int i = 0; i < {seg}; i++) {{
temp2[r] += pow(x[{self.dim}*r + lidx0*{seg} + i],2);
}}
temp[lidx0] = temp2[r];
threadgroup_barrier(mem_flags::mem_threadgroup);
if(lidx0<{n_tokens}) {{
temp2[lidx0] = 0;
for(int i = 0; i < {ls}; i++) {{
temp2[lidx0] += temp[i];
}}
temp2[lidx0] = pow(temp2[lidx0] / {size} + 1e-5,0.5);
}}
threadgroup_barrier(mem_flags::mem_threadgroup);
for(int i = 0; i < {seg}; i++) {{
x[{self.dim}*r + i + lidx0*{seg}] = (x[{self.dim}*r + i + lidx0*{seg}] * weight[i + lidx0*{seg}]) / temp2[r] + bias[i + lidx0*{seg}];
}}
}}
kernel void mm2(
device const float *x, device const float *attn_weight, device const float *attn_bias,device float *res, uint3 gid [[thread_position_in_grid]])
{{
if(gid.x < {b_cols*n_tokens}) {{ //TODO
int gidx0 = gid.x;
int i = gidx0 / {n_tokens};
int y = gidx0 % {n_tokens};
float total = 0;
for(int k = 0; k < {b_rows}; k++) {{
total += x[y*{b_rows} + k] * attn_weight[i*{b_rows} + k];
}}
res[y*{b_cols} + i] = total + attn_bias[i];
}}
}}
kernel void mm3(
device const float *xqkv, device float *new_cache, uint3 gid [[thread_position_in_grid]])
{{
int gidx0 = gid.x;
int i = gidx0 / {self.n_heads*64};
int j = gidx0 % {self.n_heads*64};
new_cache[i*{self.n_heads*64} + j] = xqkv[i*{self.n_heads*64*3} + {self.dim*1} + j];
new_cache[{max_content*self.n_heads*64} + i*{self.n_heads*64} + j] = xqkv[i*{self.n_heads*64*3} + {self.dim}*2 + j];
}}
kernel void mm4(
device const float *xqkv, device float *new_cache, uint3 gid [[thread_position_in_grid]])
{{
int gidx0 = gid.x;
int i = gidx0 / {self.n_heads*64};
int j = gidx0 % {self.n_heads*64};
new_cache[i*{self.n_heads*64} + j] = xqkv[i*{self.n_heads*64*3} + j + {self.dim}];
new_cache[{max_content*self.n_heads*64} + i*{self.n_heads*64} + j] = xqkv[i*{self.n_heads*64*3} + j + {self.dim}*2];
}}
kernel void mm5(
device float *x, device const float *ln_f_weight, device const float *ln_f_bias, uint3 gid [[thread_position_in_grid]])
{{
threadgroup float temp[{ls}];
threadgroup float mean;
int lidx0 = gid.x;
float total = 0;
for(int i = 0; i < {seg}; i++) {{
total += x[lidx0*{seg} + i];
}}
temp[lidx0] = total;
threadgroup_barrier(mem_flags::mem_threadgroup);
if(lidx0==0) {{
total = 0;
for(int i = 0; i < {ls}; i++) {{
total += temp[i];
}}
mean = total / {size};
}}
threadgroup_barrier(mem_flags::mem_threadgroup);
for(int i = 0; i < {seg}; i++) {{
x[i + lidx0*{seg} + {(n_tokens - 1)*self.dim}] -= mean;
}}
threadgroup_barrier(mem_flags::mem_threadgroup);
total = 0;
for(int i = 0; i < {seg}; i++) {{
total += pow(x[lidx0*{seg} + i + {(n_tokens - 1)*self.dim}],2);
}}
temp[lidx0] = total;
threadgroup_barrier(mem_flags::mem_threadgroup);
if(lidx0==0) {{
total = 0;
for(int i = 0; i < {ls}; i++) {{
total += temp[i];
}}
mean = pow(total / {size} + 1e-5,0.5);
}}
threadgroup_barrier(mem_flags::mem_threadgroup);
for(int i = 0; i < {seg}; i++) {{
