From 248386460e94fa57a83550b1736b5798b91a699d Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Sat, 17 Aug 2024 00:18:17 -0700 Subject: [PATCH 01/30] Add model printing before training run This will add inspectibility for parameters per block and exactly what is being used (e.g. which softmax or layernorm). --- model_info_util/model_info.py | 21 +++++++++++++++++++++ train.py | 27 +++++++++------------------ 2 files changed, 30 insertions(+), 18 deletions(-) create mode 100644 model_info_util/model_info.py diff --git a/model_info_util/model_info.py b/model_info_util/model_info.py new file mode 100644 index 0000000000..b9551a8a7a --- /dev/null +++ b/model_info_util/model_info.py @@ -0,0 +1,21 @@ +from torchinfo import summary +from rich import print + +def print_summary(model): + summary(model) + +def print_module_structure(module, prefix=''): + for name, submodule in module.named_children(): + print(f'{prefix}{name}: {submodule}') + print_module_structure(submodule, prefix + ' ') + + # Printing parameters if any + for name, param in module.named_parameters(recurse=False): + print(f'{prefix}{name}: {param.size()}') + +def print_model_blocks(model, block_range=1): + for idx, block in enumerate(model.transformer.h): + print(f"Summary for Block {idx + 1}:") + summary(block) + if (idx + 1) == block_range: + break diff --git a/train.py b/train.py index 1474a2da1d..a239daacfe 100644 --- a/train.py +++ b/train.py @@ -9,8 +9,7 @@ import sys import time -from torchinfo import summary - +from model_info_util.model_info import print_summary, print_module_structure, print_model_blocks import matplotlib.pyplot as plt import numpy as np import plotly.graph_objects as go @@ -370,19 +369,13 @@ def parse_args(): logging_group.add_argument('--save_config_json', type=str, help="Option to save model parameters as new config json file") # Visualization args - logging_group.add_argument('--statistic', choices=[ - 'input_mean', 'input_median', 'input_stdev', 'input_max', 'input_min', - 'output_mean', 'output_median', 'output_stdev', 'output_max', 'output_min', 'all_stats', 'input_all','output_all' - ], default='input_mean', help='Select one or all statistics to display, e.g., --statistic input_min, or --statistic all_stats') - logging_group.add_argument('--graph_type', choices=[ - "heatmap", "plot", "boxplot", "all" - ], default='no_graph', help='Select one of the graph types to display, e.g., --graph_type heatmap, or --graph_type plot') + logging_group.add_argument('--statistic', choices=[ 'input_mean', 'input_median', 'input_stdev', 'input_max', 'input_min', 'output_mean', 'output_median', 'output_stdev', 'output_max', 'output_min', 'all_stats', 'input_all','output_all' ], default='input_mean', help='Select one or all statistics to display, e.g., --statistic input_min, or --statistic all_stats') + logging_group.add_argument('--graph_type', choices=[ "heatmap", "plot", "boxplot", "all" ], default='no_graph', help='Select one of the graph types to display, e.g., --graph_type heatmap, or --graph_type plot') logging_group.add_argument('--box_plot_interval', default=1000, type=int, help='Instead of using mean/median/stdev statistics, create box plot of all input/output values at certain intervals of iteration') - logging_group.add_argument('--box_plot_statistic', choices=['input', 'output', 'all'], - default='', help='Select input or output statistic to display in boxplot') + logging_group.add_argument('--box_plot_statistic', choices=['input', 'output', 'all'], default='', help='Select input or output statistic to display in boxplot') # Model Parameter Distribution - logging_group.add_argument('--print_block_summary', default=False, action=argparse.BooleanOptionalAction) + logging_group.add_argument('--print_model_info', default=True, action=argparse.BooleanOptionalAction) args = parser.parse_args() @@ -523,12 +516,10 @@ def setup(self): self.model.to(self.device) # Print the model summary - summary(self.model) - - if self.args.print_block_summary: - for idx, block in enumerate(self.model.transformer.h): - print(f"Summary for Block {idx + 1}:") - summary(block) + if self.args.print_model_info: + print_summary(self.model) + print_model_blocks(self.model) + print_module_structure(self.model) # Optimizer self.scaler = torch.cuda.amp.GradScaler(enabled=(self.args.dtype == 'float16')) From 24ff05e897f841a70936bba418a767cd72f97286 Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Sat, 17 Aug 2024 00:19:44 -0700 Subject: [PATCH 02/30] Adjust plotting to be optional --- statistics_util/statistic_plots.py | 2 +- train.py | 10 ++++++++-- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/statistics_util/statistic_plots.py b/statistics_util/statistic_plots.py index d033dbc5b9..cb56abf2e3 100644 --- a/statistics_util/statistic_plots.py +++ b/statistics_util/statistic_plots.py @@ -322,4 +322,4 @@ def create_statistics(self, graph_y_labels): self.write_to_csv(self.iter_num, *betas, *gammas, prefix="beta_gamma") - """ \ No newline at end of file + """ diff --git a/train.py b/train.py index a239daacfe..498924da00 100644 --- a/train.py +++ b/train.py @@ -349,6 +349,11 @@ def parse_args(): logging_group.add_argument('--timestamp', default='', type=str) logging_group.add_argument('--save_nan_checkpoint', default=False, action=argparse.BooleanOptionalAction) + # Module And Parameter Logging and Plots of Summary Statistics + model_group.add_argument('--softmax_io_logging', default=False, action=argparse.BooleanOptionalAction, help="logs inputs and outputs of supported softmaxes") + model_group.add_argument('--consmax_beta_gamma_logging', default=False, action=argparse.BooleanOptionalAction, help="logs beta and gamma") + logging_group.add_argument('--plot_statistics', default=False, action=argparse.BooleanOptionalAction, help="logs beta and gamma") + # CSV logging logging_group.add_argument('--csv_log', default=True, action=argparse.BooleanOptionalAction) logging_group.add_argument('--csv_dir', default='csv_logs', type=str) @@ -753,7 +758,6 @@ def train(self): torch.save(checkpoint, os.path.join(self.args.out_dir, 'ckpt.pt')) if self.args.patience is not None and num_steps_with_worse_loss >= self.args.patience: print(f"Early Stopping: loss has not decreased in {self.args.patience + 1} steps") - plot_statistics(self.args, self.stats, graph_y_labels) break if losses['val'] > self.best_val_loss: num_steps_with_worse_loss += 1 @@ -817,7 +821,6 @@ def train(self): # End of training actions if self.iter_num > self.args.max_iters: - plot_statistics(self.args, self.stats, graph_y_labels) if self.args.only_save_checkpoint_at_end: checkpoint = { 'model': self.raw_model.state_dict(), @@ -833,6 +836,9 @@ def train(self): torch.save(checkpoint, os.path.join(self.args.out_dir, 'ckpt.pt')) break + if self.args.plot_statistics: + plot_statistics(self.args, self.stats, graph_y_labels) + if self.args.tensorboard_log: self.writer.flush() self.writer.close() From 7d2e241ec1d87defb0485dfaf40fe7fd58278c1a Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Sat, 17 Aug 2024 00:20:32 -0700 Subject: [PATCH 03/30] Add logging and plotting options to gptconf --- gpt_conf.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/gpt_conf.py b/gpt_conf.py index 008d14e9b6..90bb3f7a5c 100644 --- a/gpt_conf.py +++ b/gpt_conf.py @@ -18,6 +18,11 @@ class GPTConfig: moe_top_k: int = 2 moe_router_scheme: str = "softmax" + # Logging options + softmax_io_logging: bool = False + consmax_beta_gamma_logging: bool = False + plot_statistics: bool = False + # Training options ## Gradient Checkpointing - More memory efficient (can do long contexts), but is slower use_gradient_checkpointing: bool = False From 3f6110106d1cff6d25ecbed7d194b53a32731c36 Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Sat, 17 Aug 2024 00:20:55 -0700 Subject: [PATCH 04/30] Trim ending spaces in train.py and gptconf.py --- gpt_conf.py | 16 ++++++++-------- train.py | 30 +++++++++++++++--------------- 2 files changed, 23 insertions(+), 23 deletions(-) diff --git a/gpt_conf.py b/gpt_conf.py index 90bb3f7a5c..f3cddf0428 100644 --- a/gpt_conf.py +++ b/gpt_conf.py @@ -139,7 +139,7 @@ class GPTConfig: linear_std_init: float= 0.02 # Quantizations - + ## Embedding Quantizations quantize_wte: bool = False quantize_wpe: bool = False @@ -202,13 +202,13 @@ def from_json(cls, filename: str): try: with open(filename, 'r') as json_file: config_dict = json.load(json_file) - + # Get all field names of the dataclass field_names = {f.name for f in fields(cls)} - + # Filter the loaded dict to only include valid fields filtered_dict = {k: v for k, v in config_dict.items() if k in field_names} - + # Create and return a new instance return cls(**filtered_dict) except FileNotFoundError: @@ -220,14 +220,14 @@ def from_json(cls, filename: str): except TypeError as e: print(f"Error: Invalid data in JSON file. {str(e)}") return None - + def to_json(self, filename: str): """ Function to save a GPTConfig object as json to be used for later model creation - - input: + + input: - fout: string = filename of saved config file - + """ conf_dict = asdict(self) diff --git a/train.py b/train.py index 498924da00..eca93613ea 100644 --- a/train.py +++ b/train.py @@ -132,7 +132,7 @@ def parse_args(): model_group.add_argument( "--linear_std_init", type=float, default=0.02) # Quatization - + ## Quantization Method Options quant_methods = ["symmetric_quant", "affine_quant", "stochastic_quant"] @@ -162,9 +162,9 @@ def parse_args(): model_group.add_argument("--quantize_attn_act_pv_mult_output", action=argparse.BooleanOptionalAction, default=False, help="quantize output activation of pv_mult") model_group.add_argument("--quantize_attn_act_output", action=argparse.BooleanOptionalAction, default=False, help="quantize output activation of attention") - ### Default Precisions for Attention Activations + ### Default Precisions for Attention Activations model_group.add_argument("--quantize_attn_act_bits", type=int, default=8, help="number of bits for attn quantization") - + ### Overrides for granular Attention Activatinos model_group.add_argument("--quantize_attn_act_input_bits", type=int, default=None, help="number of bits for attention input quantization") model_group.add_argument("--quantize_attn_act_qk_mult_q_input_bits", type=int, default=None, help="number of bits for qk mult query input quantization") @@ -181,10 +181,10 @@ def parse_args(): model_group.add_argument("--quantize_mlp_act_activation_input", action=argparse.BooleanOptionalAction, default=False, help="quantize input activation to activation function") model_group.add_argument("--quantize_mlp_act_activation_output", action=argparse.BooleanOptionalAction, default=False, help="quantize output activation of activation function") model_group.add_argument("--quantize_mlp_act_output", action=argparse.BooleanOptionalAction, default=False, help="quantize output activation of mlp") - - ### Default Precisions for MLP Activations + + ### Default Precisions for MLP Activations model_group.add_argument("--quantize_mlp_act_bits", type=int, default=8, help="number of bits for mlp quantization") - + ### Overrides for granular MLP Activatinos model_group.add_argument("--quantize_mlp_act_input_bits", type=int, default=None, help="number of bits for mlp input