Added adagrad with tests (no clipping).

bitsandbytes/functional.py

@@ -19,6 +19,7 @@ str2optimizer32bit = {}
 str2optimizer32bit['adam'] = (lib.cadam32bit_g32, lib.cadam32bit_g16)
 str2optimizer32bit['momentum'] = (lib.cmomentum32bit_g32, lib.cmomentum32bit_g16)
 str2optimizer32bit['rmsprop'] = (lib.crmsprop32bit_g32, lib.crmsprop32bit_g16)
+str2optimizer32bit['adagrad'] = (lib.cadagrad32bit_g32, lib.cadagrad32bit_g16)
 str2optimizer32bit['lars'] = (lib.cmomentum32bit_g32, lib.cmomentum32bit_g16)
 str2optimizer32bit['lamb'] = (lib.cadam32bit_g32, lib.cadam32bit_g16)
 
@@ -33,6 +34,7 @@ str2optimizer8bit_blockwise = {}
 str2optimizer8bit_blockwise['adam'] = (lib.cadam_8bit_blockwise_fp32, lib.cadam_8bit_blockwise_fp16)
 str2optimizer8bit_blockwise['momentum'] = (lib.cmomentum_8bit_blockwise_fp32, lib.cmomentum_8bit_blockwise_fp16)
 str2optimizer8bit_blockwise['rmsprop'] = (lib.crmsprop_8bit_blockwise_fp32, lib.crmsprop_8bit_blockwise_fp16)
+str2optimizer8bit_blockwise['adagrad'] = (lib.cadagrad_8bit_blockwise_fp32, lib.cadagrad_8bit_blockwise_fp16)
 