x[i + lidx0*{seg}] = (x[i + lidx0*{seg} + {(n_tokens - 1)*self.dim}] * ln_f_weight[i + lidx0*{seg}]) / mean + ln_f_bias[i + lidx0*{seg}];
}}
}}
kernel void matmul(
device const float *a, device const float *b, device float *res, uint3 gid [[thread_position_in_grid]])
{{
if(gid.x < {b_cols2}) {{
int x = gid.x;
float total = 0;
for(int k = 0; k < {b_rows2}; k++) {{
total += a[k] * b[x*{b_rows2} + k];
}}
res[x] = total / {temperature};
}}
}}
kernel void mm6(
device const float *a, device float *res, uint3 gid [[thread_position_in_grid]])
{{
res[0] = a[0]; //todo why is this needed?, used to be a MAX
}}
kernel void mm7(
device float *a, device const float *res, uint3 gid [[thread_position_in_grid]])
{{
if(gid.x < 50257) {{ //TODO
int gidx0 = gid.x;
a[gidx0] = exp(a[gidx0] - res[0]);
}}
}}
kernel void mm9(
device float *a, device const float *res, uint3 gid [[thread_position_in_grid]])
{{
if(gid.x < 50257) {{
int gidx0 = gid.x;
a[gidx0] = a[gidx0] / res[0];
}}
}}
kernel void mm10(
device const float *a, device float *res, uint3 gid [[thread_position_in_grid]])
{{
threadgroup float temp[{ls}];
int lidx0 = gid.x;
float t = 0;
for(int i = 0; i < {seg2}; i++) {{
if(lidx0*{seg2} + i < 50257) {{
t += a[lidx0*{seg2} + i];
}}
}}
temp[lidx0] = t;
threadgroup_barrier(mem_flags::mem_threadgroup);
if(lidx0 == 0) {{
t = 0;
for(int i = 0; i < {ls}; i++) {{
t += temp[i];
}}
res[0] = t;
}}
}}
kernel void mm11(
device float *a, uint3 gid [[thread_position_in_grid]])
{{
for(int i = 1; i < 50257; i++) {{
a[i] += a[i-1];
}}
}}
kernel void mm12(
device float *a, uint3 gid [[thread_position_in_grid]])
{{
int gidx0 = gid.x;
if(a[gidx0] < {random_num}) {{
a[gidx0] = 1;
}} else {{
a[gidx0] = 0;
}}
}}
"""
if prg_str not in self.prg_cache:
library = transformer.compile(prg_str,"Metal",{"device":self.device})
self.prg_cache[prg_str] = library
prg = self.prg_cache[prg_str]
fxn = prg.newFunctionWithName_("mm")
self.run_metal(fxn,n_tokens,ls,[x_g, x0_g, weight_g, bias_g]) #TODO
fxn = prg.newFunctionWithName_("mm2")
g = math.ceil(b_cols*n_tokens / ls)
self.run_metal(fxn,g,ls3,[x0_g, attn_weight_g,attn_bias_g,c_g])
fxn = prg.newFunctionWithName_("mm4")
g = math.ceil((n_tokens*self.n_heads*64) / ls)
self.run_metal(fxn,g,ls3,[c_g, new_cache_g])
fxn = prg.newFunctionWithName_("mm5")
self.run_metal(fxn,1,ls,[x_g, ln_f_weight_g, ln_f_bias_g]) #TODO, reduce
fxn = prg.newFunctionWithName_("matmul")
g = math.ceil(b_cols2 / ls)
self.run_metal(fxn,g,ls3,[x_g, lm_head_weight_g,logits_g])
fxn = prg.newFunctionWithName_("mm6")
self.run_metal(fxn,1,1,[logits_g,res_g])
fxn = prg.newFunctionWithName_("mm7")
g = math.ceil(50257 / ls)
self.run_metal(fxn,g,ls3,[logits_g,res_g])
fxn = prg.newFunctionWithName_("mm6")
self.run_metal(fxn,1,1,[logits_g,res_g])
fxn = prg.newFunctionWithName_("mm9")
g = math.ceil(50257 / ls)
self.run_metal(fxn,g,ls3,[logits_g,res_g])
fxn = library.newFunctionWithName_("mm10")