quantization") model_group.add_argument("--quantize_mlp_act_activation_input_bits", type=int, default=None, help="number of bits for activation function input quantization") @@ -193,32 +193,32 @@ def parse_args(): ### Whether activations should be saved model_group.add_argument("--store_activations", action=argparse.BooleanOptionalAction, default=False, help="whether the activations should be saved as a buffer and updated through training") - - ## Linear Attn Weight Quantization Precision and Method - + + ## Linear Attn Weight Quantization Precision and Method + ### Default methods and precisions model_group.add_argument("--quantize_linear_method", type=str, default="affine_quant", choices=quant_methods, help="function used for linear quantization") model_group.add_argument("--quantize_linear_bits", type=int, default=8, help="number of bits for linear quantization") - + #### Overrides for granular Methods and Precisions model_group.add_argument("--quantize_linear_attn_q_method", type=str, default=None, choices=quant_methods, help="function used for c_attn_q quantization") model_group.add_argument("--quantize_linear_attn_q_bits", type=int, default=None, help="number of bits for c_attn_q quantization") - + model_group.add_argument("--quantize_linear_attn_k_method", type=str, default=None, choices=quant_methods, help="function used for c_attn_k quantization") model_group.add_argument("--quantize_linear_attn_k_bits", type=int, default=None, help="number of bits for c_attn_k quantization") - + model_group.add_argument("--quantize_linear_attn_v_method", type=str, default=None, choices=quant_methods, help="function used for c_attn_v quantization") model_group.add_argument("--quantize_linear_attn_v_bits", type=int, default=None, help="number of bits for c_attn_v quantization") - + model_group.add_argument("--quantize_linear_attn_proj_method", type=str, default=None, choices=quant_methods, help="function used for c_proj in attention quantization") model_group.add_argument("--quantize_linear_attn_proj_bits", type=int, default=None, help="number of bits for c_proj in attention quantization") - #### Overrides for Linear MLP Weight Quantization Precision and Method + #### Overrides for Linear MLP Weight Quantization Precision and Method model_group.add_argument("--quantize_linear_mlp_up_method", type=str, default=None, choices=quant_methods, help="function used for mlp_up quantization") model_group.add_argument("--quantize_linear_mlp_up_bits", type=int, default=None, help="number of bits for mlp_up quantization") model_group.add_argument("--quantize_linear_mlp_down_method", type=str, default=None, choices=quant_methods, help="function used for mlp_down quantization") model_group.add_argument("--quantize_linear_mlp_down_bits", type=int, default=None, help="number of bits for mlp_down quantization") - + ## Quantized Linear Warmup Iterations -- how many to first use regular linear, before switching to quantized model_group.add_argument("--quantization_warmup_iters", type=int, default=100) From 0c5be9fa45200a9f62e4034459368d453420f3fb Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Sat, 17 Aug 2024 00:22:25 -0700 Subject: [PATCH 05/30] Add ConSmaxV2 and add conditional io logging --- variations/softmax_variations.py | 141 +++++++++++++++++++++++++++---- 1 file changed, 123 insertions(+), 18 deletions(-) diff --git a/variations/softmax_variations.py b/variations/softmax_variations.py index b7932d40b4..8069a5bfec 100644 --- a/variations/softmax_variations.py +++ b/variations/softmax_variations.py @@ -1,3 +1,4 @@ + import torch import torch.nn as nn import math @@ -24,8 +25,10 @@ def __init__(self, config, dim=-1): super().__init__() # Input and Output Logging - self.inputs = [] - self.outputs = [] + self.softmax_io_logging = config.softmax_io_logging + if self.softmax_io_logging: + self.inputs = [] + self.outputs = [] # learnable 'xmax' - beta self.beta = nn.Parameter(torch.Tensor([config.consmax_initial_beta])) @@ -40,12 +43,80 @@ def __init__(self, config, dim=-1): self.consmax_base = config.consmax_base def forward(self, x): - self.inputs = x - x = x - self.beta - e_x = torch.pow(self.consmax_base, x) - outputs = e_x / self.gamma - self.outputs = outputs - return outputs + x_adj = x - self.beta + e_x = torch.pow(self.consmax_base, x_adj) + result = e_x / self.gamma + + if self.softmax_io_logging: + self.inputs = x + self.outputs = result + + return result + +class ConSmaxV2(nn.Module): + """ Constant learnable parameters for xmax and denominator """ + def __init__(self, config, dim=-1): + super().__init__() + + # Number of attention heads + self.n_head = config.n_head + + # Input and Output Logging + self.softmax_io_logging = config.softmax_io_logging + if self.softmax_io_logging: + self.inputs = [] + self.outputs = [] + + # Learnable Factors vs Directly Learnable Parameters + if config.consmax_learnable_factors: + if config.consmax_per_head: + # per head learnable factors + self.beta_factor = nn.Parameter(torch.ones(self.n_head, 1)) + self.gamma_factor = nn.Parameter(torch.ones(self.n_head, 1)) + else: + # if not using per head + self.beta_factor = nn.Parameter(torch.ones(torch.ones(1))) + self.gamma_factor = nn.Parameters(torch.ones(torch.ones(1))) + else: + if config.consmax_per_head: + # per head learnable factors + self.beta_factor = torch.ones(self.n_head,1) + self.gamma_factor = torch.ones(self.n_head,1) + else: + # if not using per head + self.beta_factor = torch.ones(torch.ones(1)) + self.gamma_factor = torch.ones(torch.ones(1)) + + if config.consmax_learnable_init: + self.beta_init = nn.Parameter(torch.Tensor([config.consmax_initial_beta])) + self.gamma_init = nn.Parameter(torch.Tensor([config.consmax_initial_gamma])) + else: + self.beta_init = config.consmax_initial_beta + self.gamma_init = config.consmax_initial_gamma + + # Set beta and gamma as fields for backwards compatibility + self.beta = self.beta_init * self.beta_factor + self.gamma = self.beta_init * self.gamma_factor + + # Set the base of the exponent + if config.consmax_use_euler_base: + self.consmax_base = math.e + else: + self.consmax_base = config.consmax_base + + def forward(self, x): + self.beta = self.beta_init * self.beta_factor + self.gamma = self.beta_init * self.gamma_factor + + x_adj = x - self.beta + e_x = torch.pow(self.consmax_base, x_adj) + result = e_x / self.gamma + + if self.softmax_io_logging: + self.inputs = x + self.outputs = result + + return result # Constantmax Quantized @@ -116,17 +187,23 @@ def __init__(self, config, dim=-1): self.subtract_max = config.strongermax_use_xmax self.sum_to_1 = config.strongermax_sum_to_1 self.divisor = config.strongermax_divisor - self.inputs = [] - self.outputs = [] + # Input and Output Logging + self.softmax_io_logging = config.softmax_io_logging + print(self.softmax_io_logging) + if self.softmax_io_logging: + self.inputs = [] + self.outputs = [] self.div_by_seq_len = config.div_by_seq_len def forward(self, x): - self.inputs = x + x_adj = None if self.subtract_max: max_x = x.max(dim=self.dim, keepdim=True).values - x = x - max_x + x_adj = x - max_x + else: + x_adj = x - result = torch.pow(self.strength, x) + result = torch.pow(self.strength, x_adj) if self.sum_to_1: result = result / result.sum(dim=self.dim, keepdim=True) @@ -136,7 +213,10 @@ def forward(self, x): result = result / seq_len result = result / self.divisor - self.outputs = result + + if self.softmax_io_logging: + self.inputs = x + self.outputs = result return result @@ -157,15 +237,17 @@ def __init__(self, config, dim=-1): self.power = config.polymax_power self.divisor = config.polymax_divisor - self.inputs = [] - self.outputs = [] + + self.softmax_io_logging = config.softmax_io_logging + if self.softmax_io_logging: + self.inputs = [] + self.outputs = [] def forward(self, x): # Overview: # Flat section: -inf < x < x_intercept # Linear section: x_intercept <= x <= 0 # Polynomial section: 0 < x < inf - self.inputs = x # Flat section flat_piece = torch.where(x < self.x_intercept, torch.tensor(0.0, device=x.device), torch.tensor(0.0, device=x.device)) @@ -183,7 +265,9 @@ def forward(self, x): seq_len = x.shape[self.dim] result = result / seq_len - self.outputs = result + if self.softmax_io_logging: + self.inputs = x + self.outputs = result return result @@ -368,6 +452,25 @@ def forward(self, inputs): return numerator / denominator +class ReLUMax(nn.Module): + def __init__(self, config, dim=-1): + super().__init__() + self.dim = dim + self.relumax = nn.ReLU() + self.relumax_divisor = config.relumax_divisor + self.div_by_seq_len = config.div_by_seq_len + + def forward(self, x): + + result = self.relumax(x) / self.relumax_divisor + + # divide by sequence length + if self.div_by_seq_len: + seq_len = x.shape[self.dim] + result = result / seq_len + + return result + class Softplus(nn.Module): """ Softmax variant based on arxiv 1805.10829 with added handles for base """ def __init__(self, config, dim=-1): @@ -414,6 +517,7 @@ def forward(self, x): # Note: we use the built in library for regular softmax softmax_dictionary = { "consmax": ConSmax, + "consmax_v2": ConSmaxV2, "consmax_quan": ConSmaxQuan, "saturatingconsmax": SaturatingConSmax, "vpolymax": VPolymax, @@ -422,6 +526,7 @@ def forward(self, x): "softermax": Softermax, "strongermax": Strongermax, "sigsoftmax": SigSoftmax, + "relumax": ReLUMax, "softplus": Softplus, "squareplus": Squareplus, } From d03d9887430e8cf09590979030696f0e4cce4564 Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Sat, 17 Aug 2024 00:22:56 -0700 Subject: [PATCH 06/30] Add formatted model printing functions --- model_info_util/model_info.py | 41 ++++++++++++++++++++++++++--------- 1 file changed, 31 insertions(+), 10 deletions(-) diff --git a/model_info_util/model_info.py b/model_info_util/model_info.py index b9551a8a7a..6e838252ee 100644 --- a/model_info_util/model_info.py +++ b/model_info_util/model_info.py @@ -1,21 +1,42 @@ from torchinfo import summary from rich import print +from rich.console import Console +from rich.text import Text +import io -def print_summary(model): - summary(model) +console = Console() -def print_module_structure(module, prefix=''): - for name, submodule in module.named_children(): - print(f'{prefix}{name}: {submodule}') - print_module_structure(submodule, prefix + ' ') +def colorize_summary(summary_str): + # Split the summary into lines + lines = summary_str.splitlines() + colored_lines = [] + + for line in lines: + if "Trainable params" in line or "Non-trainable params" in line: + colored_lines.append(f"[bold green]{line}[/bold green]") + elif "Total params" in line: + colored_lines.append(f"[bold red]{line}[/bold red]") + else: + colored_lines.append(f"[yellow]{line}[/yellow]") + + return "\n".join(colored_lines) - # Printing parameters if any - for name, param in module.named_parameters(recurse=False): - print(f'{prefix}{name}: {param.size()}') +def print_summary(model): + block_header = Text(f"High Level Parameters:", style="bold underline purple") + console.print(block_header) + summary(model) def print_model_blocks(model, block_range=1): for idx, block in enumerate(model.transformer.h): - print(f"Summary for Block {idx + 1}:") + block_header = Text(f"Summary for Block {idx + 1}:", style="bold underline green") + console.print(block_header) summary(block) if (idx + 1) == block_range: break + +def