 optimal_normal = [-0.9939730167388916, -0.8727636337280273, -0.8097418546676636, -0.7660024166107178, -0.7318882346153259, -0.6793879270553589, -0.657649040222168, -0.6385974884033203, -0.6211113333702087, -0.5901028513908386, -0.5762918591499329, -0.5630806684494019, -0.5509274005889893, -0.5394591689109802, -0.5283197164535522, -0.517780065536499, -0.5074946284294128, -0.4980469048023224, -0.48867011070251465, -0.48003149032592773, -0.47125306725502014, -0.4629971981048584, -0.4547359049320221, -0.446626216173172, -0.43902668356895447, -0.43158355355262756, -0.4244747757911682, -0.4173796474933624, -0.41038978099823, -0.4055633544921875, -0.4035947024822235, -0.39701032638549805, -0.39057496190071106, -0.38439232110977173, -0.3782760500907898, -0.3721940815448761, -0.3661896586418152, -0.3604033589363098, -0.354605108499527, -0.34892538189888, -0.34320303797721863, -0.3376772701740265, -0.3323028087615967, -0.3269782066345215, -0.32166096568107605, -0.316457599401474, -0.3112771809101105, -0.3061025142669678, -0.30106794834136963, -0.2961243987083435, -0.2912728488445282, -0.28644347190856934, -0.28165507316589355, -0.2769731283187866, -0.2722635865211487, -0.26779335737228394, -0.26314786076545715, -0.2586647868156433, -0.2541804611682892, -0.2496625930070877, -0.24527113139629364, -0.24097171425819397, -0.23659978806972504, -0.23218469321727753, -0.22799566388130188, -0.22380566596984863, -0.21965542435646057, -0.2154538631439209, -0.2113603949546814, -0.20735277235507965, -0.20334717631340027, -0.19932441413402557, -0.19530178606510162, -0.19136647880077362, -0.18736697733402252, -0.18337111175060272, -0.17951400578022003, -0.1757056713104248, -0.17182783782482147, -0.1680615097284317, -0.16431649029254913, -0.16053077578544617, -0.15685945749282837, -0.15298527479171753, -0.1493264138698578, -0.14566898345947266, -0.14188314974308014, -0.13819937407970428, -0.1344561129808426, -0.1306886374950409, -0.1271020770072937, -0.12346585839986801, -0.11981867253780365, -0.11614970862865448, -0.11256207525730133, -0.10889036953449249, -0.10525048524141312, -0.1016591489315033, -0.09824034571647644, -0.09469068050384521, -0.0911419615149498, -0.08773849159479141, -0.08416644483804703, -0.08071305602788925, -0.07720902562141418, -0.07371306419372559, -0.07019119709730148, -0.06673648208379745, -0.06329209357500076, -0.059800852090120316, -0.0564190037548542, -0.05296570807695389, -0.049522045999765396, -0.04609023034572601, -0.04262964054942131, -0.039246633648872375, -0.03577171266078949, -0.03236335143446922, -0.028855687007308006, -0.02542758360505104, -0.022069433704018593, -0.018754752352833748, -0.015386369079351425, -0.01194947212934494, -0.008439815603196621, -0.004995611496269703, -0.0016682245768606663, 0.0, 0.0015510577941313386, 0.005062474869191647, 0.008417150937020779, 0.011741090565919876, 0.015184164978563786, 0.018582714721560478, 0.02204744517803192, 0.025471193715929985, 0.02889077737927437, 0.0323684960603714, 0.03579240292310715, 0.039281025528907776, 0.0427563451230526, 0.04619763046503067, 0.04968220740556717, 0.05326594039797783, 0.05679265409708023, 0.060245808213949203, 0.06372645497322083, 0.06721872836351395, 0.0706876739859581, 0.0742349922657013, 0.07774098962545395, 0.08123527467250824, 0.08468879014253616, 0.08810535818338394, 0.09155989438295364, 0.09498448669910431, 0.0985206812620163, 0.10206405073404312, 0.10563778132200241, 0.10921968519687653, 0.11284469068050385, 0.11653254181146622, 0.12008969485759735, 0.12368203699588776, 0.1272617131471634, 0.13089501857757568, 0.134552001953125, 0.1382799744606018, 0.14194637537002563, 0.14563234150409698, 0.14930322766304016, 0.15303383767604828, 0.1567956507205963, 0.16050070524215698, 0.16431072354316711, 0.16813558340072632, 0.17204202711582184, 0.1758781224489212, 0.17973239719867706, 0.1836014688014984, 0.18753431737422943, 0.19138391315937042, 0.19535475969314575, 0.19931404292583466, 0.20333819091320038, 0.20738255977630615, 0.21152682602405548, 0.21568812429904938, 0.21978361904621124, 0.22393859922885895, 0.22814159095287323, 0.23241068422794342, 0.23675410449504852, 0.24123944342136383, 0.24569889903068542, 0.2500703036785126, 0.25904011726379395, 0.26349544525146484, 0.2682226300239563, 0.272907555103302, 0.2774306833744049, 0.28220856189727783, 0.2869136929512024, 0.2916390895843506, 0.29649388790130615, 0.30142995715141296, 0.3065022826194763, 0.3114383816719055, 0.31648796796798706, 0.3216581642627716, 0.32700115442276, 0.3322487473487854, 0.33778008818626404, 0.3431521952152252, 0.3487405776977539, 0.3543166518211365, 0.3601346015930176, 0.36605337262153625, 0.37217751145362854, 0.378179669380188, 0.3843980133533478, 0.3906566798686981, 0.39714935421943665, 0.40357843041419983, 0.4104187488555908, 0.4171563684940338, 0.42418959736824036, 0.43136918544769287, 0.4389212429523468, 0.44673123955726624, 0.45457619428634644, 0.4627031683921814, 0.47130417823791504, 0.4798591434955597, 0.48897242546081543, 0.4979848861694336, 0.5, 0.5076631307601929, 0.5177803635597229, 0.5282770991325378, 0.5392990112304688, 0.5506287813186646, 0.5632893443107605, 0.5764452815055847, 0.5903191566467285, 0.6051878333091736, 0.6209936141967773, 0.6382884979248047, 0.6573970913887024, 0.6795773506164551, 0.7037051916122437, 0.7327037453651428, 0.7677436470985413, 0.8111193776130676, 0.875165581703186, 1.0]
 