self.run_metal(fxn,1,ls,[logits_g,res_g]) #TODO, need to calculate seg based on ls
fxn = library.newFunctionWithName_("mm9")
g = math.ceil(50257 / ls)
self.run_metal(fxn,g,ls3,[logits_g,res_g]) #TODO
fxn = library.newFunctionWithName_("mm11")
self.run_metal(fxn,1,1,[logits_g])
fxn = library.newFunctionWithName_("mm12")
g = math.ceil(50257 / ls)
self.run_metal(fxn,g,ls3,[logits_g]) #TODO
fxn = library.newFunctionWithName_("mm10")
self.run_metal(fxn,1,ls,[logits_g,res_g]) #TODO, need to calculate seg based on ls
res = np.asarray(res_g.data.contents().as_buffer(res_g.size))
res = np.frombuffer(res, dtype=np.float32)
return res
def kernel_0(self,a_g,c_g,d_g,e_g,xqkv_g,keys_values_g,weight_g,bias_g,\
weight2_g,bias2_g,weight3_g,bias3_g,weight4_g,bias4_g,start_pos,g,j=0):
ls = 256
ls3 = 256 #TODO why is 256 fastet than 32?
seg3 = math.ceil(self.dim / ls3) #todo
seg = math.ceil(self.dim / ls)
if hasattr(self, 'temp_g') == False:
self.temp_g = create_metal_buffer_empty(self.n_heads*self.max_context*4,self.device)
if hasattr(self, 'xq_temp_g') == False:
self.xq_temp_g = create_metal_buffer_empty(self.dim*4,self.device)
#threadgroup_barrier(mem_flags::mem_threadgroup);
prg_str = f"""
#include <metal_stdlib>
#include <metal_simdgroup_matrix>
using namespace metal;
kernel void mm(
device float *a,
device float *mean, uint3 gid [[thread_position_in_grid]])
{{
threadgroup float temp[{ls}];
int lidx0 = gid.x;
float t = 0;
for(int i = 0; i < {seg}; i++) {{
t += a[lidx0*{seg} + i];
}}
temp[lidx0] = t;
threadgroup_barrier(mem_flags::mem_threadgroup);
if(lidx0==0) {{
t = 0;
for(int i = 0; i < {ls}; i++) {{
t += temp[i];
}}
mean[0] = t / {self.dim};
}}
threadgroup_barrier(mem_flags::mem_threadgroup);
t = 0;
for(int i = 0; i < {seg}; i++) {{
a[i + lidx0*{seg}] -= mean[0];
t += pow(a[lidx0*{seg} + i],2);
}}
temp[lidx0] = t;
threadgroup_barrier(mem_flags::mem_threadgroup);
if(lidx0==0) {{
t = 0;
for(int i = 0; i < {ls}; i++) {{
t += temp[i];
}}
mean[0] = pow(t / {self.dim} + 1e-5,0.5);
}}
}}
kernel void mm4(
device float *a,
device const float *weight2, device const float *bias2,
device const float *weight3, device const float *bias3,
device float *mean,
device float *h_temp, device float *h, device float *bias3_temp,
uint3 gid [[thread_position_in_grid]])
{{
int lidx0 = gid.x % {ls3};
int i = gid.x / {ls3};
bias3_temp[i + lidx0*{math.ceil(self.dim*4 / ls3)}] = bias3[i + lidx0*{math.ceil(self.dim*4 / ls3)}];
for(int j = 0; j < {self.dim}; j++) {{
bias3_temp[i + lidx0*{math.ceil(self.dim*4 / ls3)}] += ((h[j] * weight2[j]) / mean[0] + bias2[j]) * weight3[(i + lidx0*{math.ceil(self.dim*4 / ls3)})*{self.dim} + j];
}}
float tth = bias3_temp[i + lidx0*{math.ceil(self.dim*4 / ls3)}] * 0.7978845608\
* (1 + 0.044715 * pow(bias3_temp[i + lidx0*{math.ceil(self.dim*4 / ls3)}],2));
float th = tanh(tth);
if(isnan(th) && tth < 0) {{ th = -1;}}
if(isnan(th) && tth >= 0) {{ th = 1;}}