print_module_structure(module): + console.print("-" * 50, style="dim") + for name, submodule in module.named_children(): + console.print(f'{name}: {submodule}', style="yellow") + console.print("-" * 50, style="dim") + From 7a57222c3c71290cc8b7eea350eda3120c00590a Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Mon, 19 Aug 2024 11:04:09 -0700 Subject: [PATCH 07/30] Trim settings for ConSvaxV2 to only defaults --- gpt_conf.py | 7 +++++-- train.py | 4 ++++ variations/softmax_variations.py | 34 ++++++-------------------------- 3 files changed, 15 insertions(+), 30 deletions(-) diff --git a/gpt_conf.py b/gpt_conf.py index 4d9401c157..122498612e 100644 --- a/gpt_conf.py +++ b/gpt_conf.py @@ -54,10 +54,13 @@ class GPTConfig: ## ConSmax Options consmax_initial_beta: float = 2.0 # beta adjustment consmax_initial_gamma: float = 100.0 # denominator adjustment - consmax_use_euler_base: bool = True # use 'e' as base for ConSmax, default consmax_base: float = 2.0 # base to utilize for ConSmax + consmax_use_euler_base: bool = True # use 'e' as base for ConSmax, default + + ## ConSmaxV2 Special Options + consmax_per_head: bool = True # different beta gamma per head - ## SaturatingConSmax special options (otherwise same as ConSmax) + ## SaturatingConSmax Special options (otherwise same as ConSmax) consmax_saturation: float = 11.0 # for SaturatingConSmax saturation point consmax_learnable_beta: bool = True consmax_learnable_gamma: bool = True diff --git a/train.py b/train.py index 34c4dc6755..8bbd87461d 100644 --- a/train.py +++ b/train.py @@ -237,6 +237,7 @@ def parse_args(): softmax_variations = [ "saturatingconsmax", "consmax", + "consmax_v2", "consmax_quan", "polymax", "relumax", @@ -262,6 +263,9 @@ def parse_args(): model_group.add_argument('--consmax_use_euler_base', default=True, action=argparse.BooleanOptionalAction) model_group.add_argument("--consmax_base", type=float, default=2.0) + ### Special Options for ConSmaxV2 + model_group.add_argument("--consmax_per_head", default=True, action=argparse.BooleanOptionalAction) + ### Special Options for SaturatingConSmax model_group.add_argument("--consmax_saturation", type=float, default=11.0, help="point where we transition from consmax to linear saturatingconsmax, defaults to 11 to approximate e^x sat for fp16") model_group.add_argument('--consmax_learnable_beta', default=True, action=argparse.BooleanOptionalAction) diff --git a/variations/softmax_variations.py b/variations/softmax_variations.py index 8069a5bfec..b07545d86b 100644 --- a/variations/softmax_variations.py +++ b/variations/softmax_variations.py @@ -67,32 +67,10 @@ def __init__(self, config, dim=-1): self.inputs = [] self.outputs = [] - # Learnable Factors vs Directly Learnable Parameters - if config.consmax_learnable_factors: - if config.consmax_per_head: - # per head learnable factors - self.beta_factor = nn.Parameter(torch.ones(self.n_head, 1)) - self.gamma_factor = nn.Parameter(torch.ones(self.n_head, 1)) - else: - # if not using per head - self.beta_factor = nn.Parameter(torch.ones(torch.ones(1))) - self.gamma_factor = nn.Parameters(torch.ones(torch.ones(1))) - else: - if config.consmax_per_head: - # per head learnable factors - self.beta_factor = torch.ones(self.n_head,1) - self.gamma_factor = torch.ones(self.n_head,1) - else: - # if not using per head - self.beta_factor = torch.ones(torch.ones(1)) - self.gamma_factor = torch.ones(torch.ones(1)) - - if config.consmax_learnable_init: - self.beta_init = nn.Parameter(torch.Tensor([config.consmax_initial_beta])) - self.gamma_init = nn.Parameter(torch.Tensor([config.consmax_initial_gamma])) - else: - self.beta_init = config.consmax_initial_beta - self.gamma_init = config.consmax_initial_gamma + self.beta_init = config.consmax_initial_beta + self.gamma_init = config.consmax_initial_gamma + self.beta_factor = nn.Parameter(torch.ones(self.n_head, 1, 1)) + self.gamma_factor = nn.Parameter(torch.ones(self.n_head, 1, 1)) # Set beta and gamma as fields for backwards compatibility self.beta = self.beta_init * self.beta_factor @@ -105,8 +83,8 @@ def __init__(self, config, dim=-1): self.consmax_base = config.consmax_base def forward(self, x): - self.beta = self.beta_init * self.beta_factor - self.gamma = self.beta_init * self.gamma_factor + self.beta = self.beta_factor * self.beta_init + self.gamma = self.gamma_factor * self.gamma_init x_adj = x - self.beta e_x = torch.pow(self.consmax_base, x_adj) From 1d4f8a3c8772919a8c13befa9514ae8476f09bc3 Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Tue, 20 Aug 2024 15:41:18 -0700 Subject: [PATCH 08/30] Add printing of model summary to sample.py This will help with ensuring inference from the expected settings. --- sample.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/sample.py b/sample.py index 52a82800c9..bbf00872a0 100644 --- a/sample.py +++ b/sample.py @@ -15,12 +15,13 @@ from collections import OrderedDict from model import GPT, GPTConfig +from model_info_util.model_info import print_summary, print_module_structure, print_model_blocks def parse_args(): parser = argparse.ArgumentParser(description="Inference from trained models") - parser.add_argument("--device", type=str, required=True, help="Device to run inference (e.g., 'cpu', 'cuda', 'cuda:0', 'cuda:1')") - parser.add_argument("--out_dir", type=str, required=True, help="Directory to load checkpoint from") + parser.add_argument("--device", type=str, default="cuda", help="Device to run inference (e.g., 'cpu', 'cuda', 'cuda:0', 'cuda:1')") + parser.add_argument("--out_dir", type=str, default="out", help="Directory to load checkpoint from") parser.add_argument("--quantization_data_file", type=str, default=None, help="File name to export the quantized weights/activations, scale factor, and zero point") parser.add_argument("--init_from", type=str, default="resume", help="Either 'resume' (from an out_dir) or a GPT-2 variant (e.g., 'gpt2-xl')") parser.add_argument("--start", type=str, default="\n", help="Start text for generation. Can specify a file using 'FILE:prompt.txt'") @@ -41,6 +42,7 @@ def parse_args(): parser.add_argument('--sym_rot_num_angles', type=int, default=None, help="Number of angles for symmetrical rotary embedding") parser.add_argument('--rope_length', type=int, default=None, help="Number of embeddings to rotate (must be an even number <= total embedding size)") parser.add_argument('--token_boundary', type=str, default=None, help="optional separator between emitted tokens") + parser.add_argument('--print_model_info', default=True, action=argparse.BooleanOptionalAction, help="print info about model before infernece") return parser.parse_args() @@ -139,6 +141,13 @@ def main(): model.eval() model.to(args.device) + + # Print the model summary + if args.print_model_info: + print_summary(model) + print_model_blocks(model) + print_module_structure(model) + if args.compile: model = torch.compile(model) From e4843710bd52973362ffb72bcbb9dec3217963f7 Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Tue, 20 Aug 2024 16:19:52 -0700 Subject: [PATCH 09/30] Add abbreviations for config and output files --- run_experiments.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/run_experiments.py b/run_experiments.py index 9af497b2d0..1355dbbdf4 100644 --- a/run_experiments.py +++ b/run_experiments.py @@ -12,8 +12,8 @@ def parse_args(): parser = argparse.ArgumentParser(description="Run experiments based on a json configuration file.") - parser.add_argument("--config", type=str, required=True, help="Path to the configuration JSON file.") - parser.add_argument("--output_dir", type=str, default="out", help="Directory to place the set of output checkpoints.") + parser.add_argument('-c', "--config", type=str, required=True, help="Path to the configuration JSON file.") + parser.add_argument('-o', "--output_dir", type=str, default="out", help="Directory to place the set of output checkpoints.") parser.add_argument("--csv_ckpt_dir", type=str, default="", help="Directory to place the set of csv checkpoints in csv_logs.") parser.add_argument("--prefix", type=str, default='', help="Optional prefix for tensorboard_run_name and out_dir.") parser.add_argument("--add_names", action="store_true", help="Include names of values of the configuration parameters in addition to values (may cause too long a file name).") From 265ce88a3e1c065fa47594a5d589dbc6f04957cf Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Tue, 20 Aug 2024 16:20:17 -0700 Subject: [PATCH 10/30] Add gating args for logging statistics This will highly speed up the training and explorations. --- train.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/train.py b/train.py index 8bbd87461d..bb25298c61 100644 --- a/train.py +++ b/train.py @@ -344,7 +344,8 @@ def parse_args(): # Module And Parameter Logging and Plots of Summary Statistics model_group.add_argument('--softmax_io_logging', default=False, action=argparse.BooleanOptionalAction, help="logs inputs and outputs of supported softmaxes") model_group.add_argument('--consmax_beta_gamma_logging', default=False, action=argparse.BooleanOptionalAction, help="logs beta and gamma") - logging_group.add_argument('--plot_statistics', default=False, action=argparse.BooleanOptionalAction, help="logs beta and gamma") + logging_group.add_argument('--create_statistics', default=False, action=argparse.BooleanOptionalAction) + logging_group.add_argument('--plot_statistics', default=False, action=argparse.BooleanOptionalAction) # CSV logging logging_group.add_argument('--csv_log', default=True, action=argparse.BooleanOptionalAction) @@ -805,7 +806,8 @@ def train(self): self.log_metrics_non_validation(lossf, running_mfu, self.iter_num) - create_statistics(self, graph_y_labels) + if self.args.create_statistics: + create_statistics(self, graph_y_labels) self.iter_num += 1 From 26076a867aea90a051fa65a89fe00dd3a10386fe Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Tue, 20 Aug 2024 16:20:44 -0700 Subject: [PATCH 11/30] Add sweep for ConSmaxV2 This new consmax has per head beta and gamma as well as scaling factors instead of directly learnable params. --- explorations/consmax_v2_sweep.json | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 explorations/consmax_v2_sweep.json diff --git a/explorations/consmax_v2_sweep.json b/explorations/consmax_v2_sweep.json new file mode 100644 index 0000000000..4e3f8b091f --- /dev/null +++ b/explorations/consmax_v2_sweep.json @@ -0,0 +1,26 @@ +[ + { + "max_iters": ["3500"], + "n_layer": ["6"], + "n_head": ["6"], + "n_embd": ["384"], + "block_size":["256"], + "device": ["cuda"], + "dtype": ["bfloat16"], + "dataset": ["shakespeare_char"], + "use_rotary_embeddings": [false], + "use_abs_pos_embeddings": [true], + "compile": [true], + "softmax_variant_attn": ["consmax", "consmax_v2"], + "consmax_initial_beta": ["2.5"], + "consmax_initial_gamma": ["100.0"], + "softmax_io_logging": [true], + "plot_statistics": [true], + "consmax_per_head": { + "conditions": [["softmax_variant_attn", "consmax_v2"]], + "options": [true, false] + }, + "use_post_ln": [true, false] + } +] + From 9a1691337f704c90c61b60092162c27c7d36f585 Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Tue, 20 Aug 2024 16:46:21 -0700 Subject: [PATCH 12/30] Add printout for model param names --- model_info_util/model_info.py | 26 +++++++++++--------------- train.py | 3 ++- 2 files changed, 13 insertions(+), 16 deletions(-) diff --git a/model_info_util/model_info.py b/model_info_util/model_info.py index 6e838252ee..0ffc913192 100644 --- a/model_info_util/model_info.py +++ b/model_info_util/model_info.py @@ -1,3 +1,4 @@ +import torch from torchinfo import summary from rich import print from rich.console import Console @@ -6,21 +7,6 @@ console = Console() -def colorize_summary(summary_str): - # Split the summary into lines - lines = summary_str.splitlines() - colored_lines = [] - - for line in lines: - if "Trainable params" in line or "Non-trainable params" in line: - colored_lines.append(f"[bold green]{line}[/bold green]") - elif "Total params" in line: - colored_lines.append(f"[bold red]{line}[/bold red]") - else: - colored_lines.append(f"[yellow]{line}[/yellow]") - - return "\n".join(colored_lines) - def print_summary(model): block_header = Text(f"High Level Parameters:", style="bold underline purple") console.print(block_header) @@ -40,3 +26,13 @@ def print_module_structure(module): console.print(f'{name}: {submodule}', style="yellow") console.print("-" * 50, style="dim") +def print_model_tree(model, indent="", print_params=False): + for name, module in model.named_children(): + print(indent + name + ": " + str(module.