bitsandbytes/optim/__init__.py

@@ -7,4 +7,5 @@ from .sgd import SGD, SGD8bit, SGD32bit
 from .lars import LARS, LARS8bit, LARS32bit, PytorchLARS
 from .lamb import LAMB, LAMB8bit, LAMB32bit
 from .rmsprop import RMSprop, RMSprop8bit, RMSprop32bit
+from .adagrad import Adagrad, Adagrad8bit, Adagrad32bit
 from .optimizer import GlobalOptimManager

bitsandbytes/optim/adagrad.py

+# Copyright (c) Facebook, Inc. and its affiliates. 
+#   
+# This source code is licensed under the MIT license found in the 
+# LICENSE file in the root directory of this source tree.
+import torch
+from bitsandbytes.optim.optimizer import Optimizer1State
+
+torch.optim.Adagrad
+
+class Adagrad(Optimizer1State):
+    def __init__(self, params, lr=1e-2, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10,
+            optim_bits=32, args=None, min_8bit_size=4096, percentile_clipping=100, block_wise=True):
+        if not 0.0 <= lr:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if not 0.0 <= weight_decay:
+            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+        if not 0.0 <= eps:
+            raise ValueError("Invalid epsilon value: {}".format(eps))
+        if initial_accumulator_value != 0.0:
+            raise ValueError('Initial accumulator value != 0.0 not supported!')
+        if lr_decay != 0.0:
+            raise ValueError('Lr Decay != 0.0 not supported!')
+        super(Adagrad, self).__init__('adagrad', params, lr, (0.0, 0.0), eps,
+                weight_decay, optim_bits, args, min_8bit_size, percentile_clipping, block_wise)
+
+class Adagrad8bit(Optimizer1State):
+    def __init__(self, params, lr=1e-2, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10,
+            optim_bits=8, args=None, min_8bit_size=4096, percentile_clipping=100, block_wise=True):
+        if not 0.0 <= lr:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if not 0.0 <= weight_decay:
+            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+        if not 0.0 <= eps:
+            raise ValueError("Invalid epsilon value: {}".format(eps))
+        if initial_accumulator_value != 0.0:
+            raise ValueError('Initial accumulator value != 0.0 not supported!')
+        if lr_decay != 0.0:
+            raise ValueError('Lr Decay != 0.0 not supported!')
+        assert block_wise
+        super(Adagrad8bit, self).__init__('adagrad', params, lr, (0.0, 0.0), eps,
+                weight_decay, 8, args, min_8bit_size, percentile_clipping, block_wise)
+
+class Adagrad32bit(Optimizer1State):
+    def __init__(self, params, lr=1e-2, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10,
+            optim_bits=32, args=None, min_8bit_size=4096, percentile_clipping=100, block_wise=True):
+        if not 0.0 <= lr:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if not 0.0 <= weight_decay:
+            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+        if not 0.0 <= eps:
+            raise ValueError("Invalid epsilon value: {}".format(eps))
+        if initial_accumulator_value != 0.0:
+            raise ValueError('Initial accumulator value != 0.0 not supported!')
+        if lr_decay != 0.0:
+            raise ValueError('Lr Decay != 0.0 not supported!')
+        super(Adagrad32bit, self).__init__('adagrad', params, lr, (0.0, 0.0), eps,
+                weight_decay, 32, args, min_8bit_size, percentile_clipping, block_wise)

csrc/kernels.cu

@@ -790,6 +790,11 @@ __global__ void kPreconditionOptimizer32bit1State(T* g, T* p,
                   s1_vals[j] = __fdividef((float)g_vals[j],sqrtf(s1_vals[j])+eps); // update value
                   s1_vals[j] = s1_vals[j]*s1_vals[j]; // update norm
                   break;
+              case ADAGRAD: 
+                  s1_vals[j] = s1_vals[j] + ((float)g_vals[j])*((float)g_vals[j]); // state update
+                  s1_vals[j] = __fdividef((float)g_vals[j],sqrtf(s1_vals[j])+eps); // update value
+                  s1_vals[j] = s1_vals[j]*s1_vals[j]; // update norm
+                  break;
           }
       }
 