bias3_temp[i + lidx0*{math.ceil(self.dim*4 / ls3)}] = 0.5 * bias3_temp[i + lidx0*{math.ceil(self.dim*4 / ls3)}]\
* (1 + th);
}}
kernel void mm5(
device float *a,
device const float *weight4,device const float *bias4,
device float *h_temp, device float *bias3_temp,
uint3 gid [[thread_position_in_grid]])
{{
threadgroup float bias4_temp[{self.dim*3}];
int lidx0 = gid.x % {ls3};
int i = gid.x / {ls3};
bias4_temp[lidx0 + i*{ls3}] = bias4[lidx0 + i*{ls3}];
for(int j = 0; j < {self.dim*4}; j++) {{
bias4_temp[lidx0 + i*{ls3}] += bias3_temp[j] * weight4[lidx0 + i*{ls3} + j*{self.dim}];
}}
a[lidx0 + i*{ls3}] = bias4_temp[lidx0 + i*{ls3}] + h_temp[lidx0 + i*{ls3}];
}}
"""
if prg_str not in self.prg_cache:
library = transformer.compile(prg_str,"Metal",{"device":self.device})
self.prg_cache[prg_str] = library
prg = self.prg_cache[prg_str]
prg_str = f"""
#include <metal_stdlib>
#include <metal_simdgroup_matrix>
using namespace metal;
kernel void mm1(
device const float *a, device const float *c, device const float *d, device const float *e,
device const float *xqkv, device float *keys_values,
device float *xq_temp, device float *mean, uint3 gid [[thread_position_in_grid]])
{{
int lidx0 = gid.x % {ls};
int i = gid.x / {ls};
float t = 0;
xq_temp[lidx0*{math.ceil(self.dim*3 / ls)} + i] = xqkv[lidx0*{int(self.dim*3 / ls)} + i];
for(int k = 0; k < {self.dim}; k++) {{
t += ((a[k] * c[k]) / mean[0] + d[k]) * e[(lidx0*{int(self.dim*3 / ls)} + i)*{self.dim} + k];
}}
if((lidx0*{int(self.dim*3 / ls)} + i) < {g}) {{
xq_temp[lidx0*{int(self.dim*3 / ls)} + i] += t;
}}
if((lidx0*{int(self.dim*3 / ls)} + i) >= {g} && (lidx0*{int(self.dim*3 / ls)} + i) < {2*g}) {{
keys_values[{start_pos}*{self.dim} + lidx0*{int(self.dim*3 / ls)} + i - {g}] = xqkv[{self.dim} + lidx0*{int(self.dim*3 / ls)} + i - {g}] + t;
}}
if((lidx0*{int(self.dim*3 / ls)} + i) >= {2*g}) {{
keys_values[{self.dim*self.max_context} + {start_pos}*{self.dim} + lidx0*{int(self.dim*3 / ls)} + i - {2*g}] = xqkv[{self.dim*2} + lidx0*{int(self.dim*3 / ls)} + i - {2*g}] + t;
}}
}}
kernel void mm2(
device const float *keys_values, device float *temp3, device const float *xq_temp, uint3 gid [[thread_position_in_grid]])
{{
int lidx0 = gid.x;
int x = (lidx0) % {start_pos+1};
int k = (lidx0) / {start_pos+1};
float acc0 = 0;
for(int i = 0; i < 64; i++) {{
acc0 += xq_temp[i + 64*k] * keys_values[x*{self.n_heads*64} + i + 64*k];
}}
if(x + k*{start_pos+1} < {self.n_heads*self.max_context}) {{
temp3[x + k*{start_pos+1}] = acc0 / 8; //hardcoded math.sqrt(64)
}}
}}
kernel void mm3(
device float *a,
device float *keys_values,
device const float *weight,device const float *bias,
device const float *weight2, device const float *bias2,
device const float *weight3, device const float *bias3,
device const float *weight4,
device float *bias4,
device float *temp3, device float *xq_temp, device float *mean,