__class__.__name__)) + if isinstance(module, torch.nn.Module): + # Print parameters for the next level only + if print_params: + for param_name, _ in module.named_parameters(): + print(indent + " " + param_name) + else: # Recursively print submodules without parameters + print_model_tree(module, indent + " ") diff --git a/train.py b/train.py index bb25298c61..636dc3356d 100644 --- a/train.py +++ b/train.py @@ -9,7 +9,7 @@ import sys import time -from model_info_util.model_info import print_summary, print_module_structure, print_model_blocks +from model_info_util.model_info import print_summary, print_module_structure, print_model_blocks, print_model_tree import matplotlib.pyplot as plt import numpy as np import plotly.graph_objects as go @@ -518,6 +518,7 @@ def setup(self): print_summary(self.model) print_model_blocks(self.model) print_module_structure(self.model) + print_model_tree(self.model, print_params=True) # Optimizer self.scaler = torch.cuda.amp.GradScaler(enabled=(self.args.dtype == 'float16')) From dd0a852f89ca9a186cde7cf43dcccefc49980186 Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Tue, 20 Aug 2024 18:00:00 -0700 Subject: [PATCH 13/30] Add means for logging per head via tensorboard/csv Beta gamma logging per head added. --- explorations/consmax_v2_sweep.json | 8 +++-- statistics_util/statistic_plots.py | 47 +++++++++++++----------------- train.py | 18 ++++++++++-- 3 files changed, 41 insertions(+), 32 deletions(-) diff --git a/explorations/consmax_v2_sweep.json b/explorations/consmax_v2_sweep.json index 4e3f8b091f..80e4eb61ad 100644 --- a/explorations/consmax_v2_sweep.json +++ b/explorations/consmax_v2_sweep.json @@ -1,20 +1,22 @@ [ { "max_iters": ["3500"], - "n_layer": ["6"], + "n_layer": ["12"], "n_head": ["6"], "n_embd": ["384"], "block_size":["256"], "device": ["cuda"], "dtype": ["bfloat16"], "dataset": ["shakespeare_char"], + "use_gradient_checkpointing": [true], "use_rotary_embeddings": [false], "use_abs_pos_embeddings": [true], - "compile": [true], - "softmax_variant_attn": ["consmax", "consmax_v2"], + "compile": [false], + "softmax_variant_attn": ["consmax_v2"], "consmax_initial_beta": ["2.5"], "consmax_initial_gamma": ["100.0"], "softmax_io_logging": [true], + "create_statistics": [true], "plot_statistics": [true], "consmax_per_head": { "conditions": [["softmax_variant_attn", "consmax_v2"]], diff --git a/statistics_util/statistic_plots.py b/statistics_util/statistic_plots.py index cb56abf2e3..219dab6940 100644 --- a/statistics_util/statistic_plots.py +++ b/statistics_util/statistic_plots.py @@ -156,7 +156,8 @@ def plot_statistics(args, stats, graph_y_labels): create_box_plot(args.out_dir, plot_data, graph_y_labels, timestamp, data_type, stat_type) def create_statistics(self, graph_y_labels): - if self.args.softmax_variant_attn in ['consmax', 'polymax', 'strongermax']: + if self.args.softmax_variant_attn in ['consmax', 'consmax_v2', 'polymax', 'strongermax']: + betas = [] gammas = [] i_sum_vals = [] @@ -266,7 +267,24 @@ def create_statistics(self, graph_y_labels): betas.append(beta[0].item()) # are there more than just beta 0? # print("betas",beta,) # are there more than just beta 0? - self.log_gamma_beta(gamma, beta, self.iter_num, layer) + self.log_gamma_beta(gamma, beta, self.iter_num, layer, head_num=None) + elif self.args.softmax_variant_attn == 'consmax_v2': + gamma_location = f"transformer.h[{layer}].attn.softmax_layer_attn.gamma_factor" + beta_location = f"transformer.h[{layer}].attn.softmax_layer_attn.beta_factor" + + gamma = eval(f"self.model.{gamma_location}") + beta = eval(f"self.model.{beta_location}") + for h in range(0, int(self.args.n_head)): + # log gamma and beta points + g = gamma[h].item() + b = beta[h].item() + # print(gamma[h].item()) + # print(beta[h].item()) + + gammas.append(g) + betas.append(b) + + self.log_gamma_beta(g, b, self.iter_num, layer, head_num=str(h)) if self.args.box_plot_statistic and (self.iter_num % self.args.box_plot_interval == 0) and self.iter_num != 0: timestamp = time.strftime('%Y-%m-%d_%H-%M-%S', time.localtime()) @@ -298,28 +316,5 @@ def create_statistics(self, graph_y_labels): prefix="outputs") if self.args.softmax_variant_attn == 'consmax': self.write_to_csv(self.iter_num, *betas, *gammas, prefix="beta_gamma") - - """ - if self.iter_num % 50 == 0: - inputs = [] - outputs = [] - - for layer in range (self.args.n_layer): - inputs_location = f"transformer.h[{layer}].attn.softmax_layer.inputs" - outputs_location = f"transformer.h[{layer}].attn.softmax_layer.outputs" - - gamma = eval(f"self.model.{gamma_location}") - gammas.append(gamma[0].item()) # are there more than just gamma 0? - # print("gammas",gamma) # are there more than just gamma 0? - - beta = eval(f"self.model.{beta_location}") - betas.append(beta[0].item()) # are there more than just beta 0? - # print("betas",beta,) # are there more than just beta 0? - - self.log_gamma_beta(gamma, beta, self.iter_num, layer) - - + elif self.args.softmax_variant_attn == 'consmax_v2': self.write_to_csv(self.iter_num, *betas, *gammas, prefix="beta_gamma") - - - """ diff --git a/train.py b/train.py index 636dc3356d..f5b6815874 100644 --- a/train.py +++ b/train.py @@ -664,10 +664,22 @@ def write_to_csv(self, *args, prefix=""): writer.writerow(args) - def log_gamma_beta(self, gamma, beta, iter_num, layer_num): + def log_gamma_beta(self, gamma, beta, iter_num, layer_num, head_num=None): if self.args.tensorboard_log: - self.writer.add_scalar( "gamma_" + str(layer_num), gamma, iter_num) - self.writer.add_scalar( "beta_" + str(layer_num), beta, iter_num) + if head_num: + self.writer.add_scalars( + "gammas", + {"gamma_L" + str(layer_num) + "_H" + head_num: gamma}, + iter_num + ) + self.writer.add_scalars( + "betas", + {"beta_L" + str(layer_num) + "_H" + head_num: beta}, + iter_num + ) + else: + self.writer.add_scalar( "gamma_L" + str(layer_num), gamma, iter_num) + self.writer.add_scalar( "beta_L" + str(layer_num), beta, iter_num) if self.args.wandb_log and self.master_process: import wandb From 692520766b9ba956274d2e4c722ff65450091d31 Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Tue, 20 Aug 2024 19:32:34 -0700 Subject: [PATCH 14/30] Add softmax overflow recompute test This allows us to test recomputation when otherwise it would have overflowed. This might also be a good way for us to address the ConSmax overflow (adjust the beta to be higher if overflow is detected). --- explorations/strongermax_sweep.json | 26 +++++++-------- gpt_conf.py | 8 +++-- statistics_util/statistic_plots.py | 50 ++++++++++++++--------------- train.py | 2 ++ variations/softmax_variations.py | 24 ++++++++++++-- 5 files changed, 66 insertions(+), 44 deletions(-) diff --git a/explorations/strongermax_sweep.json b/explorations/strongermax_sweep.json index a1098b7b04..9aacbee674 100644 --- a/explorations/strongermax_sweep.json +++ b/explorations/strongermax_sweep.json @@ -1,27 +1,27 @@ [ { - "max_iters": ["6000"], + "max_iters": ["3500"], "n_layer": ["6"], "n_kv_group": ["6"], "n_head": ["6"], "n_embd": ["384"], "block_size":["256"], "device": ["cuda"], - "dtype": ["float16"], + "dtype": ["bfloat16"], "dataset": ["shakespeare_char"], "use_rotary_embeddings": [false], "use_abs_pos_embeddings": [true], - "compile": [true], + "compile": [false], "softmax_variant_attn": ["strongermax"], - "strongermax_strength": ["1.5", "2", "2.719", "3", "4", "5"], - "strongermax_divisor": ["1.0", "10.0", "100.0", "1000.0"], - "use_post_ln": [true, false], - "strongermax_use_xmax" : [true, false], - "strongermax_sum_to_1": [true, false], - "statistic": ["all_stats"], - "graph_type":["all"], - "box_plot_interval": ["1000"], - "box_plot_statistic": ["all"], - "patience": ["1000"] + "strongermax_strength": ["2.719"], + "strongermax_divisor": ["256"], + "strongermax_use_xmax" : [true], + "strongermax_xmax_guess" : ["-50"], + "strongermax_overflow_recompute" : [true], + "softmax_io_logging" : [false], + "create_statistics" : [false], + "plot_statistics" : [false], + "statistic" : ["input_max"], + "strongermax_sum_to_1": [false] } ] diff --git a/gpt_conf.py b/gpt_conf.py index 122498612e..892f1491d9 100644 --- a/gpt_conf.py +++ b/gpt_conf.py @@ -80,9 +80,11 @@ class GPTConfig: ## Strongermax options strongermax_strength: float = 2.0 # Softermax with option of 'stronger' (larger integer) bases - strongermax_sum_to_1: bool = False # Softermax with option of 'stronger' (larger integer) bases - strongermax_divisor: float = 1.0 # Softermax with option of 'stronger' (larger integer) bases - strongermax_use_xmax: bool = True # Softermax with option of 'stronger' (larger integer) bases + strongermax_sum_to_1: bool = False + strongermax_divisor: float = 1.0 + strongermax_use_xmax: bool = True + strongermax_xmax_guess: float = 1.0 + strongermax_overflow_recompute: bool = True ## ExpPolymax options exppolymax_use_euler_base: bool = True diff --git a/statistics_util/statistic_plots.py b/statistics_util/statistic_plots.py index 219dab6940..f9dfbf77de 100644 --- a/statistics_util/statistic_plots.py +++ b/statistics_util/statistic_plots.py @@ -193,31 +193,31 @@ def create_statistics(self, graph_y_labels): flattened = i_head.view(-1) ## Calculate statistics - i_means.append(torch.nanmean(flattened).item()) - i_medians.append(torch.nanmedian(flattened).item()) + # i_means.append(torch.nanmean(flattened).item()) + # i_medians.append(torch.nanmedian(flattened).item()) # Standard deviation, ignoring NaNs mask = ~torch.isnan(i_head) - i_stdevs.append(torch.std(i_head[mask]).item()) - i_sum_vals.append(torch.sum(i_head[mask]).item()) + # i_stdevs.append(torch.std(i_head[mask]).item()) + # i_sum_vals.append(torch.sum(i_head[mask]).item()) if self.iter_num % self.args.box_plot_interval == 0 and (self.args.box_plot_statistic == "all" or self.args.box_plot_statistic == "input"): box_plot_input_data.append(i_head[mask].detach().numpy()) # Max, temporarily replacing NaNs with -inf for calculation