@@ -884,6 +889,10 @@ __global__ void kOptimizer32bit1State(T *g, T *p,
 										s1_vals[j] = s1_vals[j]*beta1 + ((1.0f-beta1)*((float)g_vals[j])*((float)g_vals[j]));
 										p_vals[j] = ((float)p_vals[j]) - update_scale*(lr*__fdividef((float)g_vals[j],sqrtf((float)s1_vals[j])+eps));
 										break;
+								case ADAGRAD: 
+										s1_vals[j] = s1_vals[j] + ((float)g_vals[j])*((float)g_vals[j]);
+										p_vals[j] = ((float)p_vals[j]) - lr*__fdividef((float)g_vals[j],sqrtf((float)s1_vals[j])+eps);
+										break;
 						}
 					}
       }
@@ -1653,6 +1662,9 @@ kOptimizerStatic8bit1StateBlockwise(T* p, T* __restrict__ const g, unsigned char
 									case RMSPROP: 
 										s1_vals[j] = s1_vals[j]*beta1 + ((1.0f-beta1)*(g_val*g_val));
 										break;
+									case ADAGRAD: 
+										s1_vals[j] = s1_vals[j] + (g_val*g_val);
+										break;
 							}
 						}
 
@@ -1688,6 +1700,10 @@ kOptimizerStatic8bit1StateBlockwise(T* p, T* __restrict__ const g, unsigned char
 										g_val = g_vals[j];
 										p_vals[j] = ((float)p_vals[j]) - lr*(__fdividef(g_val, sqrtf(s1_vals[j])+eps));
 										break;
+									case ADAGRAD: 
+										g_val = g_vals[j];
+										p_vals[j] = ((float)p_vals[j]) - lr*(__fdividef(g_val, sqrtf(s1_vals[j])+eps));
+										break;
 							}
 						}
 				}
@@ -1738,6 +1754,8 @@ MAKE_PreconditionOptimizer32bit1State(MOMENTUM, half)
 MAKE_PreconditionOptimizer32bit1State(MOMENTUM, float)
 MAKE_PreconditionOptimizer32bit1State(RMSPROP, half)
 MAKE_PreconditionOptimizer32bit1State(RMSPROP, float)
+MAKE_PreconditionOptimizer32bit1State(ADAGRAD, half)
+MAKE_PreconditionOptimizer32bit1State(ADAGRAD, float)
 
 #define MAKE_Optimizer32bit1State(oname, gtype) \
 template __global__ void kOptimizer32bit1State<gtype, oname>(gtype* g, gtype* p, float* state1, float *unorm, const float max_unorm, const float param_norm, \
@@ -1747,6 +1765,8 @@ MAKE_Optimizer32bit1State(MOMENTUM, half)
 MAKE_Optimizer32bit1State(MOMENTUM, float)
 MAKE_Optimizer32bit1State(RMSPROP, half)
 MAKE_Optimizer32bit1State(RMSPROP, float)
+MAKE_Optimizer32bit1State(ADAGRAD, half)
+MAKE_Optimizer32bit1State(ADAGRAD, float)
 
 #define MAKE_PreconditionOptimizer32bit2State(oname, gtype) \
 template __global__ void kPreconditionOptimizer32bit2State<gtype, oname, 4096, 8>(gtype* g, gtype* p,  \
@@ -1862,3 +1882,5 @@ MAKE_OptimizerStatic8bit1StateBlockwise(MOMENTUM, float, 2048, 8)
 MAKE_OptimizerStatic8bit1StateBlockwise(MOMENTUM, half, 2048, 8)
 MAKE_OptimizerStatic8bit1StateBlockwise(RMSPROP, float, 2048, 8)
 MAKE_OptimizerStatic8bit1StateBlockwise(RMSPROP, half, 2048, 8)
+MAKE_OptimizerStatic8bit1StateBlockwise(ADAGRAD, float, 2048, 8)
+MAKE_OptimizerStatic8bit1StateBlockwise(ADAGRAD, half, 2048, 8)