device float *h_temp, device float *h,
uint3 gid [[thread_position_in_grid]])
{{
int lidx0 = gid.x;
float t = 0;
if(lidx0 < {self.n_heads}){{
float m = -INFINITY;
for(int i = 0; i < {start_pos+1}; i++) {{
m = max(m,temp3[i + lidx0*{start_pos+1}]);
}}
for(int i = 0; i < {start_pos+1}; i++) {{
temp3[i + lidx0*{start_pos+1}] = exp(temp3[i + lidx0*{start_pos+1}] - m);
t += temp3[i + lidx0*{start_pos+1}];
}}
}}
if(lidx0 < {self.n_heads}){{
for(int i = 0; i < {start_pos+1}; i++) {{
temp3[i + lidx0*{start_pos+1}] /= t;
}}
}}
}}
kernel void mm3a(
device float *keys_values,
device float *temp3, device float *xq_temp,
uint3 gid [[thread_position_in_grid]])
{{
int lidx0 = gid.x % {ls3};
int i = gid.x / {ls3};
float acc0 = 0;
for(int j = 0; j < {start_pos+1}; j++) {{
acc0 += temp3[j + {start_pos+1}*((i + lidx0*{seg3}) / 64)] * keys_values[{self.dim*self.max_context} + j*{self.n_heads*64} + i + lidx0*{seg3}];
}}
xq_temp[i + lidx0*{seg3}] = acc0;
}}
kernel void mm3b(
device float *a,
device const float *weight,device const float *bias,
device float *xq_temp,
device float *h_temp, device float *h,
uint3 gid [[thread_position_in_grid]])
{{
int lidx0 = gid.x % {ls3};
int i = gid.x / {ls3};
float acc = 0;
for(int x = 0; x < {self.dim}; x++) {{
acc += xq_temp[x] * weight[x*{self.dim} + lidx0*{seg3} + i];
}}
h[lidx0*{seg3} + i] = a[lidx0*{seg3} + i] + acc + bias[lidx0*{seg3} + i];
h_temp[lidx0*{seg3} + i] = h[lidx0*{seg3} + i];
}}
kernel void mm3c(
device float *mean,
device float *h_temp, device float *h,
uint3 gid [[thread_position_in_grid]])
{{
int lidx0 = gid.x;
threadgroup_barrier(mem_flags::mem_threadgroup);
threadgroup float temp[{ls3}];
float total = 0;
for(int i = 0; i < {seg3}; i++) {{
total += h[lidx0*{seg3} + i];
}}
temp[lidx0] = total;
threadgroup_barrier(mem_flags::mem_threadgroup);
if(lidx0==0) {{
total = 0;
for(int i = 0; i < {ls3}; i++) {{
total += temp[i];
}}
mean[0] = total / {self.dim};
}}
threadgroup_barrier(mem_flags::mem_threadgroup);
total = 0;
for(int i = 0; i < {seg3}; i++) {{
h[i + lidx0*{seg3}] = h[i + lidx0*{seg3}] - mean[0];
total += pow(h[lidx0*{seg3} + i],2);
}}
temp[lidx0] = total;
threadgroup_barrier(mem_flags::mem_threadgroup);
if(lidx0==0) {{
total = 0;
for(int i = 0; i < {ls3}; i++) {{
total += temp[i];
}}
mean[0] = pow(total / {self.dim} + 1e-5,0.5);
}}
}}
"""
if prg_str not in self.prg_cache:
library = transformer.compile(prg_str,"Metal",{"device":self.device})
self.prg_cache[prg_str] = library
prg2 = self.prg_cache[prg_str]
if hasattr(self, 'total') == False:
self.total = create_metal_buffer_empty(1*4,self.device)
if hasattr(self, 'bias3_temp') == False:
self.bias3_temp = create_metal_buffer_empty(self.dim*4*4,self.device)
if hasattr(self, 'mean') == False:
self.mean = create_metal_buffer_empty(1*4,self.device)
if hasattr(self, 'h_temp') == False:
self.h_temp = create_metal_buffer_empty(self.dim*4,self.device)