i_max_values.append(torch.max(torch.where(torch.isnan(i_head), torch.tensor(float('-inf')), i_head)).item()) - i_min_values.append(torch.min(torch.where(torch.isnan(i_head), torch.tensor(float('inf')), i_head)).item()) + # i_min_values.append(torch.min(torch.where(torch.isnan(i_head), torch.tensor(float('inf')), i_head)).item()) # Denominator computation for i_head - exp_flattened = torch.exp(i_head[mask]) - sum = torch.sum(exp_flattened) - denominator.append(sum.item()) - - # Append statistic to the input list of each head in each layer - self.stats['mean'][layer][i].append(torch.nanmean(flattened).item()) - self.stats['median'][layer][i].append(torch.nanmedian(flattened).item()) - self.stats['stdev'][layer][i].append(torch.std(i_head[mask]).item()) + # exp_flattened = torch.exp(i_head[mask]) + # sum = torch.sum(exp_flattened) + # denominator.append(sum.item()) + + # # Append statistic to the input list of each head in each layer + # self.stats['mean'][layer][i].append(torch.nanmean(flattened).item()) + # self.stats['median'][layer][i].append(torch.nanmedian(flattened).item()) + # self.stats['stdev'][layer][i].append(torch.std(i_head[mask]).item()) self.stats['max'][layer][i].append(torch.max(torch.where(torch.isnan(i_head), torch.tensor(float('-inf')), i_head)).item()) - self.stats['min'][layer][i].append(torch.min(torch.where(torch.isnan(i_head), torch.tensor(float('inf')), i_head)).item()) + # self.stats['min'][layer][i].append(torch.min(torch.where(torch.isnan(i_head), torch.tensor(float('inf')), i_head)).item()) @@ -233,26 +233,26 @@ def create_statistics(self, graph_y_labels): # Step 4: Calculate statistics ## Calculate statistics - o_means.append(torch.nanmean(flattened).item()) - o_medians.append(torch.nanmedian(flattened).item()) + # o_means.append(torch.nanmean(flattened).item()) + # o_medians.append(torch.nanmedian(flattened).item()) # Standard deviation, ignoring NaNs mask = ~torch.isnan(o_head) - o_stdevs.append(torch.std(o_head[mask]).item()) - o_sum_vals.append(torch.sum(o_head[mask]).item()) + # o_stdevs.append(torch.std(o_head[mask]).item()) + # o_sum_vals.append(torch.sum(o_head[mask]).item()) if self.iter_num % self.args.box_plot_interval == 0 and (self.args.box_plot_statistic == "all" or self.args.box_plot_statistic == "output"): box_plot_output_data.append(o_head[mask].detach().numpy()) # Max, temporarily replacing NaNs with -inf for calculation - o_max_values.append(torch.max(torch.where(torch.isnan(o_head), torch.tensor(float('-inf')), o_head)).item()) - o_min_values.append(torch.min(torch.where(torch.isnan(o_head), torch.tensor(float('inf')), o_head)).item()) + # o_max_values.append(torch.max(torch.where(torch.isnan(o_head), torch.tensor(float('-inf')), o_head)).item()) + # o_min_values.append(torch.min(torch.where(torch.isnan(o_head), torch.tensor(float('inf')), o_head)).item()) # Append statistic to the output list of each head in each layer - self.stats['o_mean'][layer][i].append(torch.nanmean(flattened).item()) - self.stats['o_median'][layer][i].append(torch.nanmedian(flattened).item()) - self.stats['o_stdev'][layer][i].append(torch.std(o_head[mask]).item()) - self.stats['o_max'][layer][i].append(torch.max(torch.where(torch.isnan(o_head), torch.tensor(float('-inf')), o_head)).item()) - self.stats['o_min'][layer][i].append(torch.min(torch.where(torch.isnan(o_head), torch.tensor(float('inf')), o_head)).item()) + # self.stats['o_mean'][layer][i].append(torch.nanmean(flattened).item()) + # self.stats['o_median'][layer][i].append(torch.nanmedian(flattened).item()) + # self.stats['o_stdev'][layer][i].append(torch.std(o_head[mask]).item()) + # self.stats['o_max'][layer][i].append(torch.max(torch.where(torch.isnan(o_head), torch.tensor(float('-inf')), o_head)).item()) + # self.stats['o_min'][layer][i].append(torch.min(torch.where(torch.isnan(o_head), torch.tensor(float('inf')), o_head)).item()) #BETA GAMMA if self.args.softmax_variant_attn == 'consmax': diff --git a/train.py b/train.py index f5b6815874..b1807d3556 100644 --- a/train.py +++ b/train.py @@ -289,6 +289,8 @@ def parse_args(): model_group.add_argument('--strongermax_sum_to_1', default=True, action=argparse.BooleanOptionalAction) model_group.add_argument("--strongermax_divisor", type=float, default=1.0) model_group.add_argument('--strongermax_use_xmax', default=True, action=argparse.BooleanOptionalAction) + model_group.add_argument('--strongermax_xmax_guess', type=float, default=None) + model_group.add_argument('--strongermax_overflow_recompute', default=True, action=argparse.BooleanOptionalAction) ### ExpPolymax Options model_group.add_argument('--exppolymax_use_euler_base', default=True, action=argparse.BooleanOptionalAction) diff --git a/variations/softmax_variations.py b/variations/softmax_variations.py index b07545d86b..398257ed81 100644 --- a/variations/softmax_variations.py +++ b/variations/softmax_variations.py @@ -155,29 +155,47 @@ def forward(self, x): e_x = torch.exp(x) return e_x/dequantize(quantize(self.gamma,scale_gamma), scale_gamma) -# Like softermax, but parameterized to permit exploration of bases greater than 2 +# Like softermax, but parameterized to permit exploration class Strongermax(nn.Module): """ Softmax with ability to increase to 'stronger' bases """ def __init__(self, config, dim=-1): super().__init__() self.dim = dim + + # Strongermax Params self.strength = config.strongermax_strength self.subtract_max = config.strongermax_use_xmax + self.xmax_guess = config.strongermax_xmax_guess self.sum_to_1 = config.strongermax_sum_to_1 self.divisor = config.strongermax_divisor + self.div_by_seq_len = config.div_by_seq_len + self.overflow_recompute = config.strongermax_overflow_recompute + # Input and Output Logging self.softmax_io_logging = config.softmax_io_logging print(self.softmax_io_logging) if self.softmax_io_logging: self.inputs = [] self.outputs = [] - self.div_by_seq_len = config.div_by_seq_len def forward(self, x): x_adj = None + if self.subtract_max: + # Guessing correctly instead of subtracting real max can save a pass + # else we use real xmax max_x = x.max(dim=self.dim, keepdim=True).values - x_adj = x - max_x + if self.overflow_recompute: + if (torch.max(x - self.xmax_guess)) > (self.xmax_guess + 88): + print("popcorn") + x_adj = x - max_x + else: + x_adj = x - self.xmax_guess + else: + if self.xmax_guess: + x_adj = x - self.xmax_guess + else: + x_adj = x - max_x else: x_adj = x From b903c04ca19c96ad5fe106cf118f3768573fc2a8 Mon Sep 17 00:00:00 2001 From: David Lisbonne Date: Wed, 21 Aug 2024 10:48:13 -0700 Subject: [PATCH 15/30] Initial commit --- model.py | 33 ++-------------- train.py | 21 ++++++++-- variations/model_variations.py | 71 ++++++++++++++++++++++++++++++++++ 3 files changed, 92 insertions(+), 33 deletions(-) create mode 100644 variations/model_variations.py diff --git a/model.py b/model.py index cafac75714..4f18066cf6 100644 --- a/model.py +++ b/model.py @@ -643,38 +643,13 @@ def crop_block_size(self, block_size): block.attn.bias = block.attn.bias[:,:,:block_size,:block_size] @classmethod - def from_pretrained(cls, model_type, override_args=None): + def from_pretrained(cls, config, model_type): # assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'} - override_args = override_args or {} # default to empty dict - # only dropout can be overridden see more notes below - assert all(k == 'dropout' for k in override_args) - from transformers import GPT2LMHeadModel, AutoModelForCausalLM + from transformers import GPT2LMHeadModel + print(f"loading weights from pretrained gpt: {model_type}") - - # n_layer, n_head and n_embd are determined from model_type - config_args = { - 'gpt2': dict(n_layer=12, n_head=12, n_embd=768), # 124M params - 'gpt2-medium': dict(n_layer=24, n_head=16, n_embd=1024), # 350M params - 'gpt2-large': dict(n_layer=36, n_head=20, n_embd=1280), # 774M params - 'gpt2-xl': dict(n_layer=48, n_head=25, n_embd=1600), # 1558M params - }[model_type] - print("forcing vocab_size=50257, block_size=1024, bias=True") - config_args['vocab_size'] = 50257 # always 50257 for GPT model checkpoints - config_args['block_size'] = 1024 # always 1024 for GPT model checkpoints - config_args['bias'] = True # always True for GPT model checkpoints - config_args['window_size'] = 128 # always None for GPT model checkpoints - # we can override the dropout rate, if desired - if 'dropout' in override_args: - print(f"overriding dropout rate to {override_args['dropout']}") - config_args['dropout'] = override_args['dropout'] + # create a from-scratch initialized minGPT model - # TODO: pass more cmd line flags like "softmax" variant into this from_pretrained - config = GPTConfig(**config_args) - - # overriding our custom GPTConf presets of "rmsnorm" to "layernorm" for compatibility - config.norm_variant_attn = "layernorm" - config.norm_variant_output = "layernorm" - model = GPT(config) model_hf = GPT2LMHeadModel.from_pretrained(model_type) diff --git a/train.py b/train.py index 6f71c510b7..453da098a0 100644 --- a/train.py +++ b/train.py @@ -20,6 +20,7 @@ from torch.nn.parallel import DistributedDataParallel as DDP from torch.utils.tensorboard import SummaryWriter from statistics_util.statistic_plots import initialize_statistics, plot_statistics, create_statistics +from variations.model_variations import model_variation_dictionary from model import GPT, GPTConfig @@ -468,6 +469,7 @@ def setup(self): ckpt_path = os.path.join(self.args.out_dir, 'ckpt.pt') checkpoint = torch.load(ckpt_path, map_location=self.device) checkpoint_model_args = checkpoint['model_args'] + print(f"*** printing checkpoint_model_args directly as read from the checkpoint :\n {checkpoint_model_args}") for k in ['n_layer', 'n_head', 'n_kv_group', 'n_embd', 'block_size', 'bias', 'vocab_size', 'window_size', 'gate']: self.model_args[k] = checkpoint_model_args[k] self.load_data() @@ -482,13 +484,24 @@ def setup(self): self.model.load_state_dict(state_dict) self.iter_num = checkpoint['iter_num'] self.best_val_loss = checkpoint['best_val_loss'] + exit() elif self.args.init_from.startswith('gpt2'): - override_args = dict(dropout=self.args.dropout) + + assert self.args.init_from in model_variation_dictionary + + # FIXME: delete the override args + # override_args = dict(dropout=self.args.dropout) + self.iter_num = 0 # for starting from scratch self.best_val_loss = 1e9 # really big number - self.model = GPT.from_pretrained(self.args.gpt2_type, override_args) - for k in ['n_layer', 'n_head', 'n_kv_group', 'n_embd', 'block_size', 'bias', 'vocab_size']: - self.model_args[k] = getattr(self.model.config, k) + + gptconf = GPTConfig(**self.model_args) + variation_dict = model_variation_dictionary[self.args.init_from] + # NOTE: the hierarchy of parameters goes: 1)variation_dict >> 2)cmd-line args >> 3)GPTConfig defaults + for k in variation_dict: + gptconf[k] = variation_dict[k] + + self.model = GPT.from_pretrained(gptconf, model_type=self.args.gpt2_type) self.load_data() elif self.args.init_from == 'prev_run': ckpt_path = os.path.join(self.args.prev_run_ckpt, 'ckpt.pt') diff --git a/variations/model_variations.py b/variations/model_variations.py new file mode 100644 index 0000000000..d8ee83e80c --- /dev/null +++ b/variations/model_variations.py @@ -0,0 +1,71 @@ +import torch +import torch.nn as nn +import math +import torch.nn.functional as F + + +model_variation_dictionary = { + + 'gpt2': { + 'n_layer': 12, + 'n_head': 12, + 'n_embd': 