csrc/ops.cu

@@ -199,6 +199,8 @@ template<typename T, int OPTIMIZER> void optimizer32bit(T* g, T* p,
 			break;
 		case MOMENTUM:
     case RMSPROP:
+    case ADAGRAD:
+
       if(max_unorm > 0.0f)
 			{ 
 				CUDA_CHECK_RETURN(cudaMemset(unorm, 0, 1*sizeof(float)));
@@ -240,6 +242,7 @@ template<typename T, int OPTIMIZER> void optimizerStatic8bit(T* p, T* g,
 		break;
 		case MOMENTUM:
     case RMSPROP:
+    case ADAGRAD:
 			CUDA_CHECK_RETURN(cudaMemset(new_max1, 0, 1*sizeof(float)));
 			kPreconditionOptimizerStatic8bit1State<T, OPTIMIZER><<<blocks, 256>>>(p, g, state1, unorm, beta1, eps, step, quantiles1, max1, new_max1, weight_decay, gnorm_scale, n);
 			CUDA_CHECK_RETURN(cudaPeekAtLastError());
@@ -274,6 +277,7 @@ template<typename T, int OPTIMIZER> void optimizerStatic8bitBlockwise(T* p, T* g
 		break;
 		case MOMENTUM:
 		case RMSPROP:
+    case ADAGRAD:
 			blocks = n/BLOCKSIZE_1STATE;
 			blocks = n % BLOCKSIZE_1STATE == 0 ? blocks : blocks + 1;
 			kOptimizerStatic8bit1StateBlockwise<T, OPTIMIZER, BLOCKSIZE_1STATE, NUM_1STATE><<<blocks, BLOCKSIZE_1STATE/NUM_1STATE>>>(p, g, state1, beta1, beta2, eps, step, lr,
@@ -321,6 +325,8 @@ MAKE_optimizer32bit(MOMENTUM, half)
 MAKE_optimizer32bit(MOMENTUM, float)
 MAKE_optimizer32bit(RMSPROP, half)
 MAKE_optimizer32bit(RMSPROP, float)
+MAKE_optimizer32bit(ADAGRAD, half)
+MAKE_optimizer32bit(ADAGRAD, float)
 
 #define MAKE_optimizerStatic8bit(name, gtype) \
 template void optimizerStatic8bit<gtype, name>(gtype* p, gtype* g, unsigned char* state1, unsigned char* state2, \
@@ -350,6 +356,8 @@ MAKE_optimizerStatic8bitBlockwise(half, MOMENTUM);
 MAKE_optimizerStatic8bitBlockwise(float, MOMENTUM);
 MAKE_optimizerStatic8bitBlockwise(half, RMSPROP);
 MAKE_optimizerStatic8bitBlockwise(float, RMSPROP);
+MAKE_optimizerStatic8bitBlockwise(half, ADAGRAD);
+MAKE_optimizerStatic8bitBlockwise(float, ADAGRAD);
 
 template void percentileClipping(float * g, float *gnorm_vec, int step, const int n);
 template void percentileClipping(half * g, float *gnorm_vec, int step, const int n);

csrc/ops.cuh

@@ -36,6 +36,7 @@ typedef enum Optimizer_t
 	MOMENTUM = 1,
   RMSPROP = 2,
   LARS = 3,
+  ADAGRAD = 4,
 } Optimizer_t;
 
 

csrc/pythonInterface.c

@@ -29,6 +29,8 @@ MAKE_FUNC32(adam, ADAM, float, 32)
 MAKE_FUNC32(adam, ADAM, half, 16)
 MAKE_FUNC32(rmsprop, RMSPROP, float, 32)
 MAKE_FUNC32(rmsprop, RMSPROP, half, 16)
+MAKE_FUNC32(adagrad, ADAGRAD, float, 32)
+MAKE_FUNC32(adagrad, ADAGRAD, half, 16)
 