if hasattr(self, 'h') == False:
self.h = create_metal_buffer_empty(self.dim*4,self.device)
fxn = prg.newFunctionWithName_("mm")
self.run_metal(fxn,1,256,[a_g\
,self.mean]) #TODO, reduce
fxn = prg2.newFunctionWithName_("mm1")
self.run_metal(fxn,math.ceil(self.dim*3 / ls),ls,[a_g,c_g,d_g,e_g,xqkv_g\
,keys_values_g,self.xq_temp_g,self.mean]) #TODO, reduce
ls = 256
fxn = prg2.newFunctionWithName_("mm2")
g = math.ceil((self.n_heads*(start_pos+1)*(start_pos+1)) / ls)
self.run_metal(fxn,g,ls,[keys_values_g ,self.temp_g, self.xq_temp_g])
fxn = prg2.newFunctionWithName_("mm3")
self.run_metal(fxn,1,ls3,[a_g\
,keys_values_g,weight_g,bias_g,\
weight2_g,bias2_g,weight3_g,bias3_g,weight4_g,bias4_g,self.temp_g, self.xq_temp_g,self.mean,
self.h_temp,self.h])
fxn = prg2.newFunctionWithName_("mm3a")
self.run_metal(fxn,seg3,ls3,[\
keys_values_g,self.temp_g, self.xq_temp_g])
fxn = prg2.newFunctionWithName_("mm3b")
self.run_metal(fxn,seg3,ls3,[a_g\
,weight_g,bias_g,\
self.xq_temp_g,
self.h_temp,self.h])
fxn = prg2.newFunctionWithName_("mm3c")
self.run_metal(fxn,1,ls3,[self.mean,
self.h_temp,self.h])
fxn = prg.newFunctionWithName_("mm4")
self.run_metal(fxn,int(self.dim*4 / ls3),ls3,[a_g,\
weight2_g,bias2_g,weight3_g,bias3_g,self.mean,
self.h_temp,self.h,self.bias3_temp])
fxn = prg.newFunctionWithName_("mm5")
self.run_metal(fxn,int(self.dim / ls3),ls3,[a_g,\
weight4_g,bias4_g,
self.h_temp,self.bias3_temp])
return a_g
def kernel_2(self,x_g,ln_1_weight_g,ln_1_bias_g,attn_weight_g,attn_bias_g,cache_kv_g,attn_c_proj_weight_g,attn_c_proj_bias_g,ln_2_weight_g,ln_2_bias_g,c_fc_weight_g,c_fc_bias_g\
,c_proj_weight_g,c_proj_bias_g,num_tokens,max_content,j=0):
if hasattr(self, 'h_g') == False:
self.h_g = create_metal_buffer_empty(max_content*self.dim*4,self.device)
if hasattr(self, 'h2_g') == False:
self.h2_g = create_metal_buffer_empty(max_content*self.dim*4,self.device)
if hasattr(self, 'xq_g') == False:
self.xq_g = create_metal_buffer_empty(self.n_heads*64*max_content*4,self.device)
if hasattr(self, 'xv_g') == False:
self.xv_g = create_metal_buffer_empty(self.n_heads*64*max_content*4,self.device)
if hasattr(self, 'c_g') == False:
self.c_g = create_metal_buffer_empty(self.n_heads*64*max_content*4,self.device)
if hasattr(self, 'xqt_g') == False:
self.xqt_g = create_metal_buffer_empty(self.n_heads*64*max_content*4,self.device)
if hasattr(self, 'res_g') == False:
self.res_g = create_metal_buffer_empty(max_content*self.n_heads*4,self.device)
if hasattr(self, 'xqkv_g') == False:
self.xqkv_g = create_metal_buffer_empty(max_content*self.dim*3*4,self.device)
if hasattr(self, 'd_g') == False:
self.d_g = create_metal_buffer_empty(max_content*self.dim*4*4,self.device)
a_rows = num_tokens
a_cols = 64
b_rows = self.dim
ls = 256
size = self.dim
seg = int(size / ls) #todo
b_cols = self.dim*3 # for first part
b_cols_2 = self.dim*4
prg_str = f"""