768, + 'vocab_size': 50257, + 'block_size': 1024, + 'bias': True, + 'window_size': 128, + 'norm_variant_attn': 'layernorm', + 'norm_variant_output': 'layernorm', + }, + 'gpt2-medium': { + 'n_layer': 24, + 'n_head': 16, + 'n_embd': 1024, + 'vocab_size': 50257, + 'block_size': 1024, + 'bias': True, + 'window_size': 128, + 'norm_variant_attn': 'layernorm', + 'norm_variant_output': 'layernorm', + }, + 'gpt2-large': { + 'n_layer': 36, + 'n_head': 20, + 'n_embd': 1280, + 'vocab_size': 50257, + 'block_size': 1024, + 'bias': True, + 'window_size': 128, + 'norm_variant_attn': 'layernorm', + 'norm_variant_output': 'layernorm', + }, + 'gpt2-xl': { + 'n_layer': 48, + 'n_head': 25, + 'n_embd': 1600, + 'vocab_size': 50257, + 'block_size': 1024, + 'bias': True, + 'window_size': 128, + 'norm_variant_attn': 'layernorm', + 'norm_variant_output': 'layernorm', + }, +} + +""" + +# Just moving this away from model.py but keeping around just in case + + # n_layer, n_head and n_embd are determined from model_type + config_args = { + 'gpt2': dict(n_layer=12, n_head=12, n_embd=768), # 124M params + 'gpt2-medium': dict(n_layer=24, n_head=16, n_embd=1024), # 350M params + 'gpt2-large': dict(n_layer=36, n_head=20, n_embd=1280), # 774M params + 'gpt2-xl': dict(n_layer=48, n_head=25, n_embd=1600), # 1558M params + }[model_type] + print("forcing vocab_size=50257, block_size=1024, bias=True") + config_args['vocab_size'] = 50257 # always 50257 for GPT model checkpoints + config_args['block_size'] = 1024 # always 1024 for GPT model checkpoints + config_args['bias'] = True # always True for GPT model checkpoints + config_args['window_size'] = 128 # always None for GPT model checkpoints +""" \ No newline at end of file From 273d9ccf046b93c6986fbc6189eac5692233e975 Mon Sep 17 00:00:00 2001 From: David Lisbonne Date: Wed, 21 Aug 2024 19:08:12 +0000 Subject: [PATCH 16/30] simplifying train.py init_from branching and added support for overriding args for finetuning when resuming or starting from prev_run --- train.py | 48 +++++++++++++++++++++++------------------------- 1 file changed, 23 insertions(+), 25 deletions(-) diff --git a/train.py b/train.py index 453da098a0..3405aa6cbf 100644 --- a/train.py +++ b/train.py @@ -291,7 +291,7 @@ def parse_args(): ### ExpPolymax Options model_group.add_argument('--exppolymax_use_euler_base', default=True, action=argparse.BooleanOptionalAction) - model_group.add_argument("--exppolymax_base", type=float, default="4") + model_group.add_argument("--exppolymax_base", type=float, default=4.0) model_group.add_argument("--exppolymax_y_intercept", type=float, default=1.0) model_group.add_argument("--exppolymax_power", type=float, default=2.0) model_group.add_argument("--exppolymax_divisor", type=float, default=1000.0) @@ -465,25 +465,37 @@ def setup(self): self.model = GPT(gptconf) self.iter_num = 0 # for starting from scratch self.best_val_loss = 1e9 # really big number - elif self.args.init_from == 'resume': - ckpt_path = os.path.join(self.args.out_dir, 'ckpt.pt') - checkpoint = torch.load(ckpt_path, map_location=self.device) + elif self.args.init_from == 'resume' or self.args.init_from == 'prev_run': + if self.args.init_from == 'resume': + ckpt_path = os.path.join(self.args.out_dir, 'ckpt.pt') + checkpoint = torch.load(ckpt_path, map_location=self.device) + self.iter_num = checkpoint['iter_num'] + else: + ckpt_path = os.path.join(self.args.prev_run_ckpt, 'ckpt.pt') + checkpoint = torch.load(ckpt_path, map_location=self.device) + self.iter_num = 0 + + # we should enforce that during resume training, the identical model args are used checkpoint_model_args = checkpoint['model_args'] - print(f"*** printing checkpoint_model_args directly as read from the checkpoint :\n {checkpoint_model_args}") - for k in ['n_layer', 'n_head', 'n_kv_group', 'n_embd', 'block_size', 'bias', 'vocab_size', 'window_size', 'gate']: - self.model_args[k] = checkpoint_model_args[k] + self.model_args = checkpoint_model_args + + # support for changing select params from resume (eg. for finetuning) based on cmd-line args entered (checks if diff than defaults) + altered_model_args = {action.dest: getattr(self.args, action.dest) for action in self.model_group._group_actions if action.default != getattr(self.args, action.dest)} + for k in altered_model_args: + self.model_args[k] = altered_model_args[k] + self.load_data() gptconf = GPTConfig(**self.model_args) self.model = GPT(gptconf) - ## TODO: Add means here to udpate the resume for: block size (finetune for longer context), rotary type, rope length, softmax type, etc. + ## TODO: Add ability here to swap WTE factors. state_dict = checkpoint['model'] for k,v in list(state_dict.items()): if k.startswith('_orig_mod.'): state_dict[k[len('_orig_mod.'):]] = state_dict.pop(k) self.model.load_state_dict(state_dict) - self.iter_num = checkpoint['iter_num'] self.best_val_loss = checkpoint['best_val_loss'] + exit() elif self.args.init_from.startswith('gpt2'): @@ -503,22 +515,8 @@ def setup(self): self.model = GPT.from_pretrained(gptconf, model_type=self.args.gpt2_type) self.load_data() - elif self.args.init_from == 'prev_run': - ckpt_path = os.path.join(self.args.prev_run_ckpt, 'ckpt.pt') - checkpoint = torch.load(ckpt_path, map_location=self.device) - checkpoint_model_args = checkpoint['model_args'] - for k in ['n_layer', 'n_head', 'n_kv_group', 'n_embd', 'block_size', 'bias', 'vocab_size', 'window_size', 'gate']: - self.model_args[k] = checkpoint_model_args[k] - self.load_data() - gptconf = GPTConfig(**self.model_args) - self.model = GPT(gptconf) - state_dict = checkpoint['model'] - for k,v in list(state_dict.items()): - if k.startswith('_orig_mod.'): - state_dict[k[len('_orig_mod.'):]] = state_dict.pop(k) - self.model.load_state_dict(state_dict) - self.iter_num = 0 - self.best_val_loss = checkpoint['best_val_loss'] + + exit() if self.args.block_size < self.model.config.block_size: self.model.crop_block_size(self.args.block_size) From c42bffc19bcd8954b8997f8a29516305dd35e14c Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Wed, 21 Aug 2024 13:10:17 -0700 Subject: [PATCH 17/30] Require xmax_guess set if overflow recompute --- variations/softmax_variations.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/variations/softmax_variations.py b/variations/softmax_variations.py index 398257ed81..eae8e9350f 100644 --- a/variations/softmax_variations.py +++ b/variations/softmax_variations.py @@ -171,6 +171,9 @@ def __init__(self, config, dim=-1): self.div_by_seq_len = config.div_by_seq_len self.overflow_recompute = config.strongermax_overflow_recompute + if self.overflow_recompute: + assert(self.xmax_guess is not None, "for overflow recompute, xmax_guess must be set") # ensure x_intercept is strictly left of the y-axis + # Input and Output Logging self.softmax_io_logging = config.softmax_io_logging print(self.softmax_io_logging) @@ -187,7 +190,6 @@ def forward(self, x): max_x = x.max(dim=self.dim, keepdim=True).values if self.overflow_recompute: if (torch.max(x - self.xmax_guess)) > (self.xmax_guess + 88): - print("popcorn") x_adj = x - max_x else: x_adj = x - self.xmax_guess From 541d31d5fe717ba12b972024edbd87d4f1210b99 Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Wed, 21 Aug 2024 13:29:44 -0700 Subject: [PATCH 18/30] Make overflow recompute false by default --- gpt_conf.py | 2 +- train.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/gpt_conf.py b/gpt_conf.py index 892f1491d9..504f04b675 100644 --- a/gpt_conf.py +++ b/gpt_conf.py @@ -84,7 +84,7 @@ class GPTConfig: strongermax_divisor: float = 1.0 strongermax_use_xmax: bool = True strongermax_xmax_guess: float = 1.0 - strongermax_overflow_recompute: bool = True + strongermax_overflow_recompute: bool = False ## ExpPolymax options exppolymax_use_euler_base: bool = True diff --git a/train.py b/train.py index b1807d3556..1838797146 100644 --- a/train.py +++ b/train.py @@ -290,7 +290,7 @@ def parse_args(): model_group.add_argument("--strongermax_divisor", type=float, default=1.0) model_group.add_argument('--strongermax_use_xmax', default=True, action=argparse.BooleanOptionalAction) model_group.add_argument('--strongermax_xmax_guess', type=float, default=None) - model_group.add_argument('--strongermax_overflow_recompute', default=True, action=argparse.BooleanOptionalAction) + model_group.add_argument('--strongermax_overflow_recompute', default=False, action=argparse.BooleanOptionalAction) ### ExpPolymax Options model_group.add_argument('--exppolymax_use_euler_base', default=True, action=argparse.BooleanOptionalAction) From 4b4f415f99e6d4c7380a5f764a6f3f122577f492 Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Wed, 21 Aug 2024 14:35:32 -0700 Subject: [PATCH 19/30] Add initial code emulating hardware --- tf_np_golden_gen/test.py | 185 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 185 insertions(+) create mode 100644 tf_np_golden_gen/test.py diff --git a/tf_np_golden_gen/test.py b/tf_np_golden_gen/test.py new file mode 100644 index 0000000000..cec3795266 --- /dev/null +++ b/tf_np_golden_gen/test.py @@ -0,0 +1,185 @@ +import numpy as np +import argparse +import pickle +import torch + +class LayerNorm: + def __init__(self, ndim, bias=True): + self.weight = np.ones((ndim,)) + self.bias = np.zeros((ndim,)) if bias else None + + def forward(self, x): + mean = np.mean(x, axis=-1, keepdims=True) + std = np.std(x, axis=-1, keepdims=True) + x = (x - mean) / (std + 1e-5) + if self.bias is not None: + x = x * self.weight + self.bias + return x + + def load_weights(self, weights): + self.weight = weights['weight'] + if self.bias is not None: + self.bias = weights['bias'] + +class CausalSelfAttention: + def __init__(self, n_embd, n_head, block_size): + self.n_head = n_head + self.n_embd = n_embd + self.block_size = block_size + + self.c_attn_q = np.random.randn(n_embd, n_embd) + self.c_attn_k = np.random.randn(n_embd, n_embd) + self.c_attn_v = np.random.randn(n_embd, n_embd) + self.c_proj = np.random.randn(n_embd, n_embd) + + self.bias = np.tril(np.ones((block_size, block_size))).reshape(1, 1, block_size, block_size) + + def forward(self, x, cache=None): + B, T, C = x.shape + + q = np.dot(x, self.c_attn_q) + k = np.dot(x, self.c_attn_k) + v = np.dot(x, self.c_attn_v) + + q = q.reshape(B, T, self.n_head, C // self.n_head).transpose(0, 2, 1, 3) + k = k.reshape(B, T, self.n_head, C // self.n_head).transpose(0, 2, 1, 3) + v = v.reshape(B, T, self.n_head, C // self.n_head).transpose(0, 2, 1, 3) + + if cache is not None: + k = np.concatenate([cache['k'], k], axis=2) + v = np.concatenate([cache['v'], v], axis=2) + else: + cache = {'k': k, 'v': v} + + att = np.matmul(q, k.transpose(0, 1, 3, 2)) / np.sqrt(k.shape[-1]) + att = np.where(self.bias[:,:,:T,:T] == 0, -np.inf, att) + att = np.exp(att - np.max(att, axis=-1, keepdims=True)) + att = att / np.sum(att, axis=-1, keepdims=True) + + y = np.matmul(att, v) + y = y.transpose(1, 2).reshape(B, T, C) + y = np.dot(y, self.c_proj) + + return y, cache + + def load_weights(self, weights): + self.c_attn_q = weights['c_attn_q'] + self.c_attn_k = weights['c_attn_k'] + self.c_attn_v = weights['c_attn_v'] + self.c_proj = weights['c_proj'] + +class MLP: + def __init__(self, n_embd): + self.c_fc = np.random.randn(n_embd, 4 * n_embd) + self.c_proj = np.random.randn(4 * n_embd, n_embd) + + def forward(self, x): + x = np.dot(x, self.c_fc) + x = np.maximum(0, x) # GELU activation approximation + x = np.dot(x, self.c_proj) + return x + + def load_weights(self, weights): + self.c_fc = weights['c_fc'] + self.c_proj = weights['c_proj'] + +class Block: + def __init__(self, n_embd, n_head, block_size): + self.ln_1 = LayerNorm(n_embd) + self.attn = CausalSelfAttention(n_embd, n_head, block_size) + self.ln_2 = LayerNorm(n_embd) + self.mlp = MLP(n_embd) + + def forward(self, x, cache=None): + attn_out, new_cache = self.attn.forward(self.ln_1.forward(x), cache) + x = x + attn_out + x = x + self.mlp.forward(self.ln_2.forward(x)) + return x, new_cache + + def load_weights(self, weights): + self.ln_1.load_weights(weights['ln_1']) + self.attn.load_weights(weights['attn']) + self.ln_2.load_weights(weights['ln_2']) + self.mlp.load_weights(weights['mlp']) + +class Transformer: + def __init__(self, vocab_size, n_layer, n_head, n_embd, block_size): + self.wte = np.random.randn(vocab_size, n_embd) + self.wpe = np.random.randn(block_size, n_embd) + + self.blocks = [Block(n_embd, n_head, block_size) for _ in range(n_layer)] + self.ln_f = LayerNorm(n_embd) + + self.lm_head = self.wte # Weight tying + + def forward(self, idx, cache=None): + B, T = idx.shape + x = self.wte[idx] + self.wpe[:T] + + new_cache = [] + for i, block in enumerate(self.blocks): + x, block_cache = block.forward(x, cache[i] if cache is not None else None) + new_cache.append(block_cache) + + x = self.ln_f.forward(x) + return x, new_cache + + def load_weights(self, weights): + self.wte = weights['wte'] + self.wpe = weights['wpe'] + + for i, block in enumerate(self.blocks): + block.load_weights(weights['blocks'][i]) + + self.ln_f.load_weights(weights['ln_f']) + self.lm_head = self.wte # Ensure weight tying + +def load_model_weights(file_path): + with open(file_path, 'rb') as f: + weights = pickle.load(f) + return weights + +def summarization_stage(model, idx): + logits, cache = model.forward(idx) + next_token = np.argmax(logits[:, -1, :], axis=-1) + return next_token, cache + +def generation_stage(model, idx, max_new_tokens, cache, block_size): + for _ in range(max_new_tokens): + idx_cond = idx if idx.shape[1] <= block_size else idx[:, -block_size:] + logits, cache = model.forward(idx_cond, cache) + next_token = np.argmax(logits[:, -1, :], axis=-1) + idx = np.concatenate((idx, next_token[:, None]), axis=1) + return idx + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--n_layer', type=int, default=12) + parser.add_argument('--n_head', type=int, default=12) + parser.add_argument('--n_embd', type=int, default=768) + parser.add_argument('--block_size', type=int, default=1024) + parser.add_argument('--vocab_size', type=int, default=50304) + parser.add_argument('--max_new_tokens', type=int, default=50) + parser.add_argument('--weights_path', type=str, required=True) + args = parser.parse_args() + + model = Transformer(args.vocab_size, args.n_layer, args.n_head, args.n_embd, args.block_size) + + # Load weights + weights = load_model_weights(args.weights_path) + model.load_weights(weights) + + # Sample input: just a sequence of zeros + idx = np.zeros((1, 1), dtype=int) + + # Summarization stage + next_token, cache = summarization_stage(model, idx) + idx = np.concatenate((idx, next_token[:, None]), axis=1) + + # Generation stage + generated_sequence = generation_stage(model, idx, args.max_new_tokens, cache, args.block_size) + print("Generated sequence:", generated_sequence) + +if __name__ == "__main__": + main() + From 2fd45cf5eed7a0f9cc3e4025cf81168314f39ded Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Wed, 21 Aug 2024 15:03:19 -0700 Subject: [PATCH 20/30] Add latest train and pickel for testing --- tf_np_golden_gen/test.pkl | Bin 0 -> 744193 bytes tf_np_golden_gen/test.py | 76 ++++++++++++++++++++++++++++++++++---- 2 files changed, 69 insertions(+), 7 deletions(-) create mode 100644 tf_np_golden_gen/test.pkl diff --git a/tf_np_golden_gen/test.pkl b/tf_np_golden_gen/test.pkl new file mode 100644 index 0000000000000000000000000000000000000000..4dde0e62237db49b8dec812a8ab3d729b46166f8 GIT binary patch literal 744193 zcmagFc~nnt^fuf)sL-T{LNZh;Wk{d1_a`ND$}F>z5JfVER8%yNQX(nMilp>8dlN-w 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z2E{=p(mS0B=j{OYHE3e7vpJn`=#5XBo)2Ri6g#QymRMUmBle# z^xCRQ?n6@<<{ROHe@S4hb^{!(oJEx|Md+7OE)0etJg|F+*n9t72#BtX+#t?~FNai_8?O@Yb5D~6FxIG1+_)f@zJ!H}6B^^$ zbyfyOY!)HhB1c@h(XT3^L5tp+T*fVuR9u*}|sQVqTo;OLdwLgNJ?O&k&Kr%eo zSp?sU2XWQY4!EbcgfCfIz$fJ{EdRY7&h{UMB{#eBrCFu)UZ)3}C3ZrCWy^V8f+2LC z&@PMk8zJmHmh^o~xkhRqvoqTu!~eMRLIBWC(w+ ziLT|N>Cl&H)G@+RVtYP@g|HL{WzD6)e%fHw)(3n2aimL$jJ;xWIqQrxzfNn0LuH>J zFJKEk+vpFaH(pg`<<+oKw%&a9F)5&wCt)ZcE4U zxMlOGEx8P=v Date: Wed, 21 Aug 2024 15:05:12 -0700 Subject: [PATCH 21/30] Update kv_group to default as none --- train.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/train.py b/train.py index 1838797146..f461991ad1 100644 --- a/train.py +++ b/train.py @@ -54,7 +54,7 @@ def parse_args(): model_group.add_argument('--block_size', default=256, type=int) model_group.add_argument('--n_layer', default=6, type=int) model_group.add_argument('--n_head', default=6, type=int) - model_group.add_argument('--n_kv_group', default=6, type=int) + model_group.add_argument('--n_kv_group', default=None, type=int) model_group.add_argument('--n_embd', default=384, type=int) model_group.add_argument('--dropout', default=0.2, type=float) model_group.add_argument('--use_post_ln', default=False, action=argparse.BooleanOptionalAction) From 355149cb1530cc3d0e9512a936dbf615a73174e1 Mon Sep 17 00:00:00 2001 From: Michael Moffatt Date: Wed, 21 Aug 2024 20:16:59 -0400 Subject: [PATCH 22/30] added file for uploading to huggingfacehub --- huggingface_model/upload.py | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 huggingface_model/upload.py diff --git a/huggingface_model/upload.py b/huggingface_model/upload.py new file mode 100644 index 0000000000..a0b3480afe --- /dev/null +++ b/huggingface_model/upload.py @@ -0,0 +1,8 @@ +from transformers import AutoTokenizer, AutoConfig, AutoModel + +config = AutoConfig.from_pretrained("gpt2-custom") +config.push_to_hub("custom_gpt2") +model = AutoModel.from_pretrained("gpt2-custom") +model.push_to_hub("custom_gpt2") +tokenizer = AutoTokenizer.from_pretrained("gpt2-custom") +tokenizer.push_to_hub("custom_gpt2") \ No newline at end of file From 4206edf3b14c4a6a634e247c99e1d0f2ca6da1bf Mon Sep 17 00:00:00 2001 From: Michael Moffatt Date: Wed, 21 Aug 2024 20:36:37 -0400 Subject: [PATCH 23/30] moved sample to its own file --- huggingface_model/gpt_sample.py | 8 ++++++++ huggingface_model/gpt_train.py | 13 ++----------- 2 files changed, 10 insertions(+), 11 deletions(-) create mode 100644 huggingface_model/gpt_sample.py diff --git a/huggingface_model/gpt_sample.py b/huggingface_model/gpt_sample.py new file mode 100644 index 0000000000..9476b4de86 --- /dev/null +++ b/huggingface_model/gpt_sample.py @@ -0,0 +1,8 @@ +from transformers import pipeline, GPT2LMHeadModel, GPT2Tokenizer + +model = GPT2LMHeadModel.from_pretrained("gpt2-custom") +tokenizer = GPT2Tokenizer.from_pretrained("gpt2-custom") + +generator = pipeline("text-generation", model=model, tokenizer=tokenizer) +output = generator("Once upon a time", max_length=50, num_return_sequences=1) +print(output) \ No newline at end of file diff --git a/huggingface_model/gpt_train.py b/huggingface_model/gpt_train.py index 72a622e80e..a68eb0c93b 100644 --- a/huggingface_model/gpt_train.py +++ b/huggingface_model/gpt_train.py @@ -1,8 +1,6 @@ -from transformers import Trainer, TrainingArguments, AutoModelForCausalLM, AutoTokenizer +from transformers import Trainer, TrainingArguments from datasets import load_dataset import gpt_model -from transformers import pipeline -from torch.utils.data import DataLoader # Load the Wikitext dataset dataset = load_dataset("wikitext", "wikitext-2-raw-v1") @@ -51,11 +49,4 @@ def tokenize_function(examples): trainer.train() gpt_model.pretrained_model.save_pretrained("gpt2-custom") -gpt_model.tokenizer.save_pretrained("gpt2-custom") - -# model = AutoModelForCausalLM.from_pretrained("gpt2-custom") -# tokenizer = AutoTokenizer.from_pretrained("gpt2-custom") - -generator = pipeline("text-generation", model=gpt_model.pretrained_model, tokenizer=gpt_model.tokenizer) -output = generator("Once upon a time", max_length=50, num_return_sequences=1) -print(output) \ No newline at end of file +gpt_model.tokenizer.save_pretrained("gpt2-custom") \ No newline at end of file From a5e75924d98714c1c3a0025d985e9585e596f06f Mon Sep 17 00:00:00 2001 From: David Lisbonne Date: Thu, 22 Aug 2024 03:23:00 +0000 Subject: [PATCH 24/30] update to sample.py to properly load pretrained GPT2 model --- sample.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/sample.py b/sample.py index 52a82800c9..8172ed1a17 100644 --- a/sample.py +++ b/sample.py @@ -15,6 +15,7 @@ from collections import OrderedDict from model import GPT, GPTConfig +from variations.model_variations import model_variation_dictionary def parse_args(): @@ -135,7 +136,12 @@ def main(): model.load_state_dict(state_dict, strict=False) else: - model = GPT.from_pretrained(args.init_from, dict(dropout=0.0)) + # need to create a completely "default" GPTConfig and overwrite using model_variations + gptconf = GPTConfig() + variation_dict = model_variation_dictionary[args.init_from] + for k in variation_dict: + gptconf[k] = variation_dict[k] + model = GPT.from_pretrained(gptconf, model_type=args.init_from) model.eval() model.to(args.device) From 02eb15ce74479ea1d97e9d11ed48975e1e35b034 Mon Sep 17 00:00:00 2001 From: David Lisbonne Date: Fri, 23 Aug 2024 09:53:00 -0700 Subject: [PATCH 25/30] Removed exits --- train.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/train.py b/train.py index 3405aa6cbf..b3bdf21468 100644 --- a/train.py +++ b/train.py @@ -495,8 +495,7 @@ def setup(self): state_dict[k[len('_orig_mod.'):]] = state_dict.pop(k) self.model.load_state_dict(state_dict) self.best_val_loss = checkpoint['best_val_loss'] - - exit() + elif self.args.init_from.startswith('gpt2'): assert self.args.init_from in model_variation_dictionary @@ -516,8 +515,6 @@ def setup(self): self.model = GPT.from_pretrained(gptconf, model_type=self.args.gpt2_type) self.load_data() - exit() - if self.args.block_size < self.model.config.block_size: self.model.crop_block_size(self.args.block_size) self.model_args['block_size'] = self.args.block_size From c4be2b12613ada57ad9cf93182f9f52e1631b657 Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Fri, 23 Aug 2024 14:27:32 -0700 Subject: [PATCH 26/30] Add test script and README for goldenbrick --- tf_np_golden_gen/README.md | 5 +++++ tf_np_golden_gen/test_script.sh | 4 ++++ 2 files changed, 9 insertions(+) create mode 100644 tf_np_golden_gen/README.md create mode 100644 tf_np_golden_gen/test_script.sh diff --git a/tf_np_golden_gen/README.md b/tf_np_golden_gen/README.md new file mode 100644 index 0000000000..d2f25089dd --- /dev/null +++ b/tf_np_golden_gen/README.md @@ -0,0 +1,5 @@ +# WIP Section + +This is for porting weights directly from the model into numpy for granular and +actual integer inference tests and quantization verification. + diff --git a/tf_np_golden_gen/test_script.sh b/tf_np_golden_gen/test_script.sh new file mode 100644 index 0000000000..c339b708de --- /dev/null +++ b/tf_np_golden_gen/test_script.sh @@ -0,0 +1,4 @@ +#!