 #define MAKE_FUNC8(fname, oname, gtype, gbits) \
 void fname##_static_8bit_g##gbits(gtype* p, gtype* g, unsigned char* state1, unsigned char* state2, \
@@ -62,6 +64,8 @@ MAKE_BLOCKWISE8(momentum, MOMENTUM, half, 16)
 MAKE_BLOCKWISE8(momentum, MOMENTUM, float, 32)
 MAKE_BLOCKWISE8(rmsprop, RMSPROP, half, 16)
 MAKE_BLOCKWISE8(rmsprop, RMSPROP, float, 32)
+MAKE_BLOCKWISE8(adagrad, ADAGRAD, half, 16)
+MAKE_BLOCKWISE8(adagrad, ADAGRAD, float, 32)
 
 
 void percentileClipping_g32(float * g, float *gnorm_vec, int step, const int n){ percentileClipping<float>(g, gnorm_vec, step, n); }
@@ -102,6 +106,8 @@ extern "C"
 	MAKE_CFUNC32(momentum, half, 16)
 	MAKE_CFUNC32(rmsprop, float, 32)
 	MAKE_CFUNC32(rmsprop, half, 16)
+	MAKE_CFUNC32(adagrad, float, 32)
+	MAKE_CFUNC32(adagrad, half, 16)
 
 	#define MAKE_CFUNC8(name, gtype, gbits) \
 	void c##name##_static_8bit_g##gbits(gtype* p, gtype* g, unsigned char* state1, unsigned char* state2, \
@@ -135,6 +141,8 @@ extern "C"
 	MAKE_CBLOCKWISE8(momentum, MOMENTUM, float, 32)
 	MAKE_CBLOCKWISE8(rmsprop, RMSPROP, half, 16)
 	MAKE_CBLOCKWISE8(rmsprop, RMSPROP, float, 32)
+	MAKE_CBLOCKWISE8(adagrad, ADAGRAD, half, 16)
+	MAKE_CBLOCKWISE8(adagrad, ADAGRAD, float, 32)
 
 
 	void cpercentile_clipping_g32(float * g, float *gnorm_vec, int step, const int n){ percentileClipping_g32(g, gnorm_vec, step, n); }

tests/test_optim.py

@@ -39,6 +39,7 @@ str2optimizers['momentum'] = (lambda pxx: torch.optim.SGD(pxx, 0.01, 0.9), lambd
 str2optimizers['lars'] = (lambda pxx: bnb.optim.PytorchLARS(pxx, 0.01, 0.9), lambda pxx: bnb.optim.LARS(pxx, 0.01, 0.9))
 str2optimizers['lamb'] = (lambda pxx: apex.optimizers.FusedLAMB(pxx, weight_decay=0.0, max_grad_norm=10000.0, eps=1e-8, use_nvlamb=True), bnb.optim.LAMB)
 str2optimizers['rmsprop'] = (lambda pxx: torch.optim.RMSprop(pxx, 0.01, 0.9), lambda pxx: bnb.optim.RMSprop(pxx, 0.01, 0.9, block_wise=False))
+str2optimizers['adagrad'] = (lambda pxx: torch.optim.Adagrad(pxx, 0.01), lambda pxx: bnb.optim.Adagrad(pxx, 0.01, block_wise=False))
 str2optimizers['adam8bit'] = (torch.optim.Adam, lambda pxx: bnb.optim.Adam8bit(pxx, block_wise=False))
 str2optimizers['momentum8bit'] = (lambda pxx: torch.optim.SGD(pxx, 0.01, 0.9), lambda pxx: bnb.optim.SGD8bit(pxx, 0.01, 0.9, block_wise=False))
 str2optimizers['rmsprop8bit'] = (lambda pxx: torch.optim.RMSprop(pxx, 0.01, 0.9), lambda pxx: bnb.optim.RMSprop8bit(pxx, 0.01, 0.9, block_wise=False))
@@ -48,6 +49,7 @@ str2optimizers['lars8bit'] = (lambda pxx: bnb.optim.PytorchLARS(pxx, 0.01, 0.9),
 str2optimizers['adam8bit_blockwise'] = (torch.optim.Adam, lambda pxx: bnb.optim.Adam8bit(pxx, block_wise=True))
 str2optimizers['momentum8bit_blockwise'] = (lambda pxx: torch.optim.SGD(pxx, 0.01, 0.9), lambda pxx: bnb.optim.SGD8bit(pxx, 0.01, 0.9, block_wise=True))
 str2optimizers['rmsprop8bit_blockwise'] = (lambda pxx: torch.optim.RMSprop(pxx, 0.01, 0.9), lambda pxx: bnb.optim.RMSprop8bit(pxx, 0.01, 0.9, block_wise=True))
+str2optimizers['adagrad8bit_blockwise'] = (lambda pxx: torch.optim.Adagrad(pxx, 0.01), lambda pxx: bnb.optim.Adagrad8bit(pxx, 0.01, block_wise=True))
 
 str2statenames = {}
 str2statenames['adam'] = [('exp_avg', 'state1'), ('exp_avg_sq', 'state2')]
@@ -55,6 +57,7 @@ str2statenames['momentum'] = [('momentum_buffer', 'state1')]
 str2statenames['lars'] = [('momentum_buffer', 'state1')]
 str2statenames['lamb'] = [('exp_avg', 'state1'), ('exp_avg_sq', 'state2')]
 str2statenames['rmsprop'] = [('square_avg', 'state1')]
+str2statenames['adagrad'] = [('sum', 'state1')]
 str2statenames['adam8bit'] = [('exp_avg', 'state1', 'qmap1', 'max1'), ('exp_avg_sq', 'state2', 'qmap2', 'max2')]
 str2statenames['lamb8bit'] = [('exp_avg', 'state1', 'qmap1', 'max1'), ('exp_avg_sq', 'state2', 'qmap2', 'max2')]
 str2statenames['adam8bit_blockwise'] = [('exp_avg', 'state1', 'qmap1', 'absmax1'), ('exp_avg_sq', 'state2', 'qmap2', 'absmax2')]
@@ -63,11 +66,12 @@ str2statenames['momentum8bit_blockwise'] = [('momentum_buffer', 'state1', 'qmap1
 str2statenames['lars8bit'] = [('momentum_buffer', 'state1', 'qmap1', 'max1')]
 str2statenames['rmsprop8bit'] = [('square_avg', 'state1', 'qmap1', 'max1')]
 str2statenames['rmsprop8bit_blockwise'] = [('square_avg', 'state1', 'qmap1', 'absmax1')]
+str2statenames['adagrad8bit_blockwise'] = [('sum', 'state1', 'qmap1', 'absmax1')]
 
 dim1 = [1024]
 dim2 = [32, 1024, 4097, 1]
 gtype = [torch.float32, torch.float16]
-optimizer_names = ['adam', 'momentum', 'rmsprop', 'lars', 'lamb']
+optimizer_names = ['adam', 'momentum', 'rmsprop', 'lars', 'lamb', 'adagrad']
 values = list(product(dim1,dim2, gtype, optimizer_names))
 names = ['dim1_{0}_dim2_{1}_gtype_{2}_optim_{3}'.format(*vals) for vals in values]
 @pytest.mark.parametrize("dim1, dim2, gtype, optim_name", values, ids=names)
@@ -197,7 +201,7 @@ def test_global_config(dim1, dim2, gtype):
 dim1 = [1024]
 dim2 = [32, 1024, 4097]
 gtype = [torch.float32, torch.float16]
-optimizer_names = ['adam8bit', 'momentum8bit', 'rmsprop8bit', 'adam8bit_blockwise', 'lamb8bit', 'lars8bit', 'momentum8bit_blockwise', 'rmsprop8bit_blockwise']
+optimizer_names = ['adam8bit', 'momentum8bit', 'rmsprop8bit', 'adam8bit_blockwise', 'lamb8bit', 'lars8bit', 'momentum8bit_blockwise', 'rmsprop8bit_blockwise', 'adagrad8bit_blockwise']
 values = list(product(dim1,dim2, gtype, optimizer_names))
 names = ['dim1_{0}_dim2_{1}_gtype_{2}_optim_{3}'.format(*vals) for vals in values]
 @pytest.mark.parametrize("dim1, dim2, gtype, optim_name", values, ids=names)