#include <metal_stdlib>
#include <metal_simdgroup_matrix>
using namespace metal;
kernel void mm(
device float *x, device const float *weight, device const float *bias,
device float *copy, uint3 gid [[thread_position_in_grid]])
{{
threadgroup float temp[{ls}];
threadgroup float temp2[{num_tokens}];
int gidx0 = gid.x;
int lidx0 = gidx0 % {ls};
int r = gidx0 / {ls};
temp2[r] = 0;
for(int i = 0; i < {seg}; i++) {{
copy[{self.dim}*r + lidx0*{seg} + i] = x[{self.dim}*r + lidx0*{seg} + i];
temp2[r] += x[{self.dim}*r + lidx0*{seg} + i];
}}
temp[lidx0] = temp2[r];
threadgroup_barrier(mem_flags::mem_threadgroup);
if(lidx0<{num_tokens}) {{
temp2[lidx0] = 0;
for(int i = 0; i < {ls}; i++) {{
temp2[lidx0] += temp[i];
}}
temp2[lidx0] = temp2[lidx0] / {size};
}}
threadgroup_barrier(mem_flags::mem_threadgroup);
for(int i = 0; i < {seg}; i++) {{
x[{self.dim}*r + i + lidx0*{seg}] -= temp2[r];
}}
threadgroup_barrier(mem_flags::mem_threadgroup);
temp2[r] = 0;
for(int i = 0; i < {seg}; i++) {{
temp2[r] += pow(x[{self.dim}*r + lidx0*{seg} + i],2.0);
}}
temp[lidx0] = temp2[r];
threadgroup_barrier(mem_flags::mem_threadgroup);
if(lidx0<{num_tokens}) {{
temp2[lidx0] = 0;
for(int i = 0; i < {ls}; i++) {{
temp2[lidx0] += temp[i];
}}
temp2[lidx0] = pow(temp2[lidx0] / {size} + 1e-5,0.5);
}}
threadgroup_barrier(mem_flags::mem_threadgroup);
for(int i = 0; i < {seg}; i++) {{
x[{self.dim}*r + i + lidx0*{seg}] = (x[{self.dim}*r + i + lidx0*{seg}] * weight[i + lidx0*{seg}]) / temp2[r] + bias[i + lidx0*{seg}];
if(isnan(x[{self.dim}*r + i + lidx0*{seg}])) {{ x[{self.dim}*r + i + lidx0*{seg}] = 0; }} //TODO shouldn't need this
}}
}}
kernel void mm2(
device const float *x, device const float *attn_weight, device const float *attn_bias,device float *res, uint3 gid [[thread_position_in_grid]])
{{
int gidx0 = gid.x;
if(gidx0 < {b_cols*num_tokens}) {{
int i = gidx0 / {num_tokens};
int y = gidx0 % {num_tokens};
float total = 0;
for(int k = 0; k < {b_rows}; k++) {{
total += x[y*{b_rows} + k] * attn_weight[i*{b_rows} + k];
}}
res[y*{b_cols} + i] = total + attn_bias[i];
}}
}}
kernel void mm3(
device const float *xqkv, device float *new_cache, uint3 gid [[thread_position_in_grid]])
{{
int gidx0 = gid.x;
int i = gidx0 / {self.n_heads*64};
int j = gidx0 % {self.n_heads*64};
new_cache[i*{self.n_heads*64} + j] = xqkv[i*{self.n_heads*64*3} + {self.dim}*1 + j];
new_cache[{max_content}*{self.n_heads*64} + i*{self.n_heads*64} + j] = xqkv[i*{self.n_heads*64*3} + {self.dim}*2 + j];
}}
kernel void tr(
device const float *xqkv, device float *xq, device float *xv,uint3 gid [[thread_position_in_grid]])
{{
int gidx0 = gid.x;
int i = (gidx0 / {64}) / {num_tokens};
int j = (gidx0 / {64}) % {num_tokens};
int k = gidx0 % 64;
xq[i*{num_tokens}*64 + j*64 + k] = xqkv[i*64 + j*{64*self.n_heads*3} + k];
xv[i*{num_tokens}*64 + j*64 + k] = xqkv[i*64 + j*{64*self.n_heads*3} + k + {64*self.n_heads*2}];
}}