/bin/bash + +python3 test.py --weights_path test.pkl --n_head 3 --n_layer 6 --n_embd 48 + From 7006be55f95b587277b51ce6f1b308f9bcbe921d Mon Sep 17 00:00:00 2001 From: Gregory Kielian Date: Fri, 23 Aug 2024 14:38:53 -0700 Subject: [PATCH 27/30] Add note and modification for weight export --- sample.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/sample.py b/sample.py index bbf00872a0..c2df3fe4c8 100644 --- a/sample.py +++ b/sample.py @@ -94,11 +94,20 @@ def save_args(args, out_dir): json.dump(vars(args), f, indent=4) +#TODO: Rename to reflect general purpose def save_quantized_data(state_dict, out_file): to_save = OrderedDict() for k, v in list(state_dict.items()): - if "mlp_act" in k or "attn_act" in k or k.endswith("quantized_bias") or k.endswith("bias_norm") or k.endswith("zero_point") or k.endswith("quantized_weight") or k.endswith("weight_norm"): - to_save[k] = v.cpu().numpy() + # if "mlp_act" in k or "attn_act" in k or k.endswith("quantized_bias") or k.endswith("bias_norm") or k.endswith("zero_point") or k.endswith("quantized_weight") or k.endswith("weight_norm"): + to_save[k] = v.cpu().numpy() + + with open(f"{out_file}.pkl", 'wb') as f: + pickle.dump(to_save, f) + +def save_data(state_dict, out_file): + to_save = OrderedDict() + for k, v in list(state_dict.items()): + to_save[k] = v.cpu().numpy() with open(f"{out_file}.pkl", 'wb') as f: pickle.dump(to_save, f) From 3eaaa6985e795556b55e0d87a43d3eed53762173 Mon Sep 17 00:00:00 2001 From: David Lisbonne Date: Fri, 23 Aug 2024 15:41:50 -0700 Subject: [PATCH 28/30] Update to state_dict translation to correctly assign q,k,v matrices from split --- legacy_model.py | 4 ++-- model.py | 2 +- train.py | 13 +++++-------- variations/model_variations.py | 4 ---- 4 files changed, 8 insertions(+), 15 deletions(-) diff --git a/legacy_model.py b/legacy_model.py index bc2ee86423..ae7434361a 100644 --- a/legacy_model.py +++ b/legacy_model.py @@ -120,7 +120,7 @@ def forward(self, x): return x @dataclass -class kGPTConfig: +class GPTConfig: block_size: int = 1024 vocab_size: int = 50304 # GPT-2 vocab_size of 50257, padded up to nearest multiple of 64 for efficiency n_layer: int = 12 @@ -129,7 +129,7 @@ class kGPTConfig: dropout: float = 0.0 bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster -class kGPT(nn.Module): +class GPT(nn.Module): def __init__(self, config): super().__init__() diff --git a/model.py b/model.py index 4f18066cf6..402aefc715 100644 --- a/model.py +++ b/model.py @@ -678,7 +678,7 @@ def from_pretrained(cls, config, model_type): sd[key].copy_(sd_hf[key].t()) elif key.endswith('attn.c_attn.weight') or key.endswith('attn.c_attn.bias'): # split into c_attn_q/k/v - q, k, v = sd_hf[key].split(config.n_embd, dim=-1) + q, k, v = sd_hf[key].t().split(config.n_embd, dim=0) q_key_str = key.replace("c_attn", "c_attn_q") k_key_str = key.replace("c_attn", "c_attn_k") v_key_str = key.replace("c_attn", "c_attn_v") diff --git a/train.py b/train.py index b3bdf21468..6941ca2286 100644 --- a/train.py +++ b/train.py @@ -495,23 +495,20 @@ def setup(self): state_dict[k[len('_orig_mod.'):]] = state_dict.pop(k) self.model.load_state_dict(state_dict) self.best_val_loss = checkpoint['best_val_loss'] - - elif self.args.init_from.startswith('gpt2'): - assert self.args.init_from in model_variation_dictionary + elif self.args.init_from.startswith('gpt2'): - # FIXME: delete the override args - # override_args = dict(dropout=self.args.dropout) + assert self.args.gpt2_type in model_variation_dictionary self.iter_num = 0 # for starting from scratch self.best_val_loss = 1e9 # really big number - gptconf = GPTConfig(**self.model_args) - variation_dict = model_variation_dictionary[self.args.init_from] + variation_dict = model_variation_dictionary[self.args.gpt2_type] # NOTE: the hierarchy of parameters goes: 1)variation_dict >> 2)cmd-line args >> 3)GPTConfig defaults for k in variation_dict: - gptconf[k] = variation_dict[k] + self.model_args[k] = variation_dict[k] + gptconf = GPTConfig(**self.model_args) self.model = GPT.from_pretrained(gptconf, model_type=self.args.gpt2_type) self.load_data() diff --git a/variations/model_variations.py b/variations/model_variations.py index d8ee83e80c..f388745089 100644 --- a/variations/model_variations.py +++ b/variations/model_variations.py @@ -13,7 +13,6 @@ 'vocab_size': 50257, 'block_size': 1024, 'bias': True, - 'window_size': 128, 'norm_variant_attn': 'layernorm', 'norm_variant_output': 'layernorm', }, @@ -24,7 +23,6 @@ 'vocab_size': 50257, 'block_size': 1024, 'bias': True, - 'window_size': 128, 'norm_variant_attn': 'layernorm', 'norm_variant_output': 'layernorm', }, @@ -35,7 +33,6 @@ 'vocab_size': 50257, 'block_size': 1024, 'bias': True, - 'window_size': 128, 'norm_variant_attn': 'layernorm', 'norm_variant_output': 'layernorm', }, @@ -46,7 +43,6 @@ 'vocab_size': 50257, 'block_size': 1024, 'bias': True, - 'window_size': 128, 'norm_variant_attn': 'layernorm', 'norm_variant_output': 'layernorm', }, From 4dc821a54d95c73516bfcc8688ce69734cc1097f Mon Sep 17 00:00:00 2001 From: Kauna <16511995+klei22@users.noreply.github.com> Date: Fri, 23 Aug 2024 22:30:45 -0700 Subject: [PATCH 29/30] Remove duplicate save file --- sample.py | 9 --------- 1 file changed, 9 deletions(-) diff --git a/sample.py b/sample.py index cc69873b13..7fd50c692c 100644 --- a/sample.py +++ b/sample.py @@ -104,15 +104,6 @@ def save_quantized_data(state_dict, out_file): with open(f"{out_file}.pkl", 'wb') as f: pickle.dump(to_save, f) -def save_data(state_dict, out_file): - to_save = OrderedDict() - for k, v in list(state_dict.items()): - to_save[k] = v.cpu().numpy() - - with open(f"{out_file}.pkl", 'wb') as f: - pickle.dump(to_save, f) - - def main(): args = parse_args() From 55864d505b162dab32a39bc265895df3c0eb0d9a Mon Sep 17 00:00:00 2001 From: Kauna <16511995+klei22@users.noreply.github.com> Date: Fri, 23 Aug 2024 22:33:58 -0700 Subject: [PATCH 30/30] Restore statistic_plots.py Seems these were commented out. Probably best to restore these for full functionality. --- statistics_util/statistic_plots.py | 51 +++++++++++++++--------------- 1 file changed, 25 insertions(+), 26 deletions(-) diff --git a/statistics_util/statistic_plots.py b/statistics_util/statistic_plots.py index f9dfbf77de..f02132f9da 100644 --- a/statistics_util/statistic_plots.py +++ b/statistics_util/statistic_plots.py @@ -193,32 +193,31 @@ def create_statistics(self, graph_y_labels): flattened = i_head.view(-1) ## Calculate statistics - # i_means.append(torch.nanmean(flattened).item()) - # i_medians.append(torch.nanmedian(flattened).item()) + i_means.append(torch.nanmean(flattened).item()) + i_medians.append(torch.nanmedian(flattened).item()) # Standard deviation, ignoring NaNs mask = ~torch.isnan(i_head) - # i_stdevs.append(torch.std(i_head[mask]).item()) - # i_sum_vals.append(torch.sum(i_head[mask]).item()) + i_stdevs.append(torch.std(i_head[mask]).item()) + i_sum_vals.append(torch.sum(i_head[mask]).item()) if self.iter_num % self.args.box_plot_interval == 0 and (self.args.box_plot_statistic == "all" or self.args.box_plot_statistic == "input"): box_plot_input_data.append(i_head[mask].detach().numpy()) # Max, temporarily replacing NaNs with -inf for calculation i_max_values.append(torch.max(torch.where(torch.isnan(i_head), torch.tensor(float('-inf')), i_head)).item()) - # i_min_values.append(torch.min(torch.where(torch.isnan(i_head), torch.tensor(float('inf')), i_head)).item()) + i_min_values.append(torch.min(torch.where(torch.isnan(i_head), torch.tensor(float('inf')), i_head)).item()) # Denominator computation for i_head - # exp_flattened = torch.exp(i_head[mask]) - # sum = torch.sum(exp_flattened) - # denominator.append(sum.item()) - - # # Append statistic to the input list of each head in each layer - # self.stats['mean'][layer][i].append(torch.nanmean(flattened).item()) - # self.stats['median'][layer][i].append(torch.nanmedian(flattened).item()) - # self.stats['stdev'][layer][i].append(torch.std(i_head[mask]).item()) + exp_flattened = torch.exp(i_head[mask]) + sum = torch.sum(exp_flattened) + denominator.append(sum.item()) + + ## Append statistic to the input list of each head in each layer + self.stats['mean'][layer][i].append(torch.nanmean(flattened).item()) + self.stats['median'][layer][i].append(torch.nanmedian(flattened).item()) + self.stats['stdev'][layer][i].append(torch.std(i_head[mask]).item()) self.stats['max'][layer][i].append(torch.max(torch.where(torch.isnan(i_head), torch.tensor(float('-inf')), i_head)).item()) - # self.stats['min'][layer][i].append(torch.min(torch.where(torch.isnan(i_head), torch.tensor(float('inf')), i_head)).item()) - + self.stats['min'][layer][i].append(torch.min(torch.where(torch.isnan(i_head), torch.tensor(float('inf')), i_head)).item()) outputs_location = f"transformer.h[{layer}].attn.softmax_layer_attn.outputs" @@ -233,26 +232,26 @@ def create_statistics(self, graph_y_labels): # Step 4: Calculate statistics ## Calculate statistics - # o_means.append(torch.nanmean(flattened).item()) - # o_medians.append(torch.nanmedian(flattened).item()) + o_means.append(torch.nanmean(flattened).item()) + o_medians.append(torch.nanmedian(flattened).item()) # Standard deviation, ignoring NaNs mask = ~torch.isnan(o_head) - # o_stdevs.append(torch.std(o_head[mask]).item()) - # o_sum_vals.append(torch.sum(o_head[mask]).item()) + o_stdevs.append(torch.std(o_head[mask]).item()) + o_sum_vals.append(torch.sum(o_head[mask]).item()) if self.iter_num % self.args.box_plot_interval == 0 and (self.args.box_plot_statistic == "all" or self.args.box_plot_statistic == "output"): box_plot_output_data.append(o_head[mask].detach().numpy()) # Max, temporarily replacing NaNs with -inf for calculation - # o_max_values.append(torch.max(torch.where(torch.isnan(o_head), torch.tensor(float('-inf')), o_head)).item()) - # o_min_values.append(torch.min(torch.where(torch.isnan(o_head), torch.tensor(float('inf')), o_head)).item()) + o_max_values.append(torch.max(torch.where(torch.isnan(o_head), torch.tensor(float('-inf')), o_head)).item()) + o_min_values.append(torch.min(torch.where(torch.isnan(o_head), torch.tensor(float('inf')), o_head)).item()) # Append statistic to the output list of each head in each layer - # self.stats['o_mean'][layer][i].append(torch.nanmean(flattened).item()) - # self.stats['o_median'][layer][i].append(torch.nanmedian(flattened).item()) - # self.stats['o_stdev'][layer][i].append(torch.std(o_head[mask]).item()) - # self.stats['o_max'][layer][i].append(torch.max(torch.where(torch.isnan(o_head), torch.tensor(float('-inf')), o_head)).item()) - # self.stats['o_min'][layer][i].append(torch.min(torch.where(torch.isnan(o_head), torch.tensor(float('inf')), o_head)).item()) + self.stats['o_mean'][layer][i].append(torch.nanmean(flattened).item()) + self.stats['o_median'][layer][i].append(torch.nanmedian(flattened).item()) + self.stats['o_stdev'][layer][i].append(torch.std(o_head[mask]).item()) + self.stats['o_max'][layer][i].append(torch.max(torch.where(torch.isnan(o_head), torch.tensor(float('-inf')), o_head)).item()) + self.stats['o_min'][layer][i].append(torch.min(torch.where(torch.isnan(o_head), torch.tensor(float('inf')), o_head)).item()) #BETA GAMMA if self.args.softmax_variant_attn